Johns Hopkins University
Public University • US
Showing 321 courses from Johns Hopkins University
A Public Health Approach to Hearing Loss and Aging
Johns Hopkins University (via Coursera)
At the Johns Hopkins Cochlear Center for Hearing and Public Health, we are dedicated to training up the next generation of clinicians and researchers to study the impact that hearing loss in older adults has on public health and to develop and implement public health strategies and solutions for hearing loss. The Center is proud to present this course which introduces a public health approach to aging and hearing population-based research, reviews the state of public health policy and ongoing interventions, and discusses the current epidemiologic research linking hearing loss to cognitive and healthcare outcomes in older adults, including dementia. Learners will be hearing from Johns Hopkins faculty and researchers who will speak on their expertise in areas of otolaryngology, audiology, epidemiology, gerontology, and public health policy/economics.
Introduction to DevSecOps
Johns Hopkins University (via Coursera)
DevSecOps has gained considerable momentum in recent years. It integrates software development (Dev), information security (Sec), and IT operations (Ops) so that businesses increase the value delivered by software. This course provides an overview of DevSecOps; introduces essential practices, such as continuous integration / continuous deployment (CI/CD), that shorten the cycle from implementing a feature to its availability to users; and describes how to start a DevSecOps transformation. This course is for anyone who develops or manages information technology (IT) systems and wants to break down barriers between teams (development, information security, and operations), to shorten the time to market of new IT capabilities and gain a competitive advantage in the industry, and to increase the dependability and security of IT services.
Introduction to the Tidyverse
Johns Hopkins University (via Coursera)
This course introduces a powerful set of data science tools known as the Tidyverse. The Tidyverse has revolutionized the way in which data scientists do almost every aspect of their job. We will cover the simple idea of "tidy data" and how this idea serves to organize data for analysis and modeling. We will also cover how non-tidy can be transformed to tidy data, the data science project life cycle, and the ecosystem of Tidyverse R packages that can be used to execute a data science project. If you are new to data science, the Tidyverse ecosystem of R packages is an excellent way to learn the different aspects of the data science pipeline, from importing the data, tidying the data into a format that is easy to work with, exploring and visualizing the data, and fitting machine learning models. If you are already experienced in data science, the Tidyverse provides a power system for streamlining your workflow in a coherent manner that can easily connect with other data science tools. In this course it is important that you be familiar with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
Gestión del análisis de datos
Johns Hopkins University (via Coursera)
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD
Honors Algebra 2: Series, Trigonometry, and Probability
Johns Hopkins University (via Coursera)
Honors Algebra 2: Sequences, Series, Trigonometry, & Probability is the fourth part of a four-part specialization, an engaging, application-focused course that explores how algebra connects to patterns, prediction, and data-driven decision-making. Designed for high school students, college-prep learners, and adult learners refreshing their skills, this course is aligned with the Common Core State Standards and is ideal for independent study through Coursera. You’ll begin by analyzing sequences and series, uncovering the structure of arithmetic and geometric patterns and using them to model real-world phenomena like interest growth and population trends. The course then transitions to trigonometry, starting with the fundamental relationships in right triangles. You will master the core trigonometric ratios of sine, cosine, and tangent and their reciprocals. Building on this foundation, you’ll explore key trigonometric identities and use them to simplify complex expressions and solve equations. This understanding culminates in exploring trigonometric functions on the unit circle, revealing them as powerful tools for describing periodic behavior—essential in science, engineering, and even music and design. From there, you’ll shift into introductory probability and statistics, learning how to quantify uncertainty, interpret two-way tables, and distinguish between theoretical and experimental results. In the data analysis portion, you’ll evaluate datasets, calculate measures of central tendency and variability, and draw informed conclusions—skills essential in today’s data-driven world. Whether you're interested in STEM, business, or simply making sense of the information around you, this course provides a deep yet accessible look at how algebra helps you describe, analyze, and predict the world around you. With interactive learning and a clear, concept-driven approach, you'll build a strong foundation for future math courses and practical applications alike.
Design and Interpretation of Clinical Trials
Johns Hopkins University (via Coursera)
Clinical trials are experiments designed to evaluate new interventions to prevent or treat disease in humans. The interventions evaluated can be drugs, devices (e.g., hearing aid), surgeries, behavioral interventions (e.g., smoking cessation program), community health programs (e.g. cancer screening programs) or health delivery systems (e.g., special care units for hospital admissions). We consider clinical trials experiments because the investigators rather than the patients or their doctors select the treatment the patients receive. Results from randomized clinical trials are usually considered the highest level of evidence for determining whether a treatment is effective because trials incorporates features to ensure that evaluation of the benefits and risks of treatments are objective and unbiased. The FDA requires that drugs or biologics (e.g., vaccines) are shown to be effective in clinical trials before they can be sold in the US. The course will explain the basic principles for design of randomized clinical trials and how they should be reported. In the first part of the course, students will be introduced to terminology used in clinical trials and the several common designs used for clinical trials, such as parallel and cross-over designs. We will also explain some of the mechanics of clinical trials, like randomization and blinding of treatment. In the second half of the course, we will explain how clinical trials are analyzed and interpreted. Finally, we will review the essential ethical consideration involved in conducting experiments on people.
PrEParing: PrEP for Providers and Patients
Johns Hopkins University (via Coursera)
Pre-Exposure Prophylaxis (PrEP) using the antiretroviral medication emtricitibine/tenofovir approved in countries around the world is a highly effective means of reducing transmission of HIV through sexual encounters and needle sharing. This Johns Hopkins University course PrEPares you with essential information, concepts and practical advice regarding PrEP from leaders in the field. A first of its kind learning opportunity, both providers and patients learn from the same experts through content that meets the needs of both audiences, while facilitating the opportunity for a shared community space. Lessons for healthcare workers provide background on foundational and cutting-edge research and PrEP guidelines, how to initiate a PrEP program, clinical management and providing culturally sensitive sexual health and primary care to diverse communities. Lessons for PrEP enthusiasts, PrEP users or the PrEP curious provide information regarding who can benefit from PrEP, how to access services, what to expect and how to stick with your PrEP program long-term. OBJECTIVES: At the conclusion of the session, the participant will be able to: 1. Describe the differences between foundational PrEP studies and demonstration projects 2. Describe the basic pharmacodynamics of tenofovir/emtricitibine including mechanism of infection prevention and time to protective concentration in mucosal tissues 3. List recommendations from PrEP for Prevention of HIV Infection in the United States clinical practice guidelines, USPHS and CDC, including initial and ongoing screening and testing 4. Describe the need for PrEP as an HIV prevention tool for priority in often stigmatized populations 5. Indicate the components for integrating PrEP services into clinical practice 6. Outline guidelines for screening and treatment of sexually transmitted infections 7. Describe how to take a thorough sexual history and to engage with clients around sex in an affirming and non- ...
Introduction to the Biology of Cancer
Johns Hopkins University (via Coursera)
Over 500,000 people in the United States and over 8 million people worldwide are dying every year from cancer. As people live longer, the incidence of cancer is rising worldwide and the disease is expected to strike over 20 million people annually by 2030. This open course is designed for people who would like to develop an understanding of cancer and how it is prevented, diagnosed, and treated. The course introduces the molecular biology of cancer (oncogenes and tumor suppressor genes) as well as the biologic hallmarks of cancer. The course also describes the risk factors for the major cancers worldwide, including lung cancer, breast cancer, colon cancer, prostate cancer, liver cancer, and stomach cancer. We explain how cancer is staged, the major ways cancer is found by imaging, and how the major cancers are treated. In addition to the core materials, this course includes two Honors lessons devoted to cancers of the liver and prostate. Upon successful completion of this course, you will be able to: Identify the major types of cancer worldwide. (Lecture 1) Describe how genes contribute to the risk and growth of cancer. (Lecture 2) List and describe the ten cellular hallmarks of cancer. (Lecture 3) Define metastasis, and identify the major steps in the metastatic process. (Lecture 4) Describe the role of imaging in the screening, diagnosis, staging, and treatments of cancer. (Lecture 5) Explain how cancer is treated. (Lecture 6) We hope that this course gives you a basic understanding of cancer biology and treatment. The course is not designed for patients seeking treatment guidance – but it can help you understand how cancer develops and provides a framework for understanding cancer diagnosis and treatment.
ERPO: A Civil Approach to Gun Violence Prevention Teach-Out
Johns Hopkins University (via Coursera)
Evidence shows that 1 life is saved for every 10-20 Extreme Risk Protection Orders (ERPOs) issued. ERPOs are legally issued civil orders that allow people on the front lines to ask a court to prevent a person at imminent risk of harm to themselves or others from purchasing or possessing firearms during a critical period of risk. As of October 2020, ERPO laws have been enacted by nineteen states and the District of Columbia and while some aspects differ by state, the general process for applying and issuing ERPOs are quite similar. In this Teach-Out, a team of public health, policy, and medical experts will each provide a unique perspective to: 1. Define what ERPO laws are and how they work; 2. Share scientific evidence supporting ERPO laws as a public health approach to preventing gun violence and suicide; 3. Describe specific considerations for those eligible to petition for (aka those who ask the court to issue) ERPOs on behalf of an individual—including law enforcement, health professionals, and personal family members; 4. Discuss the legal process for passing, implementing, and enforcing ERPO laws; 5. Recommend specific call to action activities for learners designed to encourage policy makers to implement ERPO laws in states without them and strengthen existing ERPO laws in states that already have them; and 6. Provide opportunities for learners to engage in civil discourse and collective action to increase awareness about ERPOs and empower learners to advocate for ERPO laws to reduce and prevent gun violence in our communities.
The People, Power, and Pride of Public Health
Johns Hopkins University (via Coursera)
The People, Power, and Pride of Public Health provides an engaging overview of the incredible accomplishments and promise of the public health field. The first module includes interviews with legendary public health figures whose work led to millions of lives saved with vaccines, air bags and car seats, and the federal Women Infants and Children (WIC) nutrition program. The second module brings key public health tools to life -- including use of data, communications, and policy - through discussions with experienced professionals who have used these tools to save lives. The third module includes a "Carpool Karaoke"-style trip through Baltimore County, Maryland with NACCHO President Dr. Umair Shah to see and hear real public health workers talking about how they serve their communities. Learners will come away from this course with a deeper understanding of the public health field and a greater enthusiasm for their own work in public health. Preview the course on YouTube: goo.gl/RXKbUr
Setting the Stage for Success: An Eye on Safety Culture and Teamwork (Patient Safety II)
Johns Hopkins University (via Coursera)
Safety culture is a facet of organizational culture that captures attitudes, beliefs, perceptions, and values about safety. A culture of safety is essential in high reliability organizations and is a critical mechanism for the delivery of safe and high-quality care. It requires a strong commitment from leadership and staff. In this course, a safe culture is promoted through the use of identifying and reporting patient safety hazards, accountability and transparency, involvement with patients and families, and effective teamwork.
Managing Data Analysis
Johns Hopkins University (via Coursera)
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD
Importing Data in the Tidyverse
Johns Hopkins University (via Coursera)
Getting data into your statistical analysis system can be one of the most challenging parts of any data science project. Data must be imported and harmonized into a coherent format before any insights can be obtained. You will learn how to get data into R from commonly used formats and harmonizing different kinds of datasets from different sources. If you work in an organization where different departments collect data using different systems and different storage formats, then this course will provide essential tools for bringing those datasets together and making sense of the wealth of information in your organization. This course introduces the Tidyverse tools for importing data into R so that it can be prepared for analysis, visualization, and modeling. Common data formats are introduced, including delimited files, spreadsheets and relational databases, and techniques for obtaining data from the web are demonstrated, such as web scraping and web APIs. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
Transportation, Sustainable Buildings, Green Construction
Johns Hopkins University (via Coursera)
This course will include the evaluation of best practices in parking strategies for sustainable cities. Some of the examples include downtown parking policy, understanding the high cost of free parking, the development of parking sheds, managing neighborhood parking, hiding parking lots and other parking strategies and practices. There will also be a case study of the Victoria Canada parking management approach that investigates problems with current work on parking planning and discusses the cost of parking facilities and potential savings from improved management. There will also be an introduction to the use of form-based codes for application to sustainable cities. A variety of building forms such as mid-rise and high-rise buildings, apartment houses, live/work buildings, single-family homes, and row houses will be assessed. A particular emphasis will be on vacant property strategies for equitable and healthy communities. Vacant property strategies for reclamation will be evaluated with an analysis of the revitalization cycle. Green building construction principles will be evaluated in consideration for natural light and ventilation, solar orientation, use of sustainable building materials, energy efficient design and on-site energy generation as well as other considerations. Building architectural design will leverage climate, construction materials, and the culture and history of the area. Architecture choices should have a consistent appearance within the community and provide residential privacy. Other considerations include protection and preservation of historic buildings, use of universal design concepts, careful placement of civic buildings and the appropriate use of subsidized housing. By the end of this course, you will be able to: 1. Survey and evaluate a variety of parking policies that support sustainable cities and environmental quality. 2. Compare different parking options such as parking sheds, neighborhood parking, parking lot access, permeabl...
HDFS Architecture and Programming
Johns Hopkins University (via Coursera)
The course “HDFS Architecture and Programming” offers a comprehensive understanding of the Hadoop Distributed File System (HDFS) architecture, components, and advanced programming techniques. You will gain practical experience in setting up and configuring Hadoop for Java development, while mastering key concepts such as file and directory CRUD operations, data compression, and serialization. By the end of the course, you will be proficient in using HDFS to handle large-scale data processing, enabling you to build scalable, high-availability solutions. What sets this course apart is its hands-on approach, where you will work directly with HDFS, writing client programs and applying advanced techniques such as using Sequence and Map Files for specialized data storage. Whether you're new to Hadoop or looking to refine your existing skills, this course equips you with the tools and knowledge to become proficient in HDFS programming, making you a valuable asset in the field of Big Data.
Introduction to Neurohacking In R
Johns Hopkins University (via Coursera)
Neurohacking describes how to use the R programming language (https://cran.r-project.org/) and its associated package to perform manipulation, processing, and analysis of neuroimaging data. We focus on publicly-available structural magnetic resonance imaging (MRI). We discuss concepts such as inhomogeneity correction, image registration, and image visualization. By the end of this course, you will be able to: Read/write images of the brain in the NIfTI (Neuroimaging Informatics Technology Initiative) format Visualize and explore these images Perform inhomogeneity correction, brain extraction, and image registration (within a subject and to a template).
Clinical Trials Data Management and Quality Assurance
Johns Hopkins University (via Coursera)
In this course, you’ll learn to collect and care for the data gathered during your trial and how to prevent mistakes and errors through quality assurance practices. Clinical trials generate an enormous amount of data, so you and your team must plan carefully by choosing the right collection instruments, systems, and measures to protect the integrity of your trial data. You’ll learn how to assemble, clean, and de-identify your datasets. Finally, you’ll learn to find and correct deficiencies through performance monitoring, manage treatment interventions, and implement quality assurance protocols.
Diagnosing Health Behaviors for Global Health Programs
Johns Hopkins University (via Coursera)
Health behavior lies at the core of any successful public health intervention. While we will examine the behavior of individual in depth in this course, we also recognize by way of the Ecological Model that individual behavior is encouraged or constrained by the behavior of families, social groups, communities, organizations and policy makers. We recognize that behavior change is not a simplistic process but requires an understanding of dimensions like frequency, complexity and cultural congruity. Such behavioral analysis is strengthened through the use of a toolkit of theoretical models and practical frameworks. While many of such models and frameworks exist, in this course we will review the Health Belief Model, Social Learning Theory, Theory of Reasoned Action, the Trans-Theoretical Model and the PRECEDE Framework. After building your behavioral analysis toolkit with these examples, you will see that actual behavior change program planning uses a combination of ideas and variables from different models, theories and frameworks. Ultimately we aim to encourage course participants to apply the idea that successful programs are theory based as they go about involving people in improving their health.
Fundamentals for Implementing a Hypertension Program
Johns Hopkins University (via Coursera)
This course provides the fundamental knowledge necessary for program managers and implementors in a hypertension control program, especially in resource-limited settings. The course is interactive and includes useful tips relevant to different settings. The course should be also relevant to physicians, nurses, pharmacists, community health workers, and others who are interested in learning about hypertension diagnosis and management.
Advanced Malware and Network Anomaly Detection
Johns Hopkins University (via Coursera)
The course "Advanced Malware and Network Anomaly Detection" equips learners with essential skills to combat advanced cybersecurity threats using artificial intelligence. This course takes a hands-on approach, guiding students through the intricacies of malware detection and network anomaly identification. In the first two modules, you will gain foundational knowledge about various types of malware and advanced detection techniques, including supervised and unsupervised learning methods. The subsequent modules shift focus to network security, where you’ll explore anomaly detection algorithms and their application using real-world botnet data. What sets this course apart is its emphasis on practical, project-based learning. By applying your knowledge through hands-on implementations and collaborative presentations, you will develop a robust skill set that is highly relevant in today’s cybersecurity landscape. Completing this course will prepare you to effectively identify and mitigate threats, making you a valuable asset in any cybersecurity role. With the rapid evolution of cyber threats, this course ensures you stay ahead by leveraging the power of AI for robust cybersecurity measures.
Calculus through Data & Modeling: Differentiation Rules
Johns Hopkins University (via Coursera)
Calculus through Data & Modeling: Differentiation Rules continues the study of differentiable calculus by developing new rules for finding derivatives without having to use the limit definition directly. These differentiation rules will enable the calculation of rates of change with relative ease the derivatives of polynomials, rational functions, algebraic functions, exponential and logarithmic functions, and trigonometric and inverse trigonometric functions. Once these rules are developed, they are then applied to solve problems involving rates of change and the approximation of functions.
Ongoing U.S. Settler Colonialism & Native Peoples Teach-Out
Johns Hopkins University (via Coursera)
Increasingly, terms such as “colonialism,” “decolonization,” and “social structures,” appear in media, conversations, and educational spaces, often without nuanced explanations of these concepts and how they relate to current U.S. society and the individuals in it. To provide a space to think, learn, and feel about these concepts as realities connected to everyone, this course offers many entry points to deepen understandings about the U.S. as a current settler colonial nation, to engage with contemporary Indigenous Peoples/Native Nations, and to recognize how participants’ own lives, interests, and professional domains intersect with settler colonialism. This course highlights perspectives from Indigenous Peoples/Native Nations while focusing on examples of ongoing settler colonialism as it shows up in education, law, food systems, media, land, gender, race/ethnicity, and health/medicine, among others. Additionally, this course offers a framework consisting of four cornerstones that reveal how ongoing settler colonialism in the United States: 1) attempts to eliminate Indigenous Peoples, 2) imposes ideas of property, 3) produces anti-relationality, and 4) naturalizes the assumption of limited options. Through the framework + Native perspectives and knowledge, participants will better understand ongoing settler colonialism while (re)imagining anti-colonial processes in the U.S. as a way to co-create thriving futures for everyone. This Teach-Out does not issue certificates of completion.
Algebra: Elementary to Advanced - Equations & Inequalities
Johns Hopkins University (via Coursera)
This course is intended for students looking to create a solid algebraic foundation of fundamental mathematical concepts from which to take more advanced courses that use concepts from precalculus, calculus, probability, and statistics. This course will help solidify your computational methods, review algebraic formulas and properties, and apply these concepts model real world situations. This course is for any student who will use algebraic skills in future mathematics courses. Topics include: the real numbers, equalities, inequalities, polynomials, rational expressions and equations, graphs, relations and functions, radicals and exponents, and quadratic equations.
Securing AI and Advanced Topics
Johns Hopkins University (via Coursera)
In the course "Securing AI and Advanced Topics", learners will delve into the cutting-edge intersection of AI and cybersecurity, focusing on how advanced techniques can secure AI systems against emerging threats. Through a structured approach, you will explore practical applications, including fraud prevention using cloud AI solutions and the intricacies of Generative Adversarial Networks (GANs). Each module builds upon the previous one, enabling a comprehensive understanding of both offensive and defensive strategies in cybersecurity. What sets this course apart is its hands-on experience with real-world implementations, allowing you to design effective solutions for detecting and mitigating fraud, as well as understanding adversarial attacks. By evaluating AI models and learning reinforcement learning principles, you will gain insights into enhancing cybersecurity measures. Completing this course will equip you with the skills necessary to address complex challenges in the evolving landscape of AI and cybersecurity, making you a valuable asset in any organization. Whether you are seeking to deepen your expertise or enter this critical field, this course provides the tools and knowledge you need to excel.
Healthcare Delivery for Medical Practice Managers
Johns Hopkins University (via Coursera)
Welcome to the Healthcare Delivery in Ambulatory Healthcare Management course! This course provides you with a comprehensive understanding of the various aspects of healthcare delivery within the dynamic field of ambulatory healthcare management. This course is designed for learners with little to no experience in this field, and welcomes all those interested in learning more about medical practice management. Throughout this course, you will explore key topics that are crucial for effectively managing healthcare delivery. You’ll cover the locations of care, including hospitals, ambulatory clinics, virtual care, nursing homes, and other healthcare settings. Additionally, you will delve into practice management, population health, care management, case management, and the distinction between primary and specialty care. By the end of this course, you will have the knowledge and skills necessary to navigate the complex landscape of healthcare delivery in ambulatory settings. Join us on this learning journey to enhance your understanding of ambulatory healthcare management and its impact on patient care, population health, and overall healthcare outcomes.
Registro civil y estadísticas para la salud de la población
Johns Hopkins University (via Coursera)
En este curso, aprenderá sobre el papel y la importancia de los sistemas de registro civil y estadísticas vitales (CRVS) que se utilizan para hacer un seguimiento de los nacimientos, las defunciones y los acontecimientos vitales. Expertos de todo el mundo explicarán qué son los sistemas CRVS, cómo se utilizan, las bases jurídicas para registrar acontecimientos vitales y los retos que plantean los sistemas CRVS. Aprenderá cómo se pueden utilizar los datos de la CRVS para fundamentar la toma de decisiones a nivel poblacional, incluso en torno a métodos específicos como la certificación médica de la causa de la muerte (MCCOD) y la autopsia verbal, y la aplicación de una perspectiva de género y equidad a los sistemas de datos para garantizar que responden a las necesidades de las poblaciones. Nuestros objetivos generales para el curso son apoyar la recopilación de datos sobre defunciones y nacimientos a nivel nacional, mejorar el uso de los datos para fundamentar las prioridades políticas, seguir las tendencias y planificar las intervenciones, y mejorar el seguimiento de los principales factores de riesgo de muerte prematura, especialmente por enfermedades no transmisibles. El curso es el resultado de la colaboración entre varios colaboradores, como Vital Strategies, los Centros para el Control y la Prevención de Enfermedades (CDC), la Fundación CDC, la Escuela Bloomberg de Salud Pública de Johns Hopkins, Global Health Advocacy Incubator y la Universidad de New South Wales, en Sídney. Este curso ha sido financiado por Bloomberg Philanthropies, con cofinanciación del gobierno australiano y la Fundación Bill y Melinda Gates.
Advanced Probability and Statistical Methods
Johns Hopkins University (via Coursera)
The course "Advanced Probability and Statistical Methods" provides a deep dive into advanced probability and statistical methods, essential for mastering data analysis in computer science. Covering joint distributions, expectation, statistical testing, and Markov chains, you'll explore key concepts and techniques that underpin modern data-driven decision-making. By engaging with real-world problems, you’ll learn to apply these methods effectively, gaining insights into the relationships between random variables and their applications in diverse fields. Completing this course equips you with the skills to analyze complex data sets and make informed predictions, enhancing your proficiency in statistical reasoning and inference. Unique to this course is its blend of theoretical foundations and practical applications, ensuring that you can not only understand the principles but also implement them using tools like R. Whether you're pursuing a career in data science, machine learning, or any data-centric discipline, this course will empower you to tackle challenging statistical problems and drive meaningful insights from data.
Kids with Cancer Still Need School: The Providers Role
Johns Hopkins University (via Coursera)
This course will help you understand and address the challenges parents and families face regarding schooling after a cancer diagnosis. When a child is diagnosed with cancer, families can be overwhelmed learning about and managing this new and frightening reality. As they adjust to the new normal of ongoing treatment, school may be the farthest thing from their mind. However, as their oncology health care provider, you have a critical role in starting conversations about schooling. Even for your youngest patients, it is important to talk with parents and caregivers about schooling issues early and often. This course gives you easy access to Roadmaps that help parents learn and take action so their child can access schooling supports for which they are eligible. The modules will give you the skills and resources needed in order to support parents to use the information provided in the parent-informed Roadmaps. We hope that completing this course will help you make small changes in your everyday practice that can make a big change in how well parents can advocate for their child's schooling needs.
Advanced Cybersecurity Techniques
Johns Hopkins University (via Coursera)
The course "Advanced Cybersecurity Techniques" delves into advanced cybersecurity methodologies, equipping learners with practical skills to tackle complex security challenges. Covering critical topics such as mobile device vulnerabilities, DNS attacks, network exploitation, web application security, and Wi-Fi exploitation, participants will gain hands-on experience through practical labs and real-world scenarios. Learners will benefit from a comprehensive understanding of the latest attack vectors and mitigation strategies, enhancing their ability to safeguard digital environments. The course emphasizes ethical hacking methodologies, utilizing the MITRE ATT&CK Enterprise Framework to provide a structured approach to understanding cyber exploits.
Advanced Project Management and Leadership
Johns Hopkins University (via Coursera)
The course "Advanced Project Management and Leadership" equips learners with essential skills to excel in dynamic project environments. By exploring critical areas such as project audits, leadership styles, and negotiation strategies, you’ll gain the tools necessary for effective multi-project management. This course stands out by integrating agile methodologies with innovative practices, enabling you to navigate complex project landscapes confidently. Through hands-on case studies and real-world applications, you’ll learn how to conduct thorough project progress reviews, manage project closeouts effectively, and develop a product lifecycle roadmap. The unique focus on the Project Manager's role in research and development (R&D) projects further enhances your understanding of innovation and marketing within project contexts. Completing this course not only prepares you to tackle challenges in project management but also empowers you to lead teams towards successful outcomes. Whether you are looking to refine your leadership abilities or enhance your negotiation skills, this course provides a comprehensive learning experience that will propel your career forward in project management.
Firearm Purchaser Licensing Teach-Out: The Background Check Policy Not Enough People Are Talking About
Johns Hopkins University (via Coursera)
The best available evidence shows that comprehensive background checks by themselves are insufficient at reducing gun violence, but strong bodies of research demonstrate that states with comprehensive background checks coupled with firearm purchaser licensing, or permit-to-purchase laws, have significantly fewer firearm-involved homicides, fatal mass shootings, and suicides. Additionally, public opinion polling research consistently finds that 75% or more of U.S. adults support firearm purchaser licensing laws and 60% or more of gun owners support these policies. This Teach-Out event includes a general overview of firearm licensing policies and evidence of their effectiveness, as well as materials on the social context of licensing, including public opinion polling and differences across geographic areas and groups. Over the next four weeks, you are invited to join us as we explore this topic together through brief lessons, weekly discussions, and call-to-action activities for you to undertake in your own community. We would also like to acknowledge our funders, the Johns Hopkins Center for Teaching and Learning and the David and Lucile Packard Foundation. Without their generous contributions, this Teach-Out would not be possible and we are so very grateful for their support.
Reliability, Cloud Computing and Machine Learning
Johns Hopkins University (via Coursera)
The course "Reliability, Cloud Computing and Machine Learning" explores advanced distributed database concepts, focusing on transaction management, reliability protocols, and data warehousing, while also diving deeper into cloud computing and machine learning. You will develop a solid understanding of transaction principles, concurrency control methods, and how to ensure database consistency during failures using ACID properties and protocols like ARIES. The course uniquely integrates Hadoop, MapReduce, and Accumulo, offering hands-on experience with large-scale data processing and machine learning applications such as collaborative filtering, clustering, and classification. By mastering these advanced topics, you'll gain the skills necessary to work with cutting-edge technologies used in cloud-based data processing and scalable machine learning analysis. With practical applications in both reliability management and machine learning, this course prepares you to tackle complex data management challenges, making you well-equipped for careers in cloud computing, distributed systems, and data science.
Advanced Methods in Machine Learning Applications
Johns Hopkins University (via Coursera)
The course "Advanced Methods in Machine Learning Applications" delves into sophisticated machine learning techniques, offering learners an in-depth understanding of ensemble learning, regression analysis, unsupervised learning, and reinforcement learning. The course emphasizes practical application, teaching students how to apply advanced techniques to solve complex problems and optimize model performance. Learners will explore methods like bagging, boosting, and stacking, as well as advanced regression approaches and clustering algorithms. What sets this course apart is its focus on real-world challenges, providing hands-on experience with advanced machine learning tools and techniques. From exploring reinforcement learning for decision-making to applying apriori analysis for association rule mining, this course equips learners with the skills to handle increasingly complex datasets and tasks. By the end of the course, learners will be able to implement, optimize, and evaluate sophisticated machine learning models, making them well-prepared to address advanced challenges in both research and industry.
Training and Learning Programs for Volunteer Community Health Workers
Johns Hopkins University (via Coursera)
Volunteer community health workers (CHWs) are a major strategy for increasing access to and coverage of basic health interventions. Our village health worker training course reviews the process of training and continuing education of CHWs as an important component of involving communities in their own health service delivery. Participants will be guided through the steps of planning training and continuing education activities for village volunteers. The course draws on real-life examples from community-directed onchocerciasis control, village health worker programs, community case management efforts, peer educators programs and patent medicine vendor training programs, to name a few.
Managing AI Projects: From Strategy to Delivery
Johns Hopkins University (via Coursera)
The course "AI Project Management" equips learners with the tools and strategies to successfully design, manage, and scale AI projects in real-world environments. Covering the entire lifecycle of AI project management, from resource planning to deployment, the course emphasizes effective practices for optimizing performance, minimizing risks, and addressing ethical challenges. Learners will explore key management principles, such as balancing scalability with budget constraints, mitigating biases in AI systems, and fostering team collaboration. What makes this course unique is its focus on both the technical and human aspects of AI project management. By analyzing the labor dynamics of AI adoption and exploring strategies to create cognitively diverse teams, participants gain insights into building inclusive, sustainable AI solutions. Case studies and practical examples ensure that learners leave with actionable knowledge to lead AI initiatives confidently. Whether scaling existing projects or implementing new ones, this course provides the expertise to succeed in today's AI-driven landscape.
Building R Packages
Johns Hopkins University (via Coursera)
Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres to community-based standards and provides a good user experience. This course covers the primary means by which R software is organized and distributed to others. We cover R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub. Learners will produce R packages that satisfy the criteria for submission to CRAN.
Introduction to Neural Networks
Johns Hopkins University (via Coursera)
The course "Introduction to Neural Networks" provides a comprehensive introduction to the foundational concepts of neural networks, equipping learners with essential skills in deep learning and machine learning. Dive into the mathematics that drive neural network algorithms and explore the optimization techniques that enhance their performance. Gain hands-on experience training machine learning models using gradient descent and evaluate their effectiveness in practical scenarios. You’ll also delve into the architecture of feedforward neural networks and the innovative techniques used to prevent overfitting, such as dropout and regularization. The course uniquely emphasizes Convolutional Neural Networks (CNNs), highlighting their applications in fields like computer vision and image processing. Real-world examples and research insights will help you stay current with advancements in neural networks while preparing you to propose innovative solutions for emerging challenges. This course offers the tools and knowledge to advance your expertise in algorithms and machine learning methodologies.
Advanced Linear Models for Data Science 1: Least Squares
Johns Hopkins University (via Coursera)
Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: A basic understanding of linear algebra and multivariate calculus. A basic understanding of statistics and regression models. At least a little familiarity with proof based mathematics. Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.
Design and Conduct of Clinical Trials
Johns Hopkins University (via Coursera)
In this course, you’ll learn how to design and carry out clinical trials. Each design choice has implications for the quality and validity of your results. This course provides you and your team with essential skills to evaluate options, make good design choices, and implement them within your trial. You’ll learn to control for bias, randomize participants, mask treatments and outcomes, identify errors, develop and test hypotheses, and define appropriate outcomes. Finally, a trial without participants is no trial at all, so you’ll learn the guiding principles and develop the essential skills to ethically and conscientiously recruit, obtain consent from, and retain trial participants.
The Data Scientist’s Toolbox
Johns Hopkins University (via Coursera)
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
Data and Electronic Health Records
Johns Hopkins University (via Coursera)
Welcome to the Data and EHRs in Ambulatory Healthcare Management course! In this course, you will explore the crucial role of data and electronic health records (EHRs) in the realm of ambulatory healthcare management. This course provides you with a comprehensive understanding of healthcare data basics, data integrity and management, HIPAA regulations, and the utilization of EHRs in various aspects of healthcare operations. This course is designed for those with little to no background in healthcare, and is perfect for beginners and those interested in learning more about this field. By the end of this course, you will have a comprehensive understanding of healthcare data fundamentals, data integrity and management principles, HIPAA regulations, and the utilization of EHRs in ambulatory healthcare management. Start your journey to enhancing your knowledge and skills in leveraging data and EHRs to drive improved patient care, operational efficiency, and data-driven decision-making in ambulatory healthcare settings.
One Health Investigations of Outbreaks and Spillover Events
Johns Hopkins University (via Coursera)
One Health is the concept that human, animal and environmental health are interconnected. Outbreaks of zoonoses, vector-borne diseases, or those related to contamination of the environment are best investigated using a multi-disciplinary One Health approach. Outbreaks of emerging zoonotic infectious diseases where infections in multiple species – from humans, to livestock, to wildlife – may only be well understood if a One Health approach is used. Investigations of spillover events – where a human or new animal species has been infected by a virus, bacteria, or parasite – also require a One Health approach. Beyond infectious diseases, the environment can be contaminated by heavy metals, industrial pollution and other toxins, which can pose a risk to both human and animal health. In order to protect human and animal health from these threats, One Health approaches can be integrated into outbreak investigations and spillover investigations to improve our mechanistic understanding of their proximal and distal causes leading to insights about how they can be prevented.
Algorithms for DNA Sequencing
Johns Hopkins University (via Coursera)
We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.
Data – What It Is, What We Can Do With It
Johns Hopkins University (via Coursera)
This course introduces students to data and statistics. By the end of the course, students should be able to interpret descriptive statistics, causal analyses and visualizations to draw meaningful insights. The course first introduces a framework for thinking about the various purposes of statistical analysis. We’ll talk about how analysts use data for descriptive, causal and predictive inference. We’ll then cover how to develop a research study for causal analysis, compute and interpret descriptive statistics and design effective visualizations. The course will help you to become a thoughtful and critical consumer of analytics. If you are in a field that increasingly relies on data-driven decision making, but you feel unequipped to interpret and evaluate data, this course will help you develop these fundamental tools of data literacy.
The Social and Technical Context of Health Informatics
Johns Hopkins University (via Coursera)
Improving health and healthcare institutions requires understanding of data and creation of interventions at the many levels at which health IT interact and affect the institution. These levels range from the external “world” in which the institution operates down to the specific technologies. Data scientists find that, when they aim at implementing their models in practice, it is the “socio” components that are both novel to them and mission critical to success. At the end of this course, students will be able to make a quick assessment of a health informatics problem—or a proposed solution—and to determine what is missing and what more needs to be learned. Who Is This Class For? Physicians, nurses, pharmacists, social workers, and other allied health professionals interested in expanding their understanding of digital health, big data, health information systems, and the unintended consequences of disruptive innovation in the healthcare system. The course is also aimed at those with technical, engineering, or analytics backgrounds who want to understand the nuances of those topics when it comes to healthcare.
The Creative Leader
Johns Hopkins University (via Coursera)
This aims primarily at post-baccalaureate students interested in leadership theory. The course has four modules. Module 1 covers definitions and foundations of creative leadership. Topics include, What is creative leadership? Why creative leadership? And foundations of creative leadership. Module 2 addresses the facilitative functions of the creative leader. Topics include employee creativity on an individual level; employee creativity and team complexity; and employee creativity and team diversity. Module 3 addresses the directive functions of the creative leader. Topics include directive creative leaders in politics; directive creative leaders in haute cuisine; and directive creative leaders in the arts. Module 4 addresses the integrative functions of the creative leader. Topics include integrative creative leadership and final creative products; and integrative leadership and others’ individual creative products This is one course in the Coursera specialization, Leadership: An Introduction. It examines current trends in leadership theory invoking several disciplines, including business, sociology, philosophy, history, and psychology. To complete this course successfully students should be able to analyze college-level readings and audio/visual presentations into understandable parts, including premises and conclusions; synthesize the results of the analysis into coherent and accurate summaries; and evaluate the results for accuracy and practical applicability. Upon successful completion of the course, students will be able to • Define creative leadership • Explain the facilitative functions of a creative leader • Explain the directive functions of a creative leader • Explain the integrative functions of a creative leader • Assess the value of creative leadership to contemporary organizational leadership • Apply techniques of creative leadership to organizational challenges
Living with Dementia: Impact on Individuals, Caregivers, Communities and Societies
Johns Hopkins University (via Coursera)
Health professionals and students, family caregivers, friends of and affected individuals, and others interested in learning about dementia and quality care will benefit from completing the course. Led by Drs. Nancy Hodgson and Laura Gitlin, participants will acquire foundational knowledge in the care of persons with Alzheimer’s Disease and other neurocognitive disorders.
Chatbots
Johns Hopkins University (via Coursera)
The course "Chatbots" offers a deep dive into the world of chatbots, equipping learners with the skills to design, build, and optimize conversational interfaces. You will explore the evolution of chatbot technology and understand the fundamental mechanics that drive their functionality. Through hands-on projects using Amazon Lex and AWS, you'll not only learn to create chatbots but also how to evaluate their performance using machine learning classifiers. What sets this course apart is its practical approach, allowing you to apply theoretical knowledge in real-world scenarios. Collaborating with peers, you’ll tackle challenges together, enhancing your problem-solving skills while fostering a supportive learning environment. By the end of the course, you’ll have the confidence to develop functional chatbots tailored for various applications, from customer service to personal assistants. Whether you are a novice looking to enter the tech field or an experienced professional aiming to expand your skill set, this course provides invaluable insights and practical tools to advance your career in the rapidly growing chatbot landscape. Join us to unlock the potential of conversational AI!
CUDA at Scale for the Enterprise
Johns Hopkins University (via Coursera)
This course will aid in students in learning in concepts that scale the use of GPUs and the CPUs that manage their use beyond the most common consumer-grade GPU installations. They will learn how to manage asynchronous workflows, sending and receiving events to encapsulate data transfers and control signals. Also, students will walk through application of GPUs to sorting of data and processing images, implementing their own software using these techniques and libraries. By the end of the course, you will be able to do the following: Develop software that can use multiple CPUs and GPUs Develop software that uses CUDA’s events and streams capability to create asynchronous workflows Use the CUDA computational model to to solve canonical programming challenges including data sorting and image processing To be successful in this course, you should have an understanding of parallel programming and experience programming in C/C++. This course will be extremely applicable to software developers and data scientists working in the fields of high performance computing, data processing, and machine learning.
Random Processes
Johns Hopkins University (via Coursera)
Probability and statistics provide an excellent tool for understanding, modeling and communicating uncertainty in engineering systems. In many applications there is the added challenge of considering random quantities that vary over time and/or space. Examples can be found in seismic applications, financial markets, heterogeneous materials, and image processing, among many others. This course provides an introduction into some of the ways in which random processes and random fields are measured, quantified and communicated. Through video lectures, activities, and interactive content, students will learn about correlation functions, spectral density functions, local average processes and Monte Carlo simulation. There will be an emphasis on understanding each concept, estimating these quantities from data, and using this data as the basis for generating realistic sample random processes. By the end of this course, you will be able to: Explain the meaning of the correlation function, the spectral density function, homogeneity, ergodicity. Identify parameters of a random process based on available data. Relate random process descriptors to reliability via maximum value distributions. Simulate a random process with desired correlation and/or spectral density function.
Communications and High-Speed Signals with Raspberry Pi
Johns Hopkins University (via Coursera)
Course two of this specialization is all about hardware physical layer and communication between elements of your project, how to troubleshoot high-speed signals when they don't work, and how to design your projects so they do work. We start with a review of common signal protocols available . Then, to build a deep and intuitive understanding of how circuits send and receive these signals, Module 2 explores the physics of high-frequency signals in an easy-to-follow way. Module 3 flips your thinking from the time-domain to the frequency-domain to examine the frequency components of signals and understand how unintended filtering in your circuits distorts your digital waveforms. These are "signal integrity" concepts, distilled to what you need for your Raspberry Pi projects. Now, with our knowledge of signals, Module 4 develops five rules of thumb for designing your circuits so that your high-speed signals work the first time. These five rules of thumb, combined with the experience from earlier modules, help you estimate spectral bandwidth of signals, rise time, and gain insights whether you're troubleshooting a broken design or designing something new.
Fundamentals of Project Management
Johns Hopkins University (via Coursera)
Fundamentals of Project Management offers a comprehensive introduction to the key principles and practices necessary to lead successful projects. You will learn how to navigate each phase of the project lifecycle, from initiation and planning to execution and closure. This course provides hands-on experience with essential tools like Work Breakdown Structures (WBS), scheduling, budgeting, and critical path analysis. What makes this course unique is its practical focus, exemplified through real-world scenarios like the PickPOCIT case study, where you'll apply your knowledge to simulate the role of a project manager. Whether you're new to project management or seeking to enhance your skills, this course equips you with the strategies and confidence needed to deliver projects on time and within budget. By the end, you'll be prepared to handle complex project challenges and drive successful outcomes in any industry.
Advanced Data Visualization with R
Johns Hopkins University (via Coursera)
Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today. One of the critical tools for data visualization today is the R statistical programming language. Especially in conjunction with the tidyverse software packages, R has become an extremely powerful and flexible platform for making figures, tables, and reproducible reports. However, R can be intimidating for first time users, and there are so many resources online that it can be difficult to sort through without guidance. This course is the third in the Specialization "Data Visualization and Dashboarding in R." Learners come into this course with a foundation using R to make many basic kinds of visualization, primarily with the ggplot2 package. Accordingly, this course focuses on expanding the learners' inventory of data visualization options. Drawing on additional packages to supplement ggplot2, learners will made more variants of traditional figures, as well as venture into spatial data. The course ends make interactive and animated figures. To fill that need, this course is intended for learners who have little or no experience with R but who are looking for an introduction to this tool. By the end of this course, students will be able to import data into R, manipulate that data using tools from the popular tidyverse package, and make simple reports using R Markdown. The course is designed for students with good basic computing skills, but limited if any experience with programming.
Data to Advance Population Health: Global Perspectives
Johns Hopkins University (via Coursera)
In this course, you will learn about methodologies that support the successful data use to strengthen public health programs and policies. Experts from around the world will define and explain what population-level data is, introduce the data-generation-to-use cycle, and explain other considerations for successful data use for population health. You will also learn about how health services data can be used to inform population-level decision-making, and the application of a gender and equity lens towards these data systems to ensure they are responsive to the needs of populations. Our overarching goal for this course is to support and improve the use of data to inform policy. The course is the result of a collaboration among multiple partners, including Vital Strategies, the Centers for Disease Control and Prevention (CDC) Foundation, the Johns Hopkins Bloomberg School of Public Health, and the World Health Organization. This course was funded by Bloomberg Philanthropies, with co-funding from the Australian government and Bill and Melinda Gates Foundation.
Project Execution and Control
Johns Hopkins University (via Coursera)
The course "Project Execution and Control" delves into the advanced stages of project execution and control, equipping learners with the tools needed to ensure projects stay on track and meet their objectives. From defining project objectives to managing risks and resolving conflicts, this course offers a comprehensive insight into the strategies effective Project Managers use to maintain momentum and achieve success. You’ll explore key topics like design reviews, configuration management, quality control, and Earned Value Management (EVM), gaining practical knowledge on implementing and adjusting project plans in real-time. What sets this course apart is its focus on hands-on application, enabling you to practice the skills you learn through exercises and scenarios that mirror actual project challenges. By mastering communication strategies, managing quality assurance, and collaborating with executives, you’ll be well-prepared to execute and monitor projects in diverse, high-stakes environments. Whether you’re new to project management or advancing your skills, this course offers invaluable techniques to elevate your project execution and control expertise.
Pillar #1: Drug Development - From Bench to Bedside
Johns Hopkins University (via Coursera)
Have you ever taken over the counter or prescription drug? Are you curious about how these drugs are developed, approved and brought to the market? We begin by providing an overview of drug development and approval process and the role regulatory agencies, such as the US Food and Drug Administration, play in the approval process. We discuss phases of clinical trials – phases 1, 2, 3 and 4 – to evaluate efficacy and safety of drug products, along with case studies. We define pharmacoepidemiology – a scientific discipline to study use, safety and effectiveness of medical products and interventions – and the role it plays throughout the drug life cycle with relevant case studies. We also discuss how pharmacoepidemiology informs regulatory, policy and clinical decisions. Finally, we provide high-level overview of essential tools – study protocols, data sources, study designs and analysis – to conduct pharmacoepidemiologic studies.
Health Information Technology Fundamentals
Johns Hopkins University (via Coursera)
In this course you will receive an overview of the health IT ecosystem with a specific focus on the role of electronic health records (EHRs). You’ll be introduced to the factors that contributed to the move from paper records to digitized records and who the most common vendors are. We’ll go over features of EHRs such as computerized provider order entry, clinical decision support, documentation capabilities, and medication reconciliation. Like a physician’s stethoscope, the EHR has become an important tool in healthcare delivery and plays a part throughout the patient’s journey. You’ll go through each of the steps from patient scheduling, to front desk registration, outpatient visits, emergency room encounters, and inpatient admissions. During the course, we’ll also cover examples of how technical issues related to the EHR can be as simple as problems with logging or password resets. But how they can also be more complex related to alerts that are firing and the display of information. Although some of those challenges are beyond the scope of the IT support staff, having familiarity with the scope of potential problems and the broader EHR landscape is important. This course also includes an introduction to database architecture, servers, and interfaces. We wrap up by discussing the importance of training end-users on healthcare technology and the way in which effective change management strategies are crucial.
Datenanalyse verwalten
Johns Hopkins University (via Coursera)
Dieser einwöchige Kurs beschreibt den Prozess der Datenanalyse und wie man diesen Prozess verwaltet. Wir beschreiben den iterativen Charakter der Datenanalyse sowie die Rolle der Formulierung einer präzisen Frage, der explorativen Datenanalyse, der Inferenz, der formalen statistischen Modellierung, der Interpretation und der Kommunikation. Darüber hinaus werden wir beschreiben, wie analytische Aktivitäten innerhalb eines Teams gelenkt werden können und der Datenanalyseprozess in Richtung kohärenter und nützlicher Ergebnisse gesteuert werden kann. Dies ist ein zielgerichteter Kurs, der Sie schnell in den Prozess der Datenanalyse einführen und Ihnen zeigen soll, wie dieser gehandhabt werden kann. Unser Ziel war es, dies für Sie so bequem wie möglich zu gestalten, ohne auf wesentliche Inhalte zu verzichten. Wir haben die technischen Informationen beiseitegelassen, damit Sie sich darauf konzentrieren können, Ihr Team zu managen und voranzubringen. Nach Abschluss dieses Kurses werden Sie: 1. Die grundlegende Datenanalyse-Iteration beschreiben 2. Verschiedene Arten von Fragen identifizieren und diese in spezifische Datensätze umwandeln 3. Verschiedene Arten von Datenabrufen beschreiben 4. Datensätze untersuchen, um festzustellen, ob Daten für eine bestimmte Frage geeignet sind 5. Modellerstellungsbemühungen bei gebräuchlichen Datenanalysen steuern 6. Die Ergebnisse gebräuchlicher Datenanalysen interpretieren 7. Statistische Ergebnisse zur Bildung kohärenter Datenanalyse-Präsentationen integrieren Aufwand: 1 Kurswoche, 4-6 Stunden Kurs-Cover-Bild von fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD
Beginning Custom Projects with Raspberry Pi
Johns Hopkins University (via Coursera)
In this course you will use a Raspberry Pi 4 to build a complete network-connected project with sensors and motors and access it from your smartphone. We'll explore all the parts which make this work, so you can use this experience as a foundation for your own projects. We'll use the Raspberry Pi as an "embedded system" (as opposed to a desktop computer) so you're ready to build a Raspberry Pi into your projects as the brains that make it all work. Want to build your own Internet of Things (IoT) device? Home automation? Robotics? This is the class to learn how it all works, to get you building on your own. No experience in embedded systems, programming, or electronics is assumed, and optional bonus sections are provided for those who want a fast start in Python programming, Linux essentials, and basic electronics. The course is divided into four modules to explore each focus area with demontrations and extras along the way: 1) installing and configuring a Raspberry Pi, 2) accessing the Raspberry Pi over the network, 3) programmatically controlling external sensors and motors, and 4) accessing the embedded device through a web interface. After these four modules you'll get started building your own projects right away, and the three follow-on courses in this Coursera specialization dive into each area to really boost your skills and the complexity of your projects. I hope you enjoy all the courses and I hope you take your builds to the next level.
Foundations of Probability and Random Variables
Johns Hopkins University (via Coursera)
The course "Foundations of Probability and Random Variables" introduces fundamental concepts in probability and random variables, essential for understanding computational methods in computer science and data science. Through five comprehensive modules, learners will explore combinatorial analysis, probability, conditional probability, and both discrete and continuous random variables. By mastering these topics, students will gain the ability to solve complex problems involving uncertainty, design probabilistic models, and apply these concepts in fields like machine learning, AI, and algorithm design. What makes this course unique is its practical approach: students will develop hands-on proficiency in the R programming language, which is widely used in data science and statistical modeling. The course also includes real-world applications, allowing learners to bridge theoretical knowledge with practical problem-solving skills. Whether you are aiming to pursue advanced studies in machine learning or develop data-driven solutions in professional settings, this course provides the solid foundation you need to excel. Designed for learners with a background in calculus and basic programming, this course prepares you to tackle more advanced topics in computational science.
Regression Models
Johns Hopkins University (via Coursera)
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
Distributed Query Optimization and Security
Johns Hopkins University (via Coursera)
The course "Distributed Query Optimization and Security" provides a comprehensive exploration of query optimization and data security in distributed databases. Students will gain in-depth knowledge of how to secure data access through views and dynamic authorization techniques, essential for maintaining the integrity and confidentiality of distributed systems. Learners will also master distributed query processing, understanding how to evaluate, optimize, and implement efficient query plans. The course uniquely blends advanced database security techniques with practical applications of large-scale data systems, such as Hadoop, MapReduce, and HDFS. By completing this course, learners will be equipped with the skills to optimize complex queries, enhance database security, and handle large datasets effectively. With hands-on experience in MapReduce and HDFS, learners will develop the ability to create scalable, optimized, and secure distributed database systems. This course is ideal for professionals seeking to advance their expertise in database management and distributed systems, with a focus on both performance optimization and data protection.
Introduction to Intrusion Detection Systems (IDS)
Johns Hopkins University (via Coursera)
This course introduces you to Intrusion Detection Systems (IDS), offering essential knowledge and hands-on skills for detecting and mitigating security threats. As cyberattacks become more sophisticated, learning to protect systems through IDS is a critical skill for IT and security professionals. This course is designed to give you a comprehensive understanding of both Host-Based (HIDS) and Network-Based Intrusion Detection Systems (NIDS). You’ll dive into core components, explore the differences between signature-based and anomaly-based detection, and gain practical experience by operating IDS tools on virtual machines. What makes this course unique is its combination of theory and real-world application: you’ll learn to configure IDS technologies, develop custom rules, and evaluate IDS performance quantitatively. By the end of this course, you’ll be equipped to identify and respond to security threats in various environments, from individual hosts to complex networks. This practical knowledge will set you apart, enhancing your ability to protect critical systems against emerging cyber threats.
Getting and Cleaning Data
Johns Hopkins University (via Coursera)
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
A Crash Course in Data Science
Johns Hopkins University (via Coursera)
By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials. This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. The role of a data science manager Course cover image by r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT
Applied Calculus with Python
Johns Hopkins University (via Coursera)
This course is designed for the Python programmer who wants to develop the foundations of Calculus to help solve challenging problems as well as the student of mathematics looking to learn the theory and numerical techniques of applied calculus implemented in Python. By the end of this course, you will have learned how to apply essential calculus concepts to develop robust Python applications that solve a variety of real-world challenges. Video lectures, readings, worked examples, assessments, and Python code are all provided in the course. These are used to illustrate techniques to solve equations, work with functions, and compute and apply derivatives and integrals. If you are interested in starting to develop concepts in fields such as applied math, data science, cybersecurity, or artificial intelligence, or just need a refresher of calculus or coding in Python, then this course is right for you.
Chemicals and Health
Johns Hopkins University (via Coursera)
This course covers chemicals in our environment and in our bodies and how they impact our health. It addresses policies and practices related to chemicals, particularly related to how they get into our bodies (exposures), what they do when they get there (toxicology), how we measure them (biomonitoring) and their impact on our health. Most examples are drawn from the US.
Psychedelic Science and Medicine
Johns Hopkins University (via Coursera)
Explore the science and therapeutic potential of psychedelics in this course led by experts from the Johns Hopkins Center for Psychedelic and Consciousness Research. You'll learn about the history of psychedelic use, the neuroscience underlying their effects, and the latest clinical trials evaluating their therapeutic potential. Gain insights into their risks and benefits, ethical considerations like informed consent, and ongoing challenges in the field. This course equips learners to critically assess scientific findings, moving beyond hype to understand the evidence. Whether you're interested in the neurobiological mechanisms of psychedelics, their role in mental health treatment, or broader societal impacts, this course provides a comprehensive and evidence-based foundation. Perfect for students, professionals, or anyone curious about this emerging area of science, the course offers a unique opportunity to engage with cutting-edge research and practical implications for medicine and beyond.
Multiple Regression Analysis in Public Health
Johns Hopkins University (via Coursera)
Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, you'll extend simple regression to the prediction of a single outcome of interest on the basis of multiple variables. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include multiple logistic regression, the Spline approach, confidence intervals, p-values, multiple Cox regression, adjustment, and effect modification.
Introduction to Concurrent Programming with GPUs
Johns Hopkins University (via Coursera)
This course will help prepare students for developing code that can process large amounts of data in parallel. It will focus on foundational aspects of concurrent programming, such as CPU/GPU architectures, multithreaded programming in C and Python, and an introduction to CUDA software/hardware.
Calculus through Data & Modelling: Integration Applications
Johns Hopkins University (via Coursera)
This course continues your study of calculus by focusing on the applications of integration. The applications in this section have many common features. First, each is an example of a quantity that is computed by evaluating a definite integral. Second, the formula for that application is derived from Riemann sums. Rather than measure rates of change as we did with differential calculus, the definite integral allows us to measure the accumulation of a quantity over some interval of input values. This notion of accumulation can be applied to different quantities, including money, populations, weight, area, volume, and air pollutants. The concepts in this course apply to many other disciplines outside of traditional mathematics. We will expand the notion of the average value of a data set to allow for infinite values, develop the formula for arclength and curvature, and derive formulas for velocity, acceleration, and areas between curves. Through examples and projects, we will apply the tools of this course to analyze and model real world data.
Der Werkzeugkasten des Data Scientist
Johns Hopkins University (via Coursera)
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
Données au service de la santé de la population
Johns Hopkins University (via Coursera)
Dans ce cours, vous apprendrez les méthodologies qui soutiennent l’utilisation réussie des données dans le but de renforcer les programmes et les politiques de santé publique. Des experts du monde entier définiront et expliqueront ce que sont les données au niveau de la population, présenteront le cycle de la génération à l’utilisation des données et expliqueront d’autres considérations pour une utilisation réussie des données pour la santé de la population. Vous apprendrez également comment les données sur les services de santé peuvent être utilisées pour éclairer la prise de décision au niveau de la population, et comment appliquer une dimension de genre et d’équité à ces systèmes de données pour s’assurer qu’ils répondent aux besoins des populations. L’objectif principal de ce cours est de soutenir et d’améliorer l’utilisation des données pour éclairer les politiques. Le cours est le fruit d’une collaboration entre de multiples partenaires, dont Vital Strategies, la Fondation des Centres de contrôle et de prévention des maladies (CDC), la Johns Hopkins Bloomberg School of Public Health et l’Organisation mondiale de la Santé. Ce cours a été financé par Bloomberg Philanthropies et co-financé par le gouvernement australien et la Fondation Bill et Melinda Gates.
Foundations of Distributed Database Systems
Johns Hopkins University (via Coursera)
The course "Foundations of Distributed Database Systems" lays the foundation for understanding distributed database systems, a cornerstone of modern data management. You’ll delve into core principles and architectures, gaining insight into the challenges of managing data across distributed environments. Through hands-on learning, you’ll explore horizontal and vertical partitioning techniques, understanding how to apply them to improve query performance and scalability. By mastering these concepts, you’ll be equipped to design and optimize databases that handle large-scale data efficiently. What makes this course unique is its emphasis on practical implementation, enabling you to translate theoretical knowledge into actionable skills. Whether you're a student, data professional, or developer, this course will empower you to build robust distributed database systems, a critical skill in today’s data-driven world. Prepare to tackle real-world scenarios with confidence and acquire the expertise to manage the complexities of distributed data systems effectively.
Principles of fMRI 2
Johns Hopkins University (via Coursera)
Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from many disciplines. Psychologists, statisticians, physicists, computer scientists, neuroscientists, medical researchers, behavioral scientists, engineers, public health researchers, biologists, and others are coming together to advance our understanding of the human mind and brain. This course covers the analysis of Functional Magnetic Resonance Imaging (fMRI) data. It is a continuation of the course “Principles of fMRI, Part 1”.
The Persuasive Leader
Johns Hopkins University (via Coursera)
This aims primarily at post-baccalaureate students interested in leadership theory. The course has four modules. Module 1 introduces students to agile leadership as (a) a logical sequel to adaptive and team leadership, and (b) the foundation of contemporary persuasive leadership. Topics include a working definition of agile leadership, the need for agile leadership, and characteristics of the agile leader. Module 2 answers the question, What is persuasive leadership? Topics include persuasive vs. coercive leadership, conversation as essential to persuasive leadership, types of persuasion, elements of persuasion, and principles of persuasion. Module 3 answers the question, Why persuasive leadership? Topics include setting the question, benefits to the organization, benefits, to the team, and benefits to the leader. Module 4 answers the question, Persuasive leadership: How? Topics include preparing the leader, preparing the team, engaging the team in a plan, executing the plan, and assessing and improving. To complete this course successfully students should be able to analyze college-level readings and audio/visual presentations into understandable parts, including premises and conclusions; synthesize the results of the analysis into coherent and accurate summaries; and evaluate the results for accuracy and practical applicability. This is one course in the Coursera specialization, Leadership: An Introduction. It examines current trends in leadership theory invoking several disciplines, including business, sociology, philosophy, history, and psychology. Upon successful completion of the course, students will be able to • Define persuasive leadership • Explain how adaptive leadership, team leadership, and agile leadership underlie persuasive leadership • Assess the value of persuasive leadership to contemporary organizational leadership • Apply techniques of persuasive leadership to organizational challenges
Advanced Neural Network Techniques
Johns Hopkins University (via Coursera)
The course "Advanced Neural Network Techniques" delves into advanced neural network methodologies, offering learners an in-depth understanding of cutting-edge techniques such as Recurrent Neural Networks (RNNs), Autoencoders, Generative Neural Networks, and Deep Reinforcement Learning. Through hands-on projects and practical applications, learners will master the mathematical foundations and deployment strategies behind these models. You will explore how RNNs handle sequence data, uncover the power of Autoencoders for unsupervised learning, and dive into the transformative potential of generative models like GANs. The course also covers reinforcement learning, equipping you with the skills to solve complex decision-making problems using deep neural networks and Markov Chains. Designed to bridge theoretical knowledge and practical implementation, this course stands out by incorporating real-world challenges, ethical considerations, and future research directions.
Pillar #4: Drug Safety - Pharmacovigilance
Johns Hopkins University (via Coursera)
This course provides a comprehensive overview of drug safety and pharmacovigilance. We start with the fundamentals of pharmacovigilance and regulatory requirements, including phase 4 post-marketing safety studies. We discuss passive and active surveillance methods, exploring key surveillance systems in the US and globally. The course concludes with real-world examples, demonstrating how drug safety research impacts regulatory decisions and prescribing practices.
Foundations for Active Learning in STEM Courses
Johns Hopkins University (via Coursera)
This course is aimed a current or future educators in the Science, Technology, Engineering and Math (STEM) disciplines and will take you through a deep dive into active learning theories, techniques, and practical applications for science, technology, engineering, and mathematics.
Teaching Writing Process
Johns Hopkins University (via Coursera)
Half a century ago, a revolution took place in the teaching of writing. Educators asked, “What if we were to study how professional writers wrote, as a way to learn how we might teach writing more effectively?” The result was the writing process movement, with its emphasis on not just writing as product but as process. Good writing doesn’t magically appear, nor does it spring from the brain fully formed and perfect the first time. Instead, all writers engage in a process, and while that process can vary writer to writer, some stages do seem to apply, in some way, to almost every writing situation. This course demonstrates how teaching writing as process can lead to a significant increase in students’ comfort level and confidence as writers. Learners will examine methods for teaching writing as discovery, and for using low-stakes writing and reflection in their classes. They’ll fill their toolbox with practical strategies and techniques for teaching writing to students of any age, in any situation.
Global Sodium Reduction Strategies
Johns Hopkins University (via Coursera)
This course will help guide policy makers, advocates, and program managers as they design, plan, and implement sodium reduction interventions to protect public health. We invite you to see what interventions have been proven at scale, what shows promise, and what lessons have been learned along the way from the implementation of sodium reduction strategies all around the globe. Our emphasis is implementation in settings with resource constraints. There are nine modules in this course. The first two modules set the stage with information on the science of sodium and context for lowering intake at a population level; the next five modules describe specific interventions; and the final two modules discuss comprehensive strategies in the wider context of public health, as well as tools for monitoring and evaluating interventions. Global Sodium Reduction Strategies was created by a team at the Johns Hopkins Bloomberg School of Public Health and is supported by the Resolve to Save Lives Initiative.
Data Science Decisions in Time: Using Data Effectively
Johns Hopkins University (via Coursera)
Sequential Decisions builds from math and algorithms that can be understood and used by Coursera Students. This course will start from a consideration of the simplest type of data streams and then gradually advance to more complex types of data and more nuanced decisions being made on that data. You will be able to: (a) program optimal decisions for data arriving from known distribution functions, (b) define error bars and nuanced hedges about ongoing data streams to reflect missing data and/or missing knowledge, (c)understand and use the connections from these models to further understand Markov Chains and Markov Processes and how these ideas connect to Reinforcement Learning and (d) Understand better the nuances between time-independent, time-dependent, one-dimensional and multi-dimensional data. The course is aimed at those working with data, this includes both those charged with analyzing the data and those in charge of making decisions based on that data.
Disease Clusters
Johns Hopkins University (via Coursera)
Do a lot of people in your neighborhood all seem to have the same sickness? Are people concerned about high rates of cancer? Your community may want to explore the possibility of a disease cluster, which happens when there is a higher number of cases of disease than expected. When communities hear about cases of disease in their neighborhood, they are rightfully concerned. However, the results of investigations by the health department often find no evidence of a cluster. This course will help you understand what a disease cluster is and how it is studied. The goal is to empower community (or citizen) scientists, and to help build better relationships between communities and health officials.
Responsible Reporting on Suicide for Journalists
Johns Hopkins University (via Coursera)
Responsible Reporting on Suicide for Journalists is designed to give working journalists and students who are interested in the field an understanding of how news media can impact suicide trends and how that power can be used to improve public health. An extensive body of research shows that certain methods of reporting on suicide deaths can increase the number of subsequent suicides among the public. Conversely, responsible methods of reporting on suicide can increase the likelihood of people seeking help. The World Health Organization and the Centers for Disease Control and Prevention have both identified responsible suicide reporting among the media as a key mechanism for suicide prevention. This course aims to give journalists the concrete tools they need to fulfill that goal and make a positive public health impact with their reporting.
Introduction to AI for Cybersecurity
Johns Hopkins University (via Coursera)
In "Introduction to AI for Cybersecurity," you'll gain foundational knowledge of how artificial intelligence (AI) is transforming the field of cybersecurity. This course covers key AI techniques and how they can be applied to enhance security measures, detect threats, and secure digital systems. Learners will explore hands-on implementations of AI models using tools like Jupyter Notebooks, allowing them to detect spam, phishing emails, and secure user authentication using biometric solutions. What makes this course unique is its focus on real-world applications, blending AI theory with practical skills relevant to today's cybersecurity challenges. By the end of the course, you'll have developed the ability to use AI to address cyber threats such as email fraud and fake logins, and will be equipped with practical skills to protect digital assets in a rapidly evolving technological landscape. Whether you're a cybersecurity professional or someone seeking to expand your skills in AI, this course provides a critical understanding of how AI can be leveraged to mitigate security risks and keep systems secure.
Foundations of Health Equity Research
Johns Hopkins University (via Coursera)
Introduces students to the core principles of health equity research. Covers topics such as defining health equity, engaging community and policy stakeholders, patient-centeredness, cultural competence, and dissemination of research findings. Content will recognize different geographic, cultural, and social contexts where health inequities occur.
Sustainable Neighborhoods
Johns Hopkins University (via Coursera)
This course will provide students with an introduction to tools and concepts for a better understanding of the importance of the neighborhood as part of a sustainable city. This will include a careful look at the natural context of successful neighborhoods. An evaluation of important components and structure that create a sustainable neighborhood. Complete neighborhoods can provide their residents with pedestrian access to schools, daycare, recreational centers, and a variety of open spaces, as well as opportunities for food production. These and other aspects of sustainable neighborhoods will be carefully evaluated in this course. There will also be case studies of neighborhood development projects in Houston, TX, San Antonio, TX, and Chattanooga, TN. And lastly, the course will review the importance of zoning on sustainable neighborhoods with a particular focus on form-based zoning. By the end of this course, you will be able to: • Construct general plans for urban parks and natural corridors for achieving natural context neighborhoods. • Analyze how neighborhood schools, support services and civic sites contribute to neighborhood livability and sustainability. • Examine how local open space and housing density can be balanced as neighborhood components to achieve sustainable communities. • Evaluate the aspects of neighborhood structure that include packet parks, open-space configurations, and transit orientation in communities. • Apply lessons learned from the evaluation of three case studies at the Buffalo Bayou project in Houston, Texas, the Pearl District project in San Antonio Texas, and 21st century waterfront project in Chattanooga Tennessee. • Explain the importance of zoning for sustainable neighborhoods with a particular focus on form-based zoning. Example backgrounds that would be helpful for students to succeed in this course: To have a general understanding of how public/private projects are planned and implemented Have a strong interest or e...
Reproducible Research
Johns Hopkins University (via Coursera)
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
Data Science Capstone
Johns Hopkins University (via Coursera)
The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners.
Surveillance Systems: The Building Blocks
Johns Hopkins University (via Coursera)
Epidemiology is often described as the cornerstone science and public health and public health surveillance is a cornerstone of epidemiology. This course will help you build your technical awareness and skills for working with a variety of surveillance systems. Along the way, we'll focus on system objectives, data reporting, the core surveillance attributes, and performance assessment. This course is designed for public health practitioners and anyone who wants to learn more about the basics of public health surveillance. If you develop or implement surveillance systems or aspire to do so or use the data resulting from surveillance, then this course is for you. It's s also for people who are interested in understanding more about this fundamental epidemiologic tool and public health practice.
The R Programming Environment
Johns Hopkins University (via Coursera)
This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.
Introduction to Penetration Testing and Ethical Hacking
Johns Hopkins University (via Coursera)
This Advanced Penetration Testing and Ethical Hacking course offers a deep dive into key cybersecurity concepts, providing you with hands-on experience in areas like penetration testing, cryptography, and social engineering. Through engaging modules, you will learn to conduct penetration tests using industry-standard methodologies, such as the Penetration Testing Execution Standard (PTES), and effectively communicate findings through professional reports and executive summaries. You’ll also gain a solid understanding of cryptographic principles, including public key infrastructure (PKI), while mastering techniques for ethical hacking and countering cryptographic attacks. Additionally, the course covers the art of information gathering and social engineering, equipping you to conduct reconnaissance and craft spear phishing attacks. With practical labs and real-world scenarios, this course will help you build the skills needed to protect mission-critical infrastructures and advance your career in cybersecurity.
Modeling Data in the Tidyverse
Johns Hopkins University (via Coursera)
Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships. This course covers the types of questions you can ask of data and the various modeling approaches that you can apply. Topics covered include hypothesis testing, linear regression, nonlinear modeling, and machine learning. With this collection of tools at your disposal, as well as the techniques learned in the other courses in this specialization, you will be able to make key discoveries from your data for improving decision-making throughout your organization. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
Clinical Trials Management and Advanced Operations
Johns Hopkins University (via Coursera)
In this course, you’ll learn about the more advanced elements of managing clinical trials. From anticipating and planning for protocol events to conducting systematic reviews to synthesize evidence, you and your study team need the skills to implement best practices throughout the trial process. You’ll learn how to recognize and respond to problems and adverse events, comply with regulations, and participate in frameworks that promote transparency. You’ll also learn how systematic review and meta-analysis is used to synthesize evidence from multiple sources. Finally, you’ll learn how your research can benefit from the adoption and consistent use of standardized study documents.
Implementing a Patient Safety or Quality Improvement Project (Patient Safety V)
Johns Hopkins University (via Coursera)
Now that you’ve carefully planned your patient safety and quality improvement project, the real work can begin. This course will introduce students to the unique challenges encountered when implementing, maintaining, and expanding a patient safety and quality initiative. Students will learn to apply lessons learned from the 4 E model and TRiP into developing specific aims for their QI project. Additionally, students will develop a plan to address the adaptive and technical challenges in their projects including whether their initiative needs to be submitted to an Institutional Review Board (IRB). Finally, students will develop plans to grow their local QI project into a system-wide project.
Mathematics and Democracy Teach Out
Johns Hopkins University (via Coursera)
This course is aimed at anyone who has interest in the lens through which mathematicians view democracy. You will learn theories and approaches to the mathematics of voting.
Honors Algebra 2: Algebraic, Exponential & Log Functions
Johns Hopkins University (via Coursera)
This is the third course in the Honors Algebra 2 sequence. By the end of this course, students will be able to analyze, interpret, and apply advanced families of functions, including exponential, logarithmic, radical, and inverse functions. Learners will develop fluency in evaluating and graphing these functions, solving equations that involve them, and applying their properties to real-world models and predictions. Along the way, students will deepen their understanding of how different function types connect, building a stronger algebraic foundation for future courses in precalculus, calculus, and beyond. This course is designed not only to strengthen core problem-solving skills but also to highlight the power of mathematics as a tool for describing patterns and solving practical problems. What makes this course unique is its balance between theory and application: students gain a clear understanding of the underlying algebraic rules while also engaging with meaningful contexts such as growth and decay, investment modeling, and data analysis. By completing this course, learners will be prepared to tackle higher-level mathematics with confidence and insight.
Quality and Safety in Ambulatory Healthcare Management
Johns Hopkins University (via Coursera)
Welcome to the Quality and Safety in Ambulatory Healthcare Management course! This comprehensive 4-week course provides you with the knowledge and skills needed to effectively manage quality and safety initiatives within ambulatory healthcare settings. This course is designed for entry-level and beginner learners with little or no background in healthcare, but who may be interested in transitioning into the field. Quality and safety are paramount in healthcare. This course delves into quality systems in healthcare, such as CMS/MIPPS vs. NCQA and HEDIS, safety and risk management, developing quality and safety programs, promoting a culture of safety, performance improvement, and project management. You will learn how to implement and maintain high-quality and safe healthcare practices. It will also introduce various regulatory bodies like CMS and Joint Commission. By the end of this course, you will have the necessary knowledge and skills to develop, implement, and manage quality and safety programs in ambulatory healthcare settings. Start your journey to enhance patient safety, improve quality of care, and promote a culture of excellence in ambulatory healthcare management. There are no specific prerequisites for this program. By the end of this course, you will have the necessary knowledge and skills to develop, implement, and manage quality and safety programs in ambulatory healthcare settings. Start yourJoin us on this learning journey to enhance patient safety, improve quality of care, and promote a culture of excellence in ambulatory healthcare management.
R البرمجة باستخدام لغة
Johns Hopkins University (via Coursera)
ستتعلم في هذه الدورة كيفية البرمجة بلغة R وكيفية استخدامها لتحليل البيانات بصورة فعالة. ستتعلم كيفية تثبيت البرامج اللازمة لبيئة البرمجة الإحصائية وتكوينها وكيفية وصف مفاهيم لغة البرمجة العامة إذ يتم تطبيقها بلغة إحصائية عالية المستوى. تتناول الدورة المشكلات العملية في الحوسبة الإحصائية التي تشمل البرمجة في لغة R، وقراءة البيانات في لغة البرمجة R، والوصول إلى حزم R، وكتابة دوال R، وتصحيح الأخطاء، وتحديد التعليمات البرمجية R، وتنظيم التعليمات البرمجية R والتعليق عليها. ستعرض الموضوعات في تحليل البيانات الإحصائية أمثلة عملية.
Trustworthy AI: Managing Bias, Ethics, and Accountability
Johns Hopkins University (via Coursera)
The course "Responsible AI and Ethics" explores the ethical, social, and technical aspects of artificial intelligence (AI) and machine learning (ML). It focuses on understanding bias in both human and machine systems and provides strategies for mitigating risks. By examining key issues such as fairness, accountability, and the regulatory landscape, learners will gain essential knowledge to navigate the ethical challenges in AI. Through case studies and real-world examples, students will explore the complexities of AI implementations, assessing their impact on society and industries. This course provides practical insights into responsible AI development, emphasizing both ethical decision-making and effective risk management. By the end of the course, learners will be equipped to lead AI projects that balance innovation with accountability, ensuring AI systems are fair, transparent, and sustainable. This unique combination of theoretical knowledge and real-world applications makes the course invaluable for anyone aiming to lead in the AI field.
Generative AI and Symbolic Reasoning
Johns Hopkins University (via Coursera)
The course "Generative AI" provides an in-depth exploration of generative AI, focusing on both the theory and practical applications of transformers, large language models, and symbolic AI. By completing the course, learners will gain a comprehensive understanding of how these technologies work and how they can be integrated to solve complex problems and generate new content. Through real-world case studies, students will analyze the strengths and weaknesses of generative AI systems, preparing them for the challenges and opportunities they will face in AI leadership roles. What sets this course apart is its focus on the intersection of symbolic AI and generative processes, providing insights into how these models can be enhanced for explainability and control. By examining both stochastic and symbolic AI, learners will understand how these approaches complement each other in creating responsible, ethical, and sustainable AI systems. Whether you're looking to lead AI projects, integrate cutting-edge AI tools, or understand their broader implications, this course equips you with the skills needed to navigate the evolving landscape of generative AI.
Health Equity Research & Practice: Local & Global Lessons
Johns Hopkins University (via Coursera)
This course introduces students to the local and global lessons in health equity research and practice, covering topics such as the effects of structural drivers and systems of power on health equity and inequities in reproductive health and immigrant health. This course then goes on to apply these lessons to health equity research projects in settings around the world. Through exploration of global interventions, learners will be prepared to take on health disparities in their local communities by understanding why these health problems exist, what contributes to them, and how to create sustainable solutions. This course includes lectures, panel discussions, and interviews with leading experts.
Big Data and Hadoop Foundations and Setup
Johns Hopkins University (via Coursera)
The course "Big Data and Hadoop Foundations and Setup" offers a comprehensive introduction to the world of Big Data and Hadoop, providing foundational knowledge crucial for navigating modern data-driven environments. You’ll explore the limitations of traditional data processing technologies and understand how Hadoop addresses these challenges with its robust architecture and ecosystem. Through detailed modules, you will gain a deep understanding of Big Data concepts, the role of Data Science and Big Data Analytics, and the trends shaping the Big Data revolution. The course demystifies Hadoop's subprojects and distributions, giving you the tools to differentiate between them and apply their features to real-world problems. What sets this course apart is its hands-on approach. You'll install, configure, and run Hadoop in a Linux environment, building the technical proficiency needed to process large-scale data effectively. Whether you’re looking to enhance your career in Data Science or understand Big Data’s transformative impact on businesses, this course equips you with the skills to succeed.
Precalculus: Relations and Functions
Johns Hopkins University (via Coursera)
This course helps to build the foundational material to use mathematics as a tool to model, understand, and interpret the world around us. This is done through studying functions, their properties, and applications to data analysis. Concepts of precalculus provide the set of tools for the beginning student to begin their scientific career, preparing them for future science and calculus courses. This course is designed for all students, not just those interested in further mathematics courses. Students interested in the natural sciences, computer sciences, psychology, sociology, or similar will genuinely benefit from this introductory course, applying the skills learned to their discipline to analyze and interpret their subject material. Students will be presented with not only new ideas, but also new applications of an old subject. Real-life data, exercise sets, and regular assessments help to motivate and reinforce the content in this course, leading to learning and mastery.
Advanced Linear Models for Data Science 2: Statistical Linear Models
Johns Hopkins University (via Coursera)
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: A basic understanding of linear algebra and multivariate calculus. A basic understanding of statistics and regression models. At least a little familiarity with proof based mathematics. Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.
Statistics for Genomic Data Science
Johns Hopkins University (via Coursera)
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Precalculus: Mathematical Modeling
Johns Hopkins University (via Coursera)
This course helps to build the foundational material to use mathematics as a tool to model, understand, and interpret the world around us. This is done through studying functions, their properties, and applications to data analysis. Concepts of precalculus provide the set of tools for the beginning student to begin their scientific career, preparing them for future science and calculus courses. This course is designed for all students, not just those interested in further mathematics courses. Students interested in the natural sciences, computer sciences, psychology, sociology, or similar will genuinely benefit from this introductory course, applying the skills learned to their discipline to analyze and interpret their subject material. Students will be presented with not only new ideas, but also new applications of an old subject. Real-life data, exercise sets, and regular assessments help to motivate and reinforce the content in this course, leading to learning and mastery.
Introduction to Systematic Review and Meta-Analysis
Johns Hopkins University (via Coursera)
We will introduce methods to perform systematic reviews and meta-analysis of clinical trials. We will cover how to formulate an answerable research question, define inclusion and exclusion criteria, search for the evidence, extract data, assess the risk of bias in clinical trials, and perform a meta-analysis. Upon successfully completing this course, participants will be able to: Describe the steps in conducting a systematic review Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework Describe the process used to collect and extract data from reports of clinical trials Describe methods to critically assess the risk of bias of clinical trials Describe and interpret the results of meta-analyses
Engineering Life: Synbio, Bioethics & Public Policy
Johns Hopkins University (via Coursera)
Synbio is a diverse field with diverse applications, and the different contexts (e.g., gain-of-function research, biofuels) raise different ethical and governance challenges. The objective of this course is to increase learners’ awareness and understanding of ethical and policy/governance issues that arise in the design, conduct and application of synthetic biology. The course will begin with a short history of recombinant DNA technology and how governance of that science developed and evolved, and progress through a series of areas of application of synbio. Content will be presented in many forms, including not only reading and lectures, but also recorded and live interviews and discussions with scientists, ethicists and policy makers. Learners will have the opportunity to think, write and talk about the issues and challenges in their own work and in real-life case examples. A final project will engage students in the development of governance models for synbio.
Simple Regression Analysis in Public Health
Johns Hopkins University (via Coursera)
Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, we'll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.
COVID Vaccine Ambassador Training: How to Talk to Parents
Johns Hopkins University (via Coursera)
Vaccination is a key strategy for preventing serious illness and death from COVID-19. COVID-19 vaccines are available for children 5 and older, but many parents have questions about vaccinations. This training course prepares parents of school-age children, PTAs, community members, and school staff to be Vaccine Ambassadors and promote vaccine acceptance in their communities. After completing the course, Vaccine Ambassadors will be able to share knowledge about COVID-19 and the COVID-19 vaccine, engage in conversations about vaccine hesitancy in a respectful and empathetic way, and direct people to credible sources for further information about COVID-19 vaccines.
Linear Algebra: Orthogonality and Diagonalization
Johns Hopkins University (via Coursera)
This is the third and final course in the Linear Algebra Specialization that focuses on the theory and computations that arise from working with orthogonal vectors. This includes the study of orthogonal transformation, orthogonal bases, and orthogonal transformations. The course culminates in the theory of symmetric matrices, linking the algebraic properties with their corresponding geometric equivalences. These matrices arise more often in applications than any other class of matrices. The theory, skills and techniques learned in this course have applications to AI and machine learning. In these popular fields, often the driving engine behind the systems that are interpreting, training, and using external data is exactly the matrix analysis arising from the content in this course. Successful completion of this specialization will prepare students to take advanced courses in data science, AI, and mathematics.
Child Housing Assessment for a Safer Environment Teach-Out
Johns Hopkins University (via Coursera)
Unintentional injuries are the leading cause of death for children aged 1-14. Young children spend most of their time at home, making them more vulnerable to home injuries. Many of these injuries are preventable with proper education and safety measures. By addressing common household hazards, we can significantly reduce the risk of injury and create a safer living environment for children. The CHASE (Children’s Housing Assessment for a Safe Environment) protocol is a comprehensive tool designed to identify and mitigate injury risks in children’s homes. This course provides both families and health professionals with the knowledge and tools necessary to prevent common home injuries through a structured approach. Each module covers a specific risk area, including carbon monoxide safety, falling furniture, fire safety, bathroom safety, window safety, stair safety, hot water safety, electrical safety, poison storage, and strangulation hazards. Assessors will learn about the risks, practical solutions, and engage in activities to ensure a safer living environment. Each module includes detailed training on assessing specific risks, using the CHASE tool effectively, and providing recommendations and interventions. Assessors will learn how to conduct thorough home inspections, provide educational materials, and facilitate necessary modifications to create safer homes for children. The course emphasizes the importance of proactive measures and equips participants with knowledge and tools to safeguard their homes effectively. Each module includes videos, readings, discussion prompts, and hands-on activities to ensure comprehensive understanding and practical application of the safety measures.
Global Health Program Equity and Quality: Plan and Manage
Johns Hopkins University (via Coursera)
Discover the inner workings of how to plan and manage a global health initiative Identify the logistical details of an effective global health program Discuss the best practices for human resources management in disease control programs Planning and managing a global health initiative involves a complex set of issues. At every level from global to national and sub-national, there are a wide range of considerations to make. Additionally, many steps and many actors are involved. These include establishing a robust supply chain, addressing corruption, thinking about the intersectionality of poverty and gender, and considering how the program may advance or hinder the larger goal of health equality. Using the polio eradication effort as a case study, you’ll 1) address the foundational concepts, theoretical frameworks, and practical details involved in this complicated process, and 2) explore all of these stages and will decipher how to address each of them within the context of global health. You’ll also learn about how to acquire, train and deploy appropriate human resources to each individual stage of the global health program, and will again use the polio eradication effort to put this into context.
Building Alliances in Global Health: From Global to Local
Johns Hopkins University (via Coursera)
Build alliances in global health using polio eradication as a case study Discuss the value of and best practices for building alliances in global health Apply communication and community engagement strategies to disease control program This hands-on course will prepare you to overcome the challenges associated with alliance building within the global health sector. Using The Global Polio Eradication Initiative as a case study, you’ll address the themes of political advocacy, policymaking, health communications, and community engagement, and explore the politics and interpersonal relations required to build a global health program. The Global Polio Eradication Initiative is an exemplar of global partnerships between UN agencies, the private sector, non-governmental actors, civil society organizations, and local communities. You’ll learn about the wide variety of stakeholders present throughout the policy and planning processes, and will also get an insight into the challenges that can sometimes occur due to resistance, opposition, and even hostility.
Managing Yourself and Others
Johns Hopkins University (via Coursera)
Effective management starts with mastering yourself and building strong, high-performing teams. In this course, learners develop essential management skills to create, launch, and lead teams while communicating clearly and confidently across organizational levels. Through practical frameworks and real-world scenarios, learners explore what managers actually do, how to align intent with impact, and how to prevent miscommunication in fast-paced work environments. The course emphasizes active listening, structured communication techniques such as I-messages, and strategies for navigating conflict productively. Learners will also practice using reflective journaling, feedback frameworks, and team management tools to strengthen collaboration and performance. By the end of the course, learners will be able to lead teams effectively, manage interpersonal dynamics, and respond to workplace challenges with confidence. This course is ideal for aspiring managers, team leads, and professionals seeking to improve leadership, communication, and conflict management skills.
Foundations of Leadership
Johns Hopkins University (via Coursera)
The course "Foundations of Leadership" explores foundational leadership principles to help you develop the skills and traits necessary for senior technical leadership roles. Through engaging modules, you’ll analyze leadership approaches, including traits and skills-based theories, ethical practices, and modern leadership styles like transformational, servant, and adaptive leadership. You’ll gain critical thinking skills using Paul and Elder’s model, assess your leadership strengths, and learn to navigate ethical dilemmas effectively. What sets this course apart is its focus on practical, self-reflective learning. By bridging theory and practice, you’ll evaluate your leadership characteristics, compare them with peer feedback, and apply leadership theories to real-world challenges. You’ll also explore diverse leadership models, from Path-Goal Theory to Authentic Leadership, tailored to enhance your ability to lead in complex, dynamic environments. By the end, you’ll have a comprehensive understanding of leadership theories, styles, and critical thinking techniques to drive team performance and foster organizational growth.
Operations and Patient Safety for Healthcare IT Staff
Johns Hopkins University (via Coursera)
Now that you've been introduced to the world of Health IT and the important role played by electronic health records (EHRs), we'll focus on other technologies that play a role in maintaining ongoing operations in healthcare. Telemedicine, patient portals, barcode scanners, printers, and medical devices are just some of the technologies that impact providers and patients. As an IT support specialist, you’ll be asked to troubleshoot issues with a wide variety of tools. You'll see a scenario with a medical device installation where issues related to IP addresses, networking, and MAC addresses come up. When there are disruptions in technology, you’ll need to use training, tip sheets, and problem-solving skills to determine how best to handle the situation. Supporting a high reliability organization means being familiar with the existing processes and protocols for handling calls, creating tickets, escalating issues, and resolving matters. We’ll introduce you to the concept of self-service tickets and the guidance given to hospital staff on how to submit a ticket. You’ll learn about the different priority levels for tickets as well as the tiers of IT support. When a call comes in, there are some important resources you’ll need to access in order to troubleshoot the problem. These can range from standard question templates to tip sheets to complex matrices and knowledge base articles (KBAs). Having these tools in your arsenal is essential as a Health IT support specialist. We‘ll also cover the JIRA process, the need for excellent documentation, and ways in which requests for change are communicated.
Executive Data Science Capstone
Johns Hopkins University (via Coursera)
The Executive Data Science Capstone, the specialization’s culminating project, is an opportunity for people who have completed all four EDS courses to apply what they've learned to a real-world scenario developed in collaboration with Zillow, a data-driven online real estate and rental marketplace, and DataCamp, a web-based platform for data science programming. Your task will be to lead a virtual data science team and make key decisions along the way to demonstrate that you have what it takes to shepherd a complex analysis project from start to finish. For the final project, you will prepare and submit a presentation, which will be evaluated and graded by your fellow capstone participants. Course cover image by Luckey_sun. Creative Commons BY-SA https://flic.kr/p/bx1jvU
Data Literacy Capstone – Evaluating Research
Johns Hopkins University (via Coursera)
This is the final course in the Data Literacy Specialization. In this capstone course, you'll apply the skills and knowledge you have acquired in the specialization to the critical evaluation of an original quantitative analysis. The project will first require you to identify and read a piece of high-quality, original, quantitative research on a topic of your choosing. You’ll then interpret and evaluate the findings as well as the methodological approach. As part of the project, you’ll also review other students’ submissions. By the end of the project, you should be empowered to be a critical consumer and user of quantitative research.
The Critical Role of IT Support Staff in Healthcare
Johns Hopkins University (via Coursera)
This is a very exciting time to be exploring a career in Health IT Support! In this introductory course, you’ll learn about various roles in IT support that are common in healthcare. IT support staff play critical roles in many different healthcare venues. In addition to helping clinics, hospitals, and emergency rooms, you may end up providing support in a skilled nursing facility, ambulatory surgical center, virtual care setting, or even a patient’s home! On any given day, you may interact with nurses, physicians, pharmacists, physical therapists, social workers, other allied health professionals, patients or caregivers. Each of these individuals rely on IT support specialists to help them maintain a high reliability healthcare organization. As you learn about what makes Health IT unique, we hope you find inspiration in the stories shared by some of our very own IT support staff at Johns Hopkins. They’ll talk to you about the most common problems they help resolve as well as the complexity and range of issues that arise. Whether you dream of being an end-user computer support specialist, working at a healthcare help desk, or rising to an analyst role, we’re thrilled that you are embarking upon this journey. Welcome again!
Analysis and Interpretation of Large-Scale Programs
Johns Hopkins University (via Coursera)
This course is for implementers, managers, funders, and evaluators of health programs targeting women and children in low- and middle-income countries as well as undergraduate and graduate students in health-related fields. Course participants will learn how to 1) transform quantitative components of an evaluation measurement plan into a sound analysis plan to address the evaluation questions, 2) conduct quantitative analyses of primary or secondary surveys or other available data, 3) interpret the meaning of the analysis results and their implications, and 4) disseminate the evaluation findings to program implementers, local and global stakeholders. It is highly recommended that course participants have the statistical skills to conduct and understand quantitative analysis. The development of this course was supported by a grant from Government Affairs Canada (GAC) for the Real Accountability, Data Analysis for Results (RADAR) project.
Responding, Revising and Assessing Student Writings
Johns Hopkins University (via Coursera)
When writers write, readers respond. Responding to student writing gives teachers one of the most meaningful avenues to help students learn and grow. In this module, learners will identify best practices in effectively responding to student writing. You will also define revision, identify how revision differs from editing, and examine strategies for teaching students how to engage in effective revision. Finally, because writing can also be used for testing, and because all students learn in increments over time, learners will identify a number of strategies for evaluating and assessing student writing, both for individual writings and a student writer’s progress over a period of time. Learners will also consider ways to involve students in both the responding and assessment processes. At the conclusion of this course, learners will have a toolbox full of strategies and practices for these three significant components of any writing class: responding to, revising and assessing student writing.
Decision Making and Ethical Reasoning
Johns Hopkins University (via Coursera)
Leaders must make high-quality and ethical decisions when navigating complex workplace challenges. In this course, learners develop practical tools to analyze ethical issues, recognize common decision errors, and improve judgment in both individual and team settings. Through real-world scenarios, they explore ethical reasoning, values clarification, and how integrity and organizational pressures shape decisions. The course examines cognitive biases, flawed data interpretation, and other common causes of poor decisions, and introduces structured approaches to reduce risk and improve decision quality. Learners also build skills in leading team decision-making processes that align with organizational strategy and values. By the end of the course, learners will be able to assess ethical challenges, anticipate decision risks, and guide teams toward thoughtful, responsible, and effective decisions. This course is ideal for professionals seeking to strengthen ethical leadership, critical thinking, and decision-making capability in modern organizations.
Data Visualization Capstone
Johns Hopkins University (via Coursera)
Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today. One of the critical tools for data visualization today is the R statistical programming language. Especially in conjunction with the tidyverse software packages, R has become an extremely powerful and flexible platform for making figures, tables, and reproducible reports. However, R can be intimidating for first time users, and there are so many resources online that it can be difficult to sort through without guidance. This is the final course in the Specialization "Data Visualization and Dashboarding in R." Learners in this course will enter with a well-developed set of skills making a wide variety of visualizations in R. The focus on this course will applying those skills to a unique project, drawing on publicly available data to tell a compelling story using the data visualization toolkit assembled in the previous courses.
Program Design & Evaluation for Health Systems Strengthening
Johns Hopkins University (via Coursera)
This course provides an introduction to designing and evaluating to strengthen the health system. After successful completion of all learning activities, course participants will be able to: 1. Define health systems strengthening 2. Describe health systems frameworks and how to incorporate them into evaluation planning 3. Describe how to design and prioritize implementation of health systems programs 4. Detail approaches to evaluate health systems programs, including data sources and study design 5. Introduce tools to assist with evaluation planning and simple models to assess health system intervention impact 6. Describe how to interpret health system evaluation findings and sustainability and scalability implications The development of this course was supported by a grant from Government Affairs Canada (GAC) for the Real Accountability, Data Analysis for Results (RADAR) project.
Evidence-based Toxicology
Johns Hopkins University (via Coursera)
Welcome to the Evidence-based Toxicology (EBT) course. In medicine and healthcare, evidence-based medicine has revolutionized the way that information is evaluated transparently and objectively. Over the past ten years, a movement in North America and Europe has attempted to translate this revolution to the field of toxicology. The Center for Alternatives to Animal Testing (CAAT) within the department of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health hosts the first chair for EBT and the secretariat for the EBT Collaboration on both sides of the Atlantic. Based on the Cochrane Collaboration in Evidence-based Medicine, the EBT Collaboration was established at the CAAT to foster the development of a process for quality assurance of new toxicity tests for the assessment of safety in humans and the environment. Regulatory safety sciences have undergone remarkably little change in the past fifty years. At the same time, our knowledge in the life sciences is doubling about every seven years. Systematic review and related evidence-based approaches are beginning to be adapted by regulatory agencies like the Environment Protection Agency (EPA), the European Food Safety Authority (EFSA), and the US National Toxicology Program. They provide transparent, objective, and consistent tools to identify, select, appraise, and extract evidence across studies. This course will showcase these emerging efforts and address opportunities and challenges to the expanded use of these tools within toxicology.
R 프로그래밍
Johns Hopkins University (via Coursera)
이 과정에서는 R로 프로그래밍하는 방법과 효과적인 데이터 분석을 위해 R을 사용하는 방법을 배웁니다. 통계 프로그래밍 환경에 필요한 소프트웨어를 설치 및 구성하는 방법과 고급 통계 언어로 구현되는 일반적인 프로그래밍 언어 개념을 설명합니다. 이 과정은 R 프로그래밍, R로 데이터 읽기, R 패키지 액세스, R 함수 작성, 디버깅, R 코드 프로파일링, R 코드 구성 및 주석 달기를 포함하는 통계 컴퓨팅의 실제 문제를 다룹니다. 통계 데이터 분석의 주제는 실제 사례를 제공합니다.
Opioid Epidemic: From Evidence to Impact
Johns Hopkins University (via Coursera)
While prescription opioids serve an invaluable role for the treatment of cancer pain and pain at the end of life, their overuse for acute and chronic non-cancer pain as well as the increasing availability of heroin and illicit fentanyl, have contributed to the highest rates of overdose and opioid addiction in U.S. history. Evidence-informed solutions are urgently needed to address these issues and to promote high-quality care for those with pain. This course and the report it is based on are a response to that need. They offer timely information and a path forward for all who are committed to addressing injuries and deaths associated with opioids in the United States.
Clinical Trials Analysis, Monitoring, and Presentation
Johns Hopkins University (via Coursera)
In this course, you’ll learn more advanced operational skills that you and your team need to run a successful clinical trial. You’ll learn about the computation of sample size and how to develop a sample size calculation that’s suitable for your trial design and outcome measures. You’ll also learn to use statistical methods to monitor your trial for safety, integrity, and efficacy. Next, you’ll learn how to report the results from your clinical trials through both journal articles and data monitoring reports. Finally, we’ll discuss the role of the analyst throughout the trial process, plus a few additional topics such as simulations and adaptive designs.
Effective Presentations and Persuasive Writing
Johns Hopkins University (via Coursera)
The course "Effective Presentations and Persuasive Writing" equips learners with the tools to craft and deliver impactful technical presentations and develop persuasive communication skills. Through this course, students will master the art of designing clear, engaging slides and structuring persuasive arguments tailored to diverse audiences. Learners will also gain insights into the intricacies of Requests for Proposals (RFPs) and related documents, sharpening their ability to analyze competitive strategies and write winning proposals. What sets this course apart is its focus on real-world applications, from understanding team communication dynamics and cultural diversity to developing elevator pitches and mastering human-centered design principles. Students will also explore the latest trends in communication, ensuring they stay ahead in an ever-evolving professional landscape. By the end of the course, participants will be able to confidently present complex ideas, influence audiences through well-constructed arguments, and continuously enhance their communication skills throughout their careers. This course is ideal for professionals looking to boost their presentation and writing abilities in technical and persuasive contexts.
Systems Thinking In Public Health
Johns Hopkins University (via Coursera)
This course provides an introduction to systems thinking and systems models in public health. Problems in public health and health policy tend to be complex with many actors, institutions and risk factors involved. If an outcome depends on many interacting and adaptive parts and actors the outcome cannot be analyzed or predicted with traditional statistical methods. Systems thinking is a core skill in public health and helps health policymakers build programs and policies that are aware of and prepared for unintended consequences. An important part of systems thinking is the practice to integrate multiple perspectives and synthesize them into a framework or model that can describe and predict the various ways in which a system might react to policy change. Systems thinking and systems models devise strategies to account for real world complexities. This work was coordinated by the Alliance for Health Policy and Systems Research, the World Health Organization, with the aid of a grant from the International Development Research Centre, Ottawa, Canada. Additional support was provided by the Department for International Development (DFID) through a grant (PO5467) to Future Health Systems research consortium. © World Health Organization 2014 All rights reserved. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names ...
Online Influence and Persuasion
Johns Hopkins University (via Coursera)
In the "Online Influence and Persuasion" course, learners will explore the intricate dynamics of social media through the lens of Social Network Analysis (SNA). This course is designed to equip you with essential skills to analyze how social media influences behaviors, perceptions, and organizational structures. By mastering key SNA measures and clustering techniques, you will uncover valuable insights into network subgroups and social forces. What sets this course apart is its comprehensive approach to understanding online influence, including the neurobiological aspects of social media addiction and the interplay between misinformation and persuasion. You will gain hands-on experience managing social media data through APIs, enabling you to extract, transform, and analyze data effectively. By the end of this course, you will not only understand the theoretical foundations of online influence and persuasion but also acquire practical skills that can be applied in various contexts, from marketing to behavioral research. This unique combination of theory and practice will prepare you to navigate the complexities of the digital landscape and make data-driven decisions that can enhance organizational effectiveness.
Mastering Software Development in R Capstone
Johns Hopkins University (via Coursera)
R Programming Capstone
Introduction to Uncertainty Quantification
Johns Hopkins University (via Coursera)
Uncertainty Quantification (UQ) is the science of mathematically quantifying and reducing uncertainty in systems of all types. Students will learn the nature and role of uncertainty in physical, mathematical, and engineering systems along with the basics of probability theory necessary to quantify uncertainty. The course provides an introduction to various sub-topics of UQ including uncertainty propagation, surrogate modeling, reliability analysis, random processes and random fields, and Bayesian inverse UQ methods.
Foundational Mathematics for AI
Johns Hopkins University (via Coursera)
This course offers a comprehensive introduction to the mathematical principles that form the foundation of artificial intelligence and machine learning. Designed for learners with a variety of academic backgrounds, the course bridges essential mathematical concepts with real-world AI applications, empowering students to understand and implement mathematical techniques critical for AI development. By the end of this course, learners will be able to apply functions, matrices, and vectors to represent and analyze data relationships. Students will be able to use descriptive statistics and visualization techniques to explore and summarize datasets, solve systems of linear equations and model complex relationships using linear regression of single and multiple variables, and understand and implement foundational principles of probability, including Bayes' Theorem. The course builds to advanced mathematical techniques in Calculus, and develops derivatives and integrals to analyze rates of change and distributions, essential for optimization and modeling in AI. Concepts from Linear Algebra are used to explore advanced concepts like eigenvectors, determinants, and linear transformations for dimensionality reduction and classification algorithms. This course is specifically tailored for aspiring AI practitioners. Unlike traditional math courses, this curriculum focuses on mathematical techniques directly applicable to artificial intelligence and machine learning, bridging theory with practice. Through interactive modules, real-world datasets, and tools like Python and Excel, you’ll not only understand the concepts but also apply them to solve practical problems. With clearly defined modules such as Descriptive Statistics, Linear Algebra, Probability, and Optimization, this course allows you to build knowledge progressively while connecting each concept to AI use cases. Each topic is introduced with AI-related examples, like using linear regression to model salaries or a...
The Data Science of Health Informatics
Johns Hopkins University (via Coursera)
Health data are notable for how many types there are, how complex they are, and how serious it is to get them straight. These data are used for treatment of the patient from whom they derive, but also for other uses. Examples of such secondary use of health data include population health (e.g., who requires more attention), research (e.g., which drug is more effective in practice), quality (e.g., is the institution meeting benchmarks), and translational research (e.g., are new technologies being applied appropriately). By the end of this course, students will recognize the different types of health and healthcare data, will articulate a coherent and complete question, will interpret queries designed for secondary use of EHR data, and will interpret the results of those queries.
Statistical Inference
Johns Hopkins University (via Coursera)
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
Calculus through Data & Modeling: Applying Differentiation
Johns Hopkins University (via Coursera)
As rates of change, derivatives give us information about the shape of a graph. In this course, we will apply the derivative to find linear approximations for single-variable and multi-variable functions. This gives us a straightforward way to estimate functions that may be complicated or difficult to evaluate. We will also use the derivative to locate the maximum and minimum values of a function. These optimization techniques are important for all fields, including the natural sciences and data analysis. The topics in this course lend themselves to many real-world applications, such as machine learning, minimizing costs or maximizing profits.
Neuroscience Methods
Johns Hopkins University (via Coursera)
The course "Neuroscience Methods" provides hands-on experience with cutting-edge neuroscience methods, equipping you to explore how the brain supports perception, attention, memory, and emotion. You'll gain proficiency in using tools such as neuroimaging, biometric systems, psycho-physiological sensors, and eye trackers to collect and analyze complex datasets. Learn to interpret data through advanced neural imaging and physiological measurement techniques, and critically assess the strengths and limitations of different methods. With a unique combination of theory and practice, this course empowers you to design robust research studies and make informed decisions about measurement tools. By mastering techniques like functional near-infrared spectroscopy (fNIRS) and eye-tracking analysis, you'll uncover valuable insights into cognitive and emotional processes. Whether you're a postgraduate student or researcher, this course will deepen your understanding of neuroscience tools and their applications, preparing you for innovative work in psychological and health-related fields.
Data Science Decisions in Time: Information Theory & Games
Johns Hopkins University (via Coursera)
This is part of our specialization on Making Decision in Time. For this third course we start with an intriguing study on SFPark and build new insights into the ideas that flow from this direction. The ending point should bring new code and new algorithm insights into perspective, and use, by many computer and data scientists.
مجموعة أدوات عالم البيانات
Johns Hopkins University (via Coursera)
ستتلقى في هذه الدورة التدريبية مقدمة عن الأدوات الرئيسية والأفكار الخاصة بمجموعة أدوات عالم البيانات. تقدم الدورة التدريبية نظرة عامة عن البيانات والاستفسارات والأدوات التي يعمل عليها علماء البيانات ومحللو البيانات. هناك عنصران لهذه الدورة التدريبية. الأول هو مقدمة نظرية عن الأفكار الكامنة وراء تحويل البيانات إلى معلومات قابلة للتطبيق. والثاني هو مقدمة عملية عن الأدوات التي سيتم استخدامها في البرنامج مثل التحكم في النُسَخ ولغة Markdown وGit وGitHub وR وRStudio.
R Programming
Johns Hopkins University (via Coursera)
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Household Surveys for Program Evaluation in LMICs
Johns Hopkins University (via Coursera)
This course provides an introduction to household surveys for program evaluation in low-and middle-income countries. The course will equip you with skills to: 1. Explain what coverage is, why it’s important in evaluations, and how it is measured 2. Describe what household surveys can and cannot measure 3. Plan, implement, and analyze household survey, including: 4. Calculate an appropriate household survey sample size 5. Explain the resources required for a household survey 6. Identify an appropriate sampling design 7. Design a questionnaire 8. Describe challenges for coverage survey planning and implementation 9. Present and describe how the RADAR tool can be used The development of this course was supported by a grant from Government Affairs Canada (GAC) for the Real Accountability, Data Analysis for Results (RADAR) project.
Social Network Analysis
Johns Hopkins University (via Coursera)
The "Social Network Analysis" course offers a comprehensive exploration of the intricate relationships within social networks, emphasizing the theoretical and practical applications of network analysis. Through engaging modules, learners will delve into advanced topics in graph theory, centrality measures, and statistical modeling, equipping them with the skills to analyze and interpret social structures effectively. By completing this course, learners will gain a solid understanding of how to identify key influencers, measure network cohesion, and conduct hypothesis testing using empirical data. What sets this course apart is its blend of theoretical foundations and hands-on experience using R programming for network analysis, specifically with tools like 'statnet' and 'RSiena.' Whether you’re looking to enhance your skills in data analysis or seeking to understand the dynamics of social behavior, this course will serve as a vital resource. With a focus on real-world applications, learners will emerge equipped to tackle complex social phenomena, making significant contributions to their fields.
Revenue Cycle, Billing, and Coding
Johns Hopkins University (via Coursera)
Welcome to the Revenue Cycle, Billing, and Coding in Ambulatory Healthcare Management course! In this course, you will explore the essential aspects of revenue cycle management in ambulatory healthcare settings. Designed for those with little to no background in healthcare, this course provides you with a comprehensive understanding of the revenue cycle process, including registration, insurance, billing and coding basics, denials management, collections, and price transparency. By the end of this course, you will have a comprehensive understanding of the revenue cycle billing and coding process in ambulatory healthcare management. Start your journey to acquire the knowledge and skills needed to navigate the complexities of registration, insurance, billing and coding, denials management, collections, and price transparency in the revenue cycle.
Essential Epidemiologic Tools for Public Health Practice
Johns Hopkins University (via Coursera)
In order to make a difference in the health and well-being of a population, we must understand the burden of all problems and conditions that affect the population, as well as how well our efforts to mitigate these problems are actually working. This course provides you with some essential skills and tools that will enhance your ability to describe and understand the health of your community. The tools that epidemiologists use are in fact useful for all public health practitioners, including data scientists, program officials, agency leaders, and policymakers. Whether you are deeply enmeshed in your career and looking to augment your skills, or are looking to change career paths into the field of public health, this course will give you some of the practical knowledge and skills that we hope you can apply in your professional endeavors.
Sustainable Regional Principles, Planning and Transportation
Johns Hopkins University (via Coursera)
This course will provide students with an introduction to sustainable regional principles, regional planning concepts and evaluate regional transportation system issues. This will be achieved through dynamic video lectures, practical case studies and the evaluation of practices for success. These will include discussions of the importance of the regional plan, how to engage community involvement, the importance of understanding a development transect and others. Strategies for growth priorities, along with consideration of available housing and food security will also be addressed. The concepts of providing a rural preserve and a rural reserve will be evaluated and mapping of key aspects of the region, such as neighborhoods and districts will be explained. Evaluation of regional transportation systems will be addressed including multi-mobile balance and building choices into the transportation system modes. The considerations for including a regional railway system along with accommodating user mobility and accessibility will be explored. By the end of this course, you will be able to: Formulate the relationship between regional principles, smart growth, and sustainability. Evaluate growth priorities, community involvement and scale of governance to achieve sustainable smart growth. Evaluate and explain regional mapping of rural preserves, rural reserves, neighborhoods, and districts as important tools in regional smart growth planning. Describe the implications of mapping corridors and regional centers to the overall smart growth planning at the regional level. This course is for : Government Officials involved planning, designing, monitoring, enforcement, and assessment of sustainable project developments at the local, state, and federal level. Private sector companies in the transportation and municipal design and construction business. Architects interested in advancing sustainable concepts for cities and communities. Foundations, asso...
An Introduction to the U.S. Food System: Perspectives from Public Health
Johns Hopkins University (via Coursera)
A food system encompasses the activities, people and resources involved in getting food from field to plate. Along the way, it intersects with aspects of public health, equity and the environment. In this course, we will provide a brief introduction to the U.S. food system and how food production practices and what we choose to eat impacts the world in which we live. We will discuss some key historical and political factors that have helped shape the current food system and consider alternative approaches from farm to fork. The course will be led by a team of faculty and staff from the Johns Hopkins Center for a Livable Future. Guest lecturers will include experts from a variety of disciplines, including public health, policy and agriculture.
Nouvelles approches pour mesurer la santé de la population
Johns Hopkins University (via Coursera)
Dans ce cours, vous apprendrez les techniques traditionnelles et nouvelles de collecte de données au niveau de la population qui peuvent être utilisées pour renforcer les programmes et les politiques de santé publique. Des experts du monde entier définiront et expliqueront les concepts clés de la conception et de la mise en œuvre d’enquêtes basées sur la population, en mettant l’accent sur l’utilisation de nouvelles techniques d’enquête telles que les enquêtes par téléphone portable et en ligne, les données sur les services de santé et les systèmes d’information sur la santé, ainsi que les registres de santé basés sur la population. Vous apprendrez comment ces données peuvent être utilisées pour éclairer la prise de décision au niveau de la population, et comment appliquer une perspective de genre et d’équité pour s’assurer qu’ils répondent aux besoins des populations. Les objectifs primordiaux du cours sont de soutenir la collecte de données sanitaires au niveau de la population, de suivre les tendances, de programmer les interventions et d’améliorer le suivi des principaux facteurs de risque de décès prématuré - en particulier pour les maladies non transmissibles. Le cours est le fruit d’une collaboration entre de multiples partenaires, dont Vital Strategies, des Centres de contrôle et de prévention des maladies (CDC), la Johns Hopkins Bloomberg School of Public, le ministère de la Santé du Rwanda, les Nations Unies, l’Université de Colombo au Sri Lanka et l’Organisation mondiale de la Santé. Ce cours a été financé par Bloomberg Philanthropies et co-financé par le gouvernement australien et la Fondation Bill et Melinda Gates.
Infectious Disease Modeling in Practice
Johns Hopkins University (via Coursera)
Mathematical modeling is an increasingly widespread tool for understanding infectious disease transmission and informing public health decision making. This course provides an in depth overview of the practical uses of mathematical modeling for a range of diseases and scenarios. We cover the basic principles of infectious disease models, how models are adapted to be specific to the disease and question of interest, and important assumptions of different models. We will discuss how data informs models, how models can be used to interpret data, uncertainty in model results, and pros and cons of different modeling and analytic approaches. In each module we focus on a use case for models: evaluating potential control strategies, quantifying and comparing the transmission rate of infections, and for forecasting future disease burden. Learners will get the most out of this course if they are familiar with the basics of infectious disease epidemiology and some university level mathematics background. Those who are interested in a more basic overview of model uses in public health decision-making without the requirement for any mathematics background are recommended to view our companion course, Infectious Disease Transmission Models for Decision Makers. Learners who have a stronger quantitative background and are interested in learning to construct models themselves are suggested to consider the Infectious Disease Modeling Specialization developed by Imperial College London.
Strategy and Implementation
Johns Hopkins University (via Coursera)
Successful organizations turn strategy into action through clear priorities and disciplined execution. In this course, learners build practical skills to align daily work with organizational strategy and deliver projects effectively from start to finish. Learners explore core strategy frameworks such as Porter’s Five Forces and SWOT analysis to assess competitive environments and internal capabilities. The course also covers priority and time management techniques to optimize focus, manage workload, and reduce distractions. Learners will use tools such as goal-setting frameworks (SMART goals), project planning templates, risk assessment methods, and task management systems to guide execution. By the end of the course, learners will be able to translate strategy into actionable plans, prioritize high-impact work, and manage projects across initiation, planning, execution, and closing stages. This course is ideal for professionals seeking to strengthen strategic thinking, project management, and execution skills in dynamic business environments.
Getting Started with Data Visualization in R
Johns Hopkins University (via Coursera)
Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today. One of the critical tools for data visualization today is the R statistical programming language. Especially in conjunction with the tidyverse software packages, R has become an extremely powerful and flexible platform for making figures, tables, and reproducible reports. However, R can be intimidating for first time users, and there are so many resources online that it can be difficult to sort through without guidance. To fill that need, this course is intended for learners who have little or no experience with R but who are looking for an introduction to this tool. By the end of this course, students will be able to import data into R, manipulate that data using tools from the popular tidyverse package, and make simple reports using R Markdown. The course is designed for students with good basic computing skills, but limited if any experience with programming.
Foundations of Strategic Communications
Johns Hopkins University (via Coursera)
The "Foundations of Strategic Communications" course equips learners with the skills to master professional, technical, and leadership communication strategies. By completing this course, participants will be able to analyze audiences, craft effective messages, and adapt their communication style to fit organizational cultures. Key learning outcomes include enhancing emotional intelligence, improving listening and nonverbal communication, and overcoming common communication barriers. Learners will also develop leadership communication skills, enabling them to influence and foster collaboration in both team settings and external engagements. What makes this course unique is its practical focus on applying communication models in real-world scenarios. Learners will gain the confidence to manage diverse audiences and complex workplace dynamics. The course emphasizes tailoring communication strategies based on audience analysis, ensuring learners are prepared for a variety of professional environments. By the end of the course, participants will have the tools to communicate persuasively, lead with influence, and foster stronger workplace relationships. This course provides a competitive advantage in both career growth and personal development by empowering learners with the ability to strategically communicate in any setting.
Precalculus: Periodic Functions
Johns Hopkins University (via Coursera)
This course helps to build the foundational material to use mathematics as a tool to model, understand, and interpret the world around us. This is done through studying functions, their properties, and applications to data analysis. Concepts of precalculus provide the set of tools for the beginning student to begin their scientific career, preparing them for future science and calculus courses. This course is designed for all students, not just those interested in further mathematics courses. Students interested in the natural sciences, computer sciences, psychology, sociology, or similar will genuinely benefit from this introductory course, applying the skills learned to their discipline to analyze and interpret their subject material. Students will be presented with not only new ideas, but also new applications of an old subject. Real-life data, exercise sets, and regular assessments help to motivate and reinforce the content in this course, leading to learning and mastery.
의료 서비스를 위한 시스템공학의 기초
Johns Hopkins University (via Coursera)
다양한 영상 강좌를 통해 실무에 활용할 수 있는 방법을 알아보면서, 의료 서비스를 위한 시스템공학의 기초를 배웁니다. 이 강좌를 통해 현재 의료 서비스 공급의 개선을 이끌어 낼 수 있는 동기화되고 효율적인 통합 의료 서비스 시스템이 없다는 걸 알게 될 것입니다.또한 다양한 시스템 유형을 살펴보고, 이런 시스템 유형이 어떻게 의료 서비스 분야에서 적절한 시스템공학 프로세스로 적용되는지 예시를 통해 살펴봅니다. 시스템 유형을 구체적으로 묘사하고 서술하면서, 시스템공학 접근법을 통해 다음과 같은 프로세스를 소개합니다.1) 의료 서비스 문제점, 필요 사항, 요건을 파악하고 발전시키기, 2) 요건에서 비롯된 시스템 개념을 전개하고 물리적인 형식으로 실현하기, 3) 마지막으로 필요 사항과 요건을 충족하는 의료 서비스 시스템을 확인하고 검증한 후 배포하는 방법 수립하기 활용 사례와 예시를 알아봅니다.
Surveillance Systems: Analysis, Dissemination, and Special Systems
Johns Hopkins University (via Coursera)
In this course, we'll build on the previous lessons in this specialization to focus on some very specific skills related to public health surveillance. We'll learn how to get the most out of surveillance data analysis, focusing specifically on interpreting time trend data to detect temporal aberrations as well as person, place, and time in the context of surveillance data. We'll also explore strategies for the presentation of surveillance data and some of the complex legal elements that affect its use. We'll then turn our attention to surveillance of non-communicable chronic diseases and how the data can be used to support prevention efforts. Finally, we'll explore special surveillance systems, such as syndromic surveillance, antimicrobial resistance, and event-related surveillance. This course is designed for public health practitioners with a focus on those working on health surveillance in municipal, regional, state, provincial, or even national public health agencies. We really think that this course will help those with an interest in health surveillance to see which approaches are used in actual practice of public health.
Family Spirit Nurture
Johns Hopkins University (via Coursera)
This course is designed for health educators and home visitors serving families with infants 0-6 months old. Learners will gain knowledge and skills to make a positive impact on healthy infant nutrition and growth as well as maternal and family nutrition. This course is uniquely tailored towards Indigenous families and approaches nutrition through a strengths-based lens connecting to Indigenous foods and Native Foodways.
Coding the Static Restaurant Site
Johns Hopkins University (via Coursera)
Do you realize that the only functionality of a web application that the user directly interacts with is through the web page? Implement it poorly and, to the user, the server-side becomes irrelevant! Today’s user expects a lot out of the web page: it has to load fast, expose the desired service, and be comfortable to view on all devices: from a desktop computers to tablets and mobile phones. This course offers a step-by-step guide to building a fully functional and responsive website for a real client. Starting from understanding client requirements, the course shows all aspects of website development. It covers topics from lessons on coding a website header and navigation menu, crafting a responsive homepage and menu page using Bootstrap and CSS to resolving potential website resize issues for a seamless mobile experience. The course emphasizes hands-on learning, guiding learners in creating a restaurant website. Through building the restaurant website, learners will gain expertise in requirements gathering, website design and development, responsive web design, and ensuring a visually appealing and user-friendly website that works across all devices.
Cours intensif sur la science des données
Johns Hopkins University (via Coursera)
Vous avez sûrement déjà entendu parler de la science des données et du Big Data. Ce cours intensif d’une semaine vous permettra de comprendre ce que ces termes signifient et comment ils jouent un rôle dans les entreprises prospères. Ce cours intensif s’adresse à tous ceux qui souhaitent découvrir en quoi consiste la science des données, y compris ceux qui devront éventuellement encadrer des scientifiques des données. L’objectif est de vous familiariser avec la science des données le plus rapidement possible et de ne pas s’encombrer du superflu. Nous avons conçu ce cours afin qu’il soit aussi pratique que possible, sans pour autant sacrifier les éléments essentiels. Il s'agit d’un cours ciblé conçue pour vous familiariser rapidement avec le domaine de la science des données. Notre objectif est de faire en sorte que cette formation soit aussi pratique que possible pour vous, sans pour autant sacrifier le contenu essentiel. Nous avons laissé les informations techniques de côté afin que vous puissiez vous concentrer sur l'encadrement et la progression de votre équipe. Après avoir terminé ce cours, vous saurez. 1. Décrire le rôle joué par la science des données dans différents contextes 2. Comment les statistiques, l’apprentissage automatique et le génie logiciel jouent un rôle dans la science des données 3. Comment décrire la structure d’un projet de science des données 4. Les termes et outils clés utilisés par les scientifiques des données 5. Comment identifier si un projet de science des données est réussi ou non 3. Le rôle d’un manager en science des données Image de couverture du cours par r2hox. Creative Commons BY-SA : https://flic.kr/p/gdMuhT
Psychological First Aid
Johns Hopkins University (via Coursera)
Learn to provide psychological first aid to people in an emergency by employing the RAPID model: Reflective listening, Assessment of needs, Prioritization, Intervention, and Disposition. Utilizing the RAPID model (Reflective listening, Assessment of needs, Prioritization, Intervention, and Disposition), this specialized course provides perspectives on injuries and trauma that are beyond those physical in nature. The RAPID model is readily applicable to public health settings, the workplace, the military, faith-based organizations, mass disaster venues, and even the demands of more commonplace critical events, e.g., dealing with the psychological aftermath of accidents, robberies, suicide, homicide, or community violence. In addition, the RAPID model has been found effective in promoting personal and community resilience. Participants will increase their abilities to: - Discuss key concepts related to PFA - Listen reflectively - Differentiate benign, non-incapacitating psychological/ behavioral crisis reactions from more severe, potentially incapacitating, crisis reactions - Prioritize (triage) psychological/ behavioral crisis reactions - Mitigate acute distress and dysfunction, as appropriate - Recognize when to facilitate access to further mental health support - Practice self-care Developed in collaboration with Johns Hopkins Open Education Lab.
Mathematical Biostatistics Boot Camp 2
Johns Hopkins University (via Coursera)
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
Specialized Data Visualization Approaches
Johns Hopkins University (via Coursera)
The course "Specialized Data Visualization Approaches" explores advanced data visualization techniques, equipping you with the skills to tackle complex data challenges. You’ll learn how to visualize temporal, volumetric, and multidimensional data, leveraging specialized methods such as direct volume rendering, isosurfaces, and flow visualization. With a focus on scientific accuracy and practical applications, you'll gain the tools to represent data over time and visualize 3D datasets for fields like engineering and medicine. By applying these techniques, you’ll enhance your ability to create insightful and impactful visualizations for various domains. What sets this course apart is its emphasis on evaluating and validating visualizations for effectiveness, ensuring that your visual outputs are not only technically advanced but also user-friendly and accurate. Whether you’re working in research, engineering, or any field that requires advanced data analysis, this course will help you build the expertise to communicate complex insights with clarity and precision.
Advanced R Programming
Johns Hopkins University (via Coursera)
This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization’s mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.
Rastreo de los contactos de la COVID-19
Johns Hopkins University (via Coursera)
La crisis de la COVID-19 ha creado una necesidad sin precedentes de rastreo de los contactos en todo el país, y exige que miles de personas aprendan habilidades nuevas con rapidez. La certificación laboral para puestos de rastreo de contactos es distinta en todo el país y el mundo, y algunos puestos nuevos se abren para personas que tienen diploma de escuela secundaria o un equivalente. En este curso introductorio, los estudiantes aprenderán acerca de la ciencia del SARS-CoV-2, que incluye el período de contagio, el cuadro clínico inicial de la COVID-19, y las pruebas de cómo el SARS-CoV-2 se contagia de una persona a otra y por qué el rastreo de contactos puede ser una intervención tan efectiva de salud pública. Los estudiantes aprenderán cómo se realiza el rastreo de los contactos, incluso cómo establecer una buena relación con los casos, identificar sus contactos y brindar apoyo a los casos y a los contactos para detener el contagio en sus comunidades. El curso también abarcará varias consideraciones importantes de ética acerca del rastreo de contactos, del aislamiento y de la cuarentena. Por último, en el curso se identificarán algunos de los obstáculos que se encuentran con más frecuencia para los esfuerzos de rastreo de los contactos, junto con estrategias para superar esos obstáculos.
Introduction to Genomic Technologies
Johns Hopkins University (via Coursera)
This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed. This is the first course in the Genomic Data Science Specialization.
Data Analysis Using Hadoop Tools
Johns Hopkins University (via Coursera)
The course "Data Analysis Using Hadoop Tools" provides a thorough and hands-on introduction to key tools within the Hadoop ecosystem, such as Hive, Pig, HBase, and Apache Spark, for data processing, management, and analysis. Learners will gain practical experience with Hive's SQL-like interface for complex data querying, Pig Latin scripting for data transformation, and HBase's NoSQL capabilities for efficient big data management. The course also covers Apache Spark's powerful in-memory computation capabilities for high-performance data processing tasks. By the end, participants will be equipped with the skills to leverage these technologies within the Hadoop platform to address real-world big data challenges. What makes this course unique is its comprehensive approach to integrating various Hadoop tools into a cohesive workflow. You'll not only learn how to use each tool individually but also understand how to effectively combine them to optimize data processing and analysis. Through hands-on exercises and examples, you'll gain the confidence and skills to tackle complex data challenges and extract valuable insights from big data. Whether you're looking to enhance your data analysis capabilities for work or want to deepen your knowledge of Hadoop and big data tools, this course offers valuable skills that will help you succeed.
Advanced Business Analytics: Excel Optimization & Simulation
Johns Hopkins University (via Coursera)
This course equips learners with advanced skills in building and analyzing models using Excel to solve real-world business problems. Participants will learn to optimize decision-making processes using Solver, conduct sensitivity analysis to refine model outcomes, and apply advanced integer programming techniques to tackle complex scenarios. The course also covers sophisticated methods for solving assignment and transportation problems and introduces simulation techniques to analyze uncertainty and variability in decision-making. By the end of this course, learners will have the tools to enhance efficiency, improve resource allocation, and drive strategic decisions using Excel’s powerful modeling and analytical capabilities. What sets this course apart is its focus on actionable insights and practical, hands-on applications of advanced techniques, ensuring students are prepared to address challenges across a range of industries. Whether optimizing operations, solving logistical challenges, or preparing for uncertainty, this course provides essential skills for making confident, data-driven decisions. Prerequisite knowledge of basic Excel functions and foundational analytics is recommended.
Helping Families Avoid Negative Court Involvement Teach-Out
Johns Hopkins University (via Coursera)
This course will teach learners how to understand when issues can become legal issues, encourage learners to recognize legal problems early, identify possible “legal soft spots” in their own situations, and act when issues are identified to avoid them escalating. This course will also teach and encourage learners to think preventatively and provide some tools to use with an eye toward preventing future legal issues. Finally, this Teach-out seeks to expand the idea of legal health checks to a community level by encouraging learners to take on the task of informing others of legal soft spots and prevention tools to prevent and mitigate future legal problems. This Teach-out is for anyone in a romantic relationship, co-parenting or parenting relationship, or people who work with families and young and emerging adults. Examples of professionals who may benefit from this course include: teachers, pastors and other church leaders, community organizers, social workers, doctors, nurses, and hairdressers.
CUDA Advanced Libraries
Johns Hopkins University (via Coursera)
This course will complete the GPU specialization, focusing on the leading libraries distributed as part of the CUDA Toolkit. Students will learn how to use CuFFT, and linear algebra libraries to perform complex mathematical computations. The Thrust library’s capabilities in representing common data structures and associated algorithms will be introduced. Using cuDNN and cuTensor they will be able to develop machine learning applications that help with object detection, human language translation and image classification.
Excellence in Online Teaching
Johns Hopkins University (via Coursera)
This course is aimed at anyone who is teaching online or will be in the future. Learners will come away with ways to improve their online courses and teaching practices today.
Artificial Intelligence in Social Media Analytics
Johns Hopkins University (via Coursera)
In the course "Artificial Intelligence in Social Media Analytics", learners will explore the intersection of artificial intelligence and social media analytics, equipping them with essential skills to navigate and analyze digital landscapes. By delving into machine learning fundamentals, natural language processing, sentiment analysis, and topic modeling, participants will gain practical experience in applying AI techniques to real-world social media data. This course stands out by providing not only theoretical insights but also hands-on opportunities to construct classifiers, perform sentiment analysis, and build semantic networks, all tailored to the complexities of social media content. As learners progress, they will develop a keen understanding of how AI can uncover hidden patterns, sentiment, and topics within vast amounts of unstructured data. The unique blend of foundational concepts and practical applications ensures that participants can effectively analyze social media interactions and derive actionable insights. Whether for career advancement or personal interest, this course offers a comprehensive toolkit to leverage AI for understanding social dynamics and enhancing engagement strategies in digital platforms.
Public Health in Humanitarian Crises 2
Johns Hopkins University (via Coursera)
This course, Public Health in Humanitarian Crises 2, addresses public health issues of people affected by disasters, both natural or conflict-related. It discusses the many changes that occur in people’s lives when they are uprooted by a disaster, including many important topics related to humanitarian crises, such as when there is an epidemic a public health emergency; what do we mean with the humanitarian development nexus; what are the basics of disaster epidemiology and surveillance; humanitarian principles; and other very relevant topics. We will explore what humanitarian interventions could look like if we want to mitigate the effects of disasters. This course is a follow-up to Public Health in Humanitarian Crises 1, which dealt with changes in disease patterns, access to health care, livelihoods, shelter, sanitary conditions, nutritional status, and other issues. The course content is a mix of theoretical knowledge and many practical examples from recent disasters. We think this course is unique because it contains so many practical ‘real-life’ examples and is taught be instructors and guest lecturers who together have over 200 years of experience in this field. The course consists of 10 modules totaling approximately 14-16 hours of delivered content with an additional 2-3 hours of self-work (quizzes and writing and evaluating a short peer-review assignment) as well as lively discussions forums. The first course, Public Health in Humanitarian Crises 1 (PHHC1), has been designed in a way that each module builds on the lessons of previous modules. However, the modules from this second course, Public Health in Humanitarian Crises 2 (PHHC2) can be accessed in any order and some can stand alone. You do not necessarily need to do PHHC1 before PHHC2, but it might be helpful to take some or all of PHHC1, as some of the basic or fundamental issues are covered in this course. PHHC2 contains a somewhat more diverse set of topics than the previous course as it deals...
Let's talk about it: A Health and Immigration Teach Out
Johns Hopkins University (via Coursera)
As the United States grapples with both the short-term and long-term impacts and considers how different groups and individuals are affected, it can easily become overwhelming to keep up with conversations about health and immigration. Most people can generally agree that health and immigration policy affect ALL communities, families, and individuals in one way or another. This Teach-Out is designed to help you have a more well-informed discussion about health and immigration. As part of this Teach-Out, you will hear the first-hand perspectives of researchers, elected officials, healthcare systems, business leaders, and community members. You will get a sneak peek into safe spaces within the immigrant community. Along the way, you will also get to test yourself and your knowledge about immigration and health, and do a fact-check with helpful resources and information. Last but not least, you will share in the spirit of a Teach-Out: bringing people together to learn about and address a current and important topic in society. At the end of each lesson, you will be asked to practice how to talk about it by sharing your thoughts and reflections on the different topics that were discussed.
Taking Safety and Quality Improvement Work to the Next Level (Patient Safety VII)
Johns Hopkins University (via Coursera)
In this culminating course in the Patient Safety and Quality Improvement Specialization, you will apply the skills you have acquired across the previous six courses to address a realistic patient safety issue confronting Mercy Grace, a 500-bed urban hospital that is part of a larger hospital system. Based on the scenario provided, you will assess the situation and work through the problem using a variety of tools and strategies. You will have the opportunity to identify defects, root causes, and potential mitigation strategies; you will create a project implementation plan for addressing the issue in the form of an A3; you will identify risks of project failure and design a change management plan; you will identify means of converting the project from local to system-wide; and you will identify quality and safety measurements that will be used in evaluating the success of the project’s implementation.
État civil et statistiques pour la santé des populations
Johns Hopkins University (via Coursera)
Dans ce cours, vous découvrirez le rôle et l'importance des systèmes d'enregistrement des faits d'état civil (CRVS) qui sont utilisés pour suivre les naissances, les décès et les événements de la vie. Des experts du monde entier expliqueront ce que sont les systèmes CRVS, comment ils sont utilisés, les bases juridiques de l'enregistrement des événements de l'état civil et les défis posés par les systèmes CRVS. Vous apprendrez comment les données CRVS peuvent être utilisées pour informer la prise de décision au niveau de la population, y compris autour de méthodes spécifiques telles que la certification médicale de la cause du décès (MCCOD) et l'autopsie verbale, et l'application d'une lentille de genre et d'équité aux systèmes de données pour s'assurer qu'ils répondent aux besoins des populations. Les objectifs principaux du cours sont le soutien à la collecte de données sur les décès et les naissances au niveau national, l'amélioration de l'utilisation des données pour informer les priorités politiques, le suivi des tendances et la planification des interventions, et l'amélioration du suivi des principaux facteurs de risque de décès prématuré, en particulier ceux liés aux maladies non transmissibles. Ce cours est le produit d'une collaboration entre plusieurs partenaires, dont Vital Strategies, les Centres de contrôle et de prévention des maladies (CDC), la Fondation CDC, la Johns Hopkins Bloomberg School of Public Health, Global Health Advocacy Incubator, et l'Université de New South Wales, à Sydney. Ce cours a été financé par Bloomberg Philanthropies, avec un cofinancement du gouvernement australien et de la Fondation Bill et Melinda Gates.
Developing Data Products
Johns Hopkins University (via Coursera)
A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.
Pillar #3: Drug Effectiveness - Real-World Evidence
Johns Hopkins University (via Coursera)
This course addresses the key principles of research aimed at assessing the effectiveness and safety of pharmaceuticals. We begin by exploring the role of effectiveness research and stakeholders who need this evidence and explore the common observational designs that are used with real world data to generate real world evidence about safety, effectiveness and comparative effectiveness of drugs. We discuss confounders and biases that must be addressed and methods for overcoming these challenges both in the design and in the analysis of observational studies. We briefly address pragmatic trials as a source of effectiveness and safety information. The course concludes with examples of published studies illustrating good practices.
Teaching Reluctant Writers
Johns Hopkins University (via Coursera)
All educators will encounter students who struggle with writing. This course first focuses on the reasons student writers may be reluctant and then provides learners with a variety of strategies and practices to help reluctant writers develop a greater comfort and confidence with writing. Learners will examine classroom relationships, mentoring, scaffolding, conferencing, low-risk writing and mini-lessons, all tools and techniques that can be brought right into the classroom to help struggling writers increase student participation and success in writing. They'll conduct their own study of one reluctant writer and use their learning to help create a plan for teaching reluctant writers in their current and future classrooms.
Strategies for Senior Housing Communities during COVID-19
Johns Hopkins University (via Coursera)
SARS-CoV-2, the virus that causes COVID-19, poses a high risk for senior housing communities and the people who live and work there. The COVID-19 response has largely focused on nursing homes, leaving independent living communities serving older adults in the United States with fragmented guidance on how to respond to COVID-19 challenges. This course provides comprehensive instruction and resources for property owners and managers, senior housing staff, service coordinators and providers, community housing leaders, and other senior housing stakeholders to build upon their emergency preparedness and response strategies related to COVID-19. The course lectures, interviews, and assignments are largely geared toward federally-subsidized, multifamily senior housing providers, but the material is relevant for all senior housing providers. Learners will hear from experts about best practices to prevent COVID-19 outbreaks and promote well-being. Topics discussed include unique challenges for senior housing communities, development of emergency preparedness plans, outbreak prevention, and coronavirus-adapted housing operations. The course also covers strategies for communicating with stakeholders, promoting pandemic-safe behavior on site, and leveraging health departments and other agency resources, including a collection of resources for COVID-19 vaccination guidance. Learners are encouraged to concurrently develop and enhance their own community’s policies, procedures, and practices. This course was developed in partnership with the Baltimore City Health Department.
الحصول على البيانات وتنظيفها
Johns Hopkins University (via Coursera)
قبل أن تتمكن من العمل مع البيانات، يجب أن تحصل على بعضها. ستتناول هذه الدورة التدريبية الطرق الأساسية التي يمكن من خلالها الحصول على البيانات. ستتناول الدورة التدريبية كيفية الحصول على بيانات من الويب ومن واجهات برمجة التطبيقات ومن قواعد البيانات ومن الزملاء بتنسيقات مختلفة. كما أنها ستتناول أساسيات تنظيف البيانات وكيفية جعل البيانات "مُرتبة". فالبيانات المرتبة تزيد من سرعة مهام تحليل البيانات النهائية. وكذلك، ستتناول الدورة مكونات مجموعة بيانات كاملة بما في ذلك البيانات الأولية وتعليمات المعالجة وكتب التعليمات البرمجية والبيانات التي تمت معالجتها. ستتناول الدورة التدريبية الأساسيات اللازمة لجمع البيانات وتنظيفها ومشاركتها.
Cybersecurity Foundations: Threats Networks, and IoT Protection
Johns Hopkins University (via Coursera)
The course "Cybersecurity Fundamentals" provides a robust foundation in cybersecurity essentials, preparing you to recognize and manage threats in today's digital landscape. Across four comprehensive modules, you'll gain hands-on experience with malware identification, network traffic analysis, internet addressing, and IoT security protocols. In Module 1, explore the relationship between faults, vulnerabilities, and exploits, and learn essential malware identification techniques. Module 2 introduces NetFlow analysis with tools like SiLK and Wireshark, equipping you to collect and interpret network data. Module 3 deepens your understanding of IPv4 and IPv6, BGP routing, and host lookup tools, while Module 4 addresses IoT cybersecurity, where you’ll build a secure IoT framework using Docker and Node-Red. This course stands out by combining practical exercises with foundational knowledge, ensuring you’re ready to apply what you learn to real-world situations. Whether you’re a beginner or expanding your cybersecurity expertise, completing this course will enhance your ability to protect networks, manage internet space, and secure IoT devices—skills in high demand across the IT sector.
Introduction to Using Generative AI in Public Health
Johns Hopkins University (via Coursera)
The emergence of practical, everyday uses for artificial intelligence tools has captured the imagination of the public, for both good and bad. How, though, can artificial intelligence and generative AI tools like ChatGPT, Midjourney, or even Microsoft Copilot be used to improve the practice of public health? Can they be used safely and ethically? In this introductory, hands-on course, we'll look at these questions through a variety of lenses, offering opportunities to build skills in using generative AI tools in your own public health work. Introduces students to core concepts in the utilization of generative artificial intelligence (AI) tools. Explores ethical, financial, and policy-based issues in the application of generative AI to public health. Contrasts accuracy and reach of generative AI with the potential for both misinformation and problem-solving. Enables students to develop skills in utilizing generative AI tools for public health research and practice.
تحليل البيانات الاستكشافية
Johns Hopkins University (via Coursera)
يغطي هذا المقرر التقنيات الاستكشافية الأساسية لتلخيص البيانات. يتم تطبيق هذه الأساليب عادة قبل أن تبدأ النمذجة الرسمية ويمكن أن تساعد في تطوير نماذج إحصائية أكثر تعقيدًا. تعد التقنيات الاستكشافية مهمة أيضًا لإزالة أو شحذ الفرضيات المحتملة حول العالم التي يمكن معالجتها بواسطة البيانات. سنغطي بالتفصيل أنظمة التخطيط في R بالإضافة إلى بعض المبادئ الأساسية لإنشاء رسومات البيانات. سنغطي أيضًا بعض الأساليب الإحصائية الشائعة متعددة المتغيرات المستخدمة لتصور البيانات عالية الأبعاد.
Artificial Intelligence for Breast Cancer Detection
Johns Hopkins University (via Coursera)
The objective of this course is to provide students the knowledge of artificial intelligence processing approaches to breast cancer detection. Students will take quizzes and participate in discussion sessions to reinforce critical concepts conveyed in the modules. Reading assignments, including journal papers to understand the topics in the modules, will be provided. The course is designed for students who are interested in the career of product development using artificial intelligence and would like to know how AI can be applied to mammography. The course content is focused on the AI processing paradigm along with the domain knowledge of breast imaging. This course approach is unique, providing students a broad perspective of AI, rather than homing in on a particular implementation method. Students who complete this course will not only leverage the knowledge into an entry level job in the field of artificial intelligence but also perform well on projects because their thorough understanding of the AI processing paradigm.
Leadership in Action and Planning
Johns Hopkins University (via Coursera)
The course "Leadership in Action and Planning" provides practical skills and strategies for advancing your leadership capabilities in organizational settings. Focusing on strategic planning, change leadership, and coalition building, you'll gain the tools necessary to drive success and growth within any organization. Through real-world scenarios, you will learn how to develop comprehensive strategic plans, lead organizational change effectively, and create powerful alliances to enhance influence and collaboration. What sets this course apart is its focus on actionable leadership principles that can be applied immediately to both senior and emerging leaders. Whether you’re refining your existing skills or developing your leadership potential, the insights and methods offered will help you navigate complex leadership challenges with confidence and clarity. By the end of this course, you’ll have a deeper understanding of leadership as a strategic force, making you better equipped to lead, inspire, and succeed in high-stakes environments.
Public Health Perspectives on Sustainable Diets
Johns Hopkins University (via Coursera)
What we eat and how we produce that food have significant effects on human health and the sustainability of our planet. But what is a ‘sustainable’ diet? A sustainable diet, as defined by the FAO, promotes health and well-being and provides food security for the present population while sustaining human and natural resources for future generations. This short course looks at the urgent need to address the sustainability of our food systems, including better understanding the complex relationship between diet and climate change. We’ll explore current research on dietary shifts needed in high, middle, and low-income countries to achieve both sustainability and food security goals and discuss evidence-based strategies to promote sustainable diets. This course is offered by the Johns Hopkins Center for a Livable Future and draws from our graduate-level food systems curriculum at the Bloomberg School of Public Health. You may also be interested in our eight-week flagship Coursera course, “An Introduction to the US Food System: Perspectives from Public Health”.
Datos para Avanzar en la Salud Poblacional
Johns Hopkins University (via Coursera)
En este curso, aprenderá sobre las metodologías que respaldan el uso exitoso de los datos para fortalecer los programas y las políticas de salud pública. Expertos de todo el mundo definirán y explicarán qué son los datos a nivel de población, presentarán el ciclo de generación y uso de datos, y explicarán otras consideraciones para el uso exitoso de los datos para la salud de la población. También aprenderá cómo se pueden utilizar los datos de los servicios de salud para fundamentar la toma de decisiones a nivel de la población y la aplicación de una perspectiva de género y equidad hacia estos sistemas de datos para garantizar que respondan a las necesidades de las poblaciones. Nuestro objetivo general para este curso es apoyar y mejorar el uso de los datos para fundamentar las políticas. El curso es el resultado de una colaboración entre múltiples socios, que incluyen Vital Strategies, la Fundación de los Centros para el Control y la Prevención de Enfermedades (CDC), la Escuela de Salud Pública Bloomberg de Johns Hopkins y la Organización Mundial de la Salud. Este curso fue financiado por Bloomberg Philanthropies, con cofinanciación del gobierno australiano y la Fundación Bill y Melinda Gates.
Understanding Cancer Metastasis
Johns Hopkins University (via Coursera)
Over 500,000 people in the United States and over 8 million people worldwide are dying from cancer every year. As people live longer, the incidence of cancer is rising worldwide, and the disease is expected to strike over 20 million people annually by 2030. Everyone has been, or will be touched by cancer in some way during their lifetime. Thanks to years of dedication and commitment to research we’ve made enormous advances in the prevention and treatment of cancer, But there is still a lot of work to be done. In this course, physicians and scientists at the Johns Hopkins School of Medicine explain how cancer spreads or metastasizes. We’ll describe the major theories of metastasis and then describe the biology behind the steps in metastasis. The course also describes the major organs targeted by metastasis and describes how metastases harm the patient.
Principles of fMRI 1
Johns Hopkins University (via Coursera)
Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from many disciplines. Psychologists, statisticians, physicists, computer scientists, neuroscientists, medical researchers, behavioral scientists, engineers, public health researchers, biologists, and others are coming together to advance our understanding of the human mind and brain. This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data, including psychological inference, MR Physics, K Space, experimental design, pre-processing of fMRI data, as well as Generalized Linear Models (GLM’s). A book related to the class can be found here: https://leanpub.com/principlesoffmri.
Gender Foundations in Health Data: A Data for Health Course
Johns Hopkins University (via Coursera)
Welcome to Gender Foundations in Health Data: A Data for Health course. This course was developed from an online seminar series of the same name, that was hosted by Johns Hopkins University Bloomberg School of Health in 2021-22. The course instructors are Drs. Michelle Kaufman and Tahilin Sanchez Karver. This course will raise learners' awareness of the necessity of utilizing a gender lens in global public health data, policy, and practice, feature how-tos and key examples of integration of gender in data collection, analysis, and use from Data for Health partners Technical Specifications This course uses a third-party app called Articulate Rise to deliver the course materials. You will need to click-through a few screens (Articulate Rise, pop-up blockers) to enter and exit each Module. Please be sure to turn off pop-up blockers to access the materials. To post in the discussion forums, you will need to exit the Modules in Articulate Rise and go back to Coursera. Please feel free to reach out to teamgenderhealth@jh.edu with any questions, including technology concerns. We hope you have a good experience learning about gender integration in your health data work.
Exploring Algorithmic Bias as a Policy Issue: A Teach-Out
Johns Hopkins University (via Coursera)
This Teach Out does not issue certificates of completion. Algorithms – and algorithmic bias – are making regular appearances in the news, and increasingly, are being recognized as a policy issue. But what is an algorithm, exactly? And what does it mean when someone describes an algorithm as biased? This Teach-Out will encourage policy makers, agency leaders, and others in similar positions to identify algorithms that are already in use and make connections to broader ideas about fairness, justice, and equity. After completing the Teach-Out, learners will be able to participate in discussions around algorithmic bias, inform others about how algorithms can perpetuate existing disparities, and take steps to reduce the impact of algorithmic bias on the people and communities they serve.
The Ethical Leader
Johns Hopkins University (via Coursera)
This aims primarily at post-baccalaureate students interested in leadership theory and ethical leadership. The first part of this course introduces students to the classical literature in philosophical ethics, including consequentialist, regularian, deontological, and virtue theory approaches. This includes exploration of the ethical responsibilities leaders have toward themselves, corporations, the government, and the public. In the second part of the course, students apply decision- making frameworks, drawing from theories of leadership, and gain experience in decision-making surrounding ethical issues. Finally, students will consider the moral challenges that artificial intelligence poses to ethical leaders. This is one course in the Coursera specialization, Leadership: An Introduction. It examines the latest trends in leadership theory invoking several disciplines, including business, sociology, philosophy, history, and psychology. To complete this course successfully students should be able to analyze college-level readings and audio/visual presentations into understandable parts, including premises and conclusions; synthesize the results of the analysis into coherent and accurate summaries; and evaluate the results for accuracy and practical applicability. Upon successful completion of the course, students will be able to • Identify ethical principles for leadership practices • Compare theories of leadership in terms of their ethical strengths and weaknesses • Apply ethical principles for leadership practices • Compare and contrast industry standards and techniques of ethical leadership practices
Building a Data Science Team
Johns Hopkins University (via Coursera)
Data science is a team sport. As a data science executive it is your job to recruit, organize, and manage the team to success. In this one-week course, we will cover how you can find the right people to fill out your data science team, how to organize them to give them the best chance to feel empowered and successful, and how to manage your team as it grows. This is a focused course designed to rapidly get you up to speed on the process of building and managing a data science team. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. The different roles in the data science team including data scientist and data engineer 2. How the data science team relates to other teams in an organization 3. What are the expected qualifications of different data science team members 4. Relevant questions for interviewing data scientists 5. How to manage the onboarding process for the team 6. How to guide data science teams to success 7. How to encourage and empower data science teams Commitment: 1 week of study, 4-6 hours Course cover image by JaredZammit. Creative Commons BY-SA. https://flic.kr/p/5vuWZz
Practical Neuromarketing Research
Johns Hopkins University (via Coursera)
The course "Practical Neuromarketing Research" offers a deep dive into the intersection of behavioral science and neuromarketing, equipping you with the tools and knowledge to influence consumer decisions effectively. You will explore key behavior change models, including the neurocognitive influence model, and gain hands-on experience with biometric and neuroimaging tools for data collection. As you develop your research protocols, you’ll learn to apply ethical guidelines to human subject research, ensuring compliance with university and federal standards. By analyzing real-world data, you'll gain practical insights into the mechanisms behind influence, persuasion, and counter-arguing. What sets this course apart is its emphasis on applying theory to practice: from developing experimental designs to exploring cross-cultural neuroscience experiments, you'll walk away with not just knowledge but also the experience needed to advance science and contribute to neuromarketing research. This course is ideal for anyone looking to understand the neuroscience behind consumer behavior and apply it to real-world marketing challenges.
Teaching Texts and Forms
Johns Hopkins University (via Coursera)
The first job of any writer is to get words down on paper, and teaching writing as process helps students gain the fluency, comfort and confidence they need to succeed at any writing task. But complex, comprehensive writing tasks often bring with them specific expectations and conventions the writer must address to be successful. This course will examine some of those more comprehensive writing tasks: personal essays; argument, analysis and other forms of transactional writing; and creative writing. Learners will also identify strategies for supporting the reading/writing connection and practical assignments for engaging students in writing around texts.
Point of Care Testing for Sexually Transmitted Infections
Johns Hopkins University (via Coursera)
What are sexually transmitted infections (STIs), and why do we care? What is point of care testing (POCT)? What do clinicians and patients want and need from the POCT for STIs? Point of care testing, or medical diagnostic testing done at the time of patient care, is an important tool for the treatment of sexually transmitted infections. This course looks at point of care testing for sexually transmitted infections from the perspective of the clinician, the patient, and the regulatory environment. Learners will hear from experts about the basics of sexually transmitted infections, the methods of diagnosis, and then dive more deeply into the why and how of point of care testing. Note: This course discusses the specifics of sexually transmitted infections in detail, including photos of genitalia exhibiting symptoms of these infections.
Exploratory Data Analysis
Johns Hopkins University (via Coursera)
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
Fundamentals of Scientific Research Under Uncertainty
Johns Hopkins University (via Coursera)
This course teaches the fundamentals of scientific research. We approach the research process as a means of systematically reducing uncertainty and demonstrate how conducting a scientific investigation can be posed as an exercise in Bayesian uncertainty quantification. We begin by exploring the scientific landscape to understand the different types of research, where they are conducted, how they are supported, and why each of these types of research is important. We then formalize scientific inquiry and the scientific method and elaborate the research process and its scientific merits. Basic concepts in probability theory are introduced leading to a conceptually simple presentation of Bayes’ Rule. We then illustrate how Bayes’ Rule provides a mathematical framework for the research process. We place an emphasis on the role that research plays in our daily and professional lives and how research skills can help us think critically, whether you’re in a technical field or not. Exercises are designed to help you improve your research skills and think more scientifically. Learners who are new to research fields or would like to improve their research skills in any field for career/professional or personal growth are encouraged to enroll. The course is taught at an introductory level such that, by the end of the course, you will be able to formulate a research hypothesis and devise a scientific research plan to test that hypothesis. To be successful in this course, you will need entrance-level college mathematics.
Data Science in Real Life
Johns Hopkins University (via Coursera)
Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses. This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to: 1, Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts 5. Describe common pitfalls in communicating data analyses 6. Get a glimpse into a day in the life of a data analysis manager. The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include: 1. Experimental design, randomization, A/B testing 2. Causal inference, counterfactuals, 3. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference Course promo: https://www.youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic...
Leading Change in Health Informatics
Johns Hopkins University (via Coursera)
Do you dream of being a CMIO or a Senior Director of Clinical Informatics? If you are aiming to rise up in the ranks in your health system or looking to pivot your career in the direction of big data and health IT, this course is made for you. You'll hear from experts at Johns Hopkins about their experiences harnessing the power of big data in healthcare, improving EHR adoption, and separating out the hope vs hype when it comes to digital medicine. Whether you're a nurse, pharmacist, physician, other allied health professional or come from a non-clinical background--you know that Health Informatics skills are in demand. This newly launched 5-course specialization by JohnsHopkins faculty members provides a solid foundation for anyone wanting to become a leader in one of the hottest fields in healthcare. As health informaticians, we need to be very clear in our understanding of the current state (as-is), the future state (to-be) and any unintended consequences that can result from our interventions. Prior to introducing large scale change, we need to assess whether a healthcare organization is truly ready for change. This involves taking into account an organization's current culture and values. Successfully leading change through health informatics also requires strategic planning and careful financial considerations. Proper workflow redesign and a clear change management strategy are of utmost importance when introducing new technologies and in ensuring their successful adoption and proper use. By the end of this course, students will become familiar with examples of successful and failed attempts at change in health informatics, and the reasons for each. Students will be armed with tools to help optimize their chances for successfully leading change in their respective organizations.
Systems Science and Obesity
Johns Hopkins University (via Coursera)
Systems science has been instrumental in breaking new scientific ground in diverse fields such as meteorology, engineering and decision analysis. However, it is just beginning to impact public health. This seminar is designed to introduce students to basic tools of theory building and data analysis in systems science and to apply those tools to better understand the obesity epidemic in human populations. There will also be a lab in which students will use a simple demonstration model of food acquisition behavior using agent-based modeling on standard (free) software (netlogo). The central organizing idea of the course is to examine the obesity epidemic at a population level as an emergent properties of complex, nested systems, with attention to feedback processes, multilevel interactions, and the phenomenon of emergence. While the emphasis will be on obesity, the goal will be to explore ways in which the systems approach can be applied to other non-communicable diseases both nationally and internationally. Topics will include: a) the epidemiology of obesity across time and place, b) theories to explain population obesity, c) the role of environments and economic resources in obesity c) basic concepts and tools of systems science, d) modeling energy-balance related behaviors in context, e) agent-based models, systems dynamic models, and social network models
Assessing Health Program Delivery
Johns Hopkins University (via Coursera)
This course provides in-depth knowledge about implementation strength, quality of care, and service utilization, which are essential components of health program delivery. This course is primarily aimed at implementers, managers, funders, and evaluators of health programs in low- and middle-income settings (LMISs) targeting women and children, and undergraduate and graduate students in health-related fields. Those who complete this course successfully will be able to: 1. Explain why assessing health program delivery is an essential part of any large-scale program evaluation in LMISs. 2. Design an assessment of delivery for a health program in a LMIS, including proposed documentation of the program, indicators, measurement methods and tools, relevant contextual factors, and a plan for data analysis. 3. Interpret assessment results in the context of a large-scale effectiveness evaluation, and explain how they can be used to improve program policies and implementation. 4. Describe the RADAR tools for assessing implementation strength and the quality of care, and access guidelines for the use of the tools. The development of this course was supported by a grant from Government Affairs Canada (GAC) for the Real Accountability, Data Analysis for Results (RADAR) project.
데이터 과학자의 도구 상자
Johns Hopkins University (via Coursera)
이 과정에서는 데이터 과학자의 도구 상자에 있는 메인 도구와 아이디어를 소개합니다. 본 과정은 데이터 분석가와 데이터 과학자가 작업하는 데이터, 질문 및 도구의 개요에 대해 설명합니다. 이 과정에는 두 가지 구성 요소가 있습니다. 첫 번째는 데이터를 실행 가능한 지식으로 바꾸는 아이디어에 대한 개념적 소개입니다. 두 번째는 버전 관리, 마크다운, git, GitHub, R 및 RStudio와 같은 프로그램에서 사용할 도구에 대한 실용적인 소개입니다.
Introduction to Digital Health Entrepreneurship
Johns Hopkins University (via Coursera)
This course will provide an overview of digital health entrepreneurship with an initial emphasis on learning the basic digital health terminology, exploring a current example of telemedicine and its growth during the COVID pandemic, and gaining an understanding of the landscape and macro forces that affect the US healthcare system and the evolution of digital health. Students who successfully complete the course will understand the impact that payers, regulators, clinicians, and patients have on and the implications for digital health innovations.
Publishing Visualizations in R with Shiny and flexdashboard
Johns Hopkins University (via Coursera)
Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today. One of the critical tools for data visualization today is the R statistical programming language. Especially in conjunction with the tidyverse software packages, R has become an extremely powerful and flexible platform for making figures, tables, and reproducible reports. However, R can be intimidating for first time users, and there are so many resources online that it can be difficult to sort through without guidance. This course is the fourth in the Specialization "Data Visualization and Dashboarding in R." Learners will come to this course with a strong background in making visualization in R using ggplot2. To build on those skills, this course covers creating interactive visualization using Shiny, as well as combining different kinds of figures made in R into interactive dashboards.
Calculus through Data & Modeling: Limits & Derivatives
Johns Hopkins University (via Coursera)
This first course on concepts of single variable calculus will introduce the notions of limits of a function to define the derivative of a function. In mathematics, the derivative measures the sensitivity to change of the function. For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time advances. This fundamental notion will be applied through the modelling and analysis of data.
Управление анализом данных
Johns Hopkins University (via Coursera)
Этот недельный курс описывает процесс анализа данных и способы управления этим процессом. Мы рассмотрим итеративный характер анализа данных и роль постановки точного вопроса, исследовательский анализ данных, выводы, формальное статистическое моделирование, интерпретацию и коммуникацию. Также вы узнаете, как организовать аналитическую работу внутри команды и направить процесс анализа данных на получение понятных и полезных результатов. Это специализированный курс, предназначенный для быстрого ознакомления с процессом анализа данных и способами управления этим процессом. Мы стремились сделать курс максимально удобным для вас, не жертвуя важными материалами. Техническую информацию мы оставили в стороне, чтобы вы могли сконцентрироваться на управлении своей командой и ее развитии. По окончании этого курса вы сможете: 1. Описать базовый цикл анализа данных 2. Определять различные типы вопросов и преобразовывать их в специальные наборы данных 3. Описать различные типы получения данных 4. Анализировать наборы данных, чтобы определять, подходят ли они для вопроса 5. Координировать построение модели для распространенных типов анализа данных 6. Интерпретировать результаты распространенных типов анализа данных 7. Объединять статистические выводы для составления последовательных презентаций анализа данных Затраты времени: 1 учебная неделя, 4–6 часов Обложка курса от fdecomite. Creative Commons https://flic.kr/p/4HjmvD
Honors Algebra 2: Polynomials and Complex Numbers
Johns Hopkins University (via Coursera)
Honors Algebra 2: Polynomials and Complex Numbers By the end of this course, learners will be able to analyze, graph, and transform polynomial functions, apply techniques such as factoring, division, and the Remainder and Factor Theorems to solve higher-order equations, and use the Fundamental Theorem of Algebra to understand the complete set of polynomial solutions. They will also develop fluency with complex numbers, performing arithmetic operations, representing them in both algebraic and geometric forms, and applying them to solve equations that have no real solutions. As the second course in the three-part Honors Algebra 2 specialization, this class moves beyond routine algebraic skills to build deep mathematical reasoning. Students will see how polynomials and complex numbers provide the foundation for modern algebra, engineering, and physics, while practicing advanced problem-solving strategies that encourage both precision and creativity. What makes this course unique is its balance of rigor and accessibility. Learners progress through challenging concepts with step-by-step guidance, visual explanations, and real-world applications that demonstrate why these topics matter. Completing this course prepares students not only to excel in advanced high school mathematics but also to transition confidently into college-level coursework.
Evaluating Public Health Programs at Scale
Johns Hopkins University (via Coursera)
This course provides an introduction to evaluating public health programs at scale. This course focuses on evaluating public health programs and policies in low- and middle-income countries, however, core skills of designing and carrying out an evaluation are applicable to any public health programs and policies. The course will equip you with skills to: 1. Critique an evaluation of an international health program, identifying its strengths and possible weaknesses and how they could be addressed. 2. Develop a technically-sound evaluation plan for a reproductive, maternal, newborn, child health (RMNCAH) and nutrition program being implemented at scale in a low- or middle-income country, including evaluation design, key indicators, measurement methods, analysis, and communication of results. 3. Guide program managers and donors through a process of agreeing on priority evaluation activities included in an evaluation plan for a specific RMNCAH and nutrition program. 4. Make informed decisions about whether they want to pursue further learning and/or a professional role as an evaluator of large-scale programs. The development of this course was supported by a grant from Government Affairs Canada (GAC) for the Real Accountability, Data Analysis for Results (RADAR) project.
Introduction to Javascript and Ajax: Building Web Apps
Johns Hopkins University (via Coursera)
Do you realize that the only functionality of a web application that the user directly interacts with is through the web page? Implement it poorly and, to the user, the server-side becomes irrelevant! Today’s user expects a lot out of the web page: it has to load fast, expose the desired service, and be comfortable to view on all devices: from a desktop computers to tablets and mobile phones. The course covers fundamental Javascript programming concepts, starting with variables, data types, operators, and control flow mechanisms. It will then teach learners about object-oriented programming in Javascript, object creation using literals and constructors, prototypes, and the intricacies of the 'this' keyword. After which, the course goes into the power of Javascript arrays, using namespaces and IIFEs to prevent variable collisions, and discussion of closures and their use cases in Javascript programming. After covering the fundamental Javascript programming concepts, the course transitions into building dynamic and interactive websites by using Javascript and AJAX (Asynchronous JavaScript and XML) to interact with the Document Object Model (DOM), handle user events, and dynamically update web page content. The course will also talk about the HTTP protocol, different HTTP request methods, and how to work with JSON data. Building on these skills, learners will gain practical, hands-on experience by converting the restaurant website into a dynamic single-page application (SPA) powered by Javascript and AJAX.
Artificial Intelligence Industrial Control Systems Security
Johns Hopkins University (via Coursera)
The course "Artificial Intelligence Industrial Control Systems Security" explores the intersection of artificial intelligence (AI) and industrial control systems (ICS) security, focusing on the safety, trust, and privacy of AI technologies within critical infrastructures. Learners will gain a comprehensive understanding of the key cybersecurity challenges faced by ICS and the role AI can play in mitigating these risks. Through the exploration of large language models (LLMs), regulatory frameworks, and advanced ICS protocols, students will learn how to implement robust security measures for AI systems and industrial control environments. The course stands out by providing hands-on learning experiences in critical areas such as supply chain risks, cybersecurity for PLCs, and OT protocols. By combining AI principles with real-world ICS security practices, learners will be equipped to analyze and respond to emerging threats in both AI and ICS sectors. This unique approach ensures a deeper, more integrated understanding of how AI can be applied to enhance cybersecurity in industrial environments. Whether you're a professional or a beginner, this course will prepare you to tackle the most pressing security challenges at the intersection of AI and industrial control systems.
Measurement – Turning Concepts into Data
Johns Hopkins University (via Coursera)
This course provides a framework for how analysts can create and evaluate quantitative measures. Consider the many tricky concepts that are often of interest to analysts, such as health, educational attainment and trust in government. This course will explore various approaches for quantifying these concepts. The course begins with an overview of the different levels of measurement and ways to transform variables. We’ll then discuss how to construct and build a measurement model. We’ll next examine surveys, as they are one of the most frequently used measurement tools. As part of this discussion, we’ll cover survey sampling, design and evaluation. Lastly, we’ll consider different ways to judge the quality of a measure, such as by its level of reliability or validity. By the end of this course, you should be able to develop and critically assess measures for concepts worth study. After all, a good analysis is built on good measures.
Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors
Johns Hopkins University (via Coursera)
This course is the second course in the Linear Algebra Specialization. In this course, we continue to develop the techniques and theory to study matrices as special linear transformations (functions) on vectors. In particular, we develop techniques to manipulate matrices algebraically. This will allow us to better analyze and solve systems of linear equations. Furthermore, the definitions and theorems presented in the course allow use to identify the properties of an invertible matrix, identify relevant subspaces in R^n, We then focus on the geometry of the matrix transformation by studying the eigenvalues and eigenvectors of matrices. These numbers are useful for both pure and applied concepts in mathematics, data science, machine learning, artificial intelligence, and dynamical systems. We will see an application of Markov Chains and the Google PageRank Algorithm at the end of the course.
Infectious Disease Transmission Models for Decision-Makers
Johns Hopkins University (via Coursera)
During the COVID-19 pandemic, both the promise and perils of using infectious disease transmission models to make public health policy decisions became clearer than ever. Optimal use of modeled output requires that public health policy makers be informed consumers of models, that they understand the strengths and limitations of possible approaches, and they know the right questions to ask about the vulnerabilities of the model results. This course aims to provide anyone who makes decisions about public health policies and programs with a clear understanding of how infectious disease transmission models work, the various types and functions, and how they can be appropriately used to make decisions. There is no requirement that students have any prior background in infectious disease models and the course does not include any equations. Anyone with a basic background in public health and infectious diseases with an interest in learning more about infectious disease models will benefit from this course. In this course, participants will review the basics of infectious disease transmission models, including comparisons to other types of predictions used in daily life and an overview of the key components of a model and modeling structure. Next, participants will delve into the types of infectious disease models: forecasting, inferential, and theoretical models. Then, participants will learn about assessing whether a model is useful, reasonable and relevant, as well as the vulnerabilities of these models. These concepts will be applied to case studies of the Ebola outbreak in West Africa from 2014-2016 and the COVID-19 pandemic. The course will be rounded out with a review of how models inform policy decisions, including major sources of uncertainty for decision making. Learners who complete this course will have a broad view of infectious disease transmission models, how to assess the usefulness of a given model, and how these models inform policy decisions. Who ...
The Unix Workbench
Johns Hopkins University (via Coursera)
Unix forms a foundation that is often very helpful for accomplishing other goals you might have for you and your computer, whether that goal is running a business, writing a book, curing disease, or creating the next great app. The means to these goals are sometimes carried out by writing software. Software can’t be mined out of the ground, nor can software seeds be planted in spring to harvest by autumn. Software isn’t produced in factories on an assembly line. Software is a hand-made, often bespoke good. If a software developer is an artisan, then Unix is their workbench. Unix provides an essential and simple set of tools in a distraction-free environment. Even if you’re not a software developer learning Unix can open you up to new methods of thinking and novel ways to scale your ideas. This course is intended for folks who are new to programming and new to Unix-like operating systems like macOS and Linux distributions like Ubuntu. Most of the technologies discussed in this course will be accessed via a command line interface. Command line interfaces can seem alien at first, so this course attempts to draw parallels between using the command line and actions that you would normally take while using your mouse and keyboard. You’ll also learn how to write little pieces of software in a programming language called Bash, which allows you to connect together the tools we’ll discuss. My hope is that by the end of this course you be able to use different Unix tools as if they’re interconnecting Lego bricks.
Digitizing Population Health in Low-Resource Settings
Johns Hopkins University (via Coursera)
The rapid development of digital technologies provides fundamentally new ways of addressing pressing public health challenges, especially around understanding population health status, strengthening the collection and use of health data to inform public health policies and plan interventions, and strengthening health systems to advance disease prevention, health promotion, and health service delivery. This course introduces foundational concepts in digitization of population health data and programs, with the aim to support decision-making around the appropriate ecosystem for digitizing health data, the key stakeholders who should be engaged for effective digitization and strategies for implementing and scaling digitization efforts to strengthen population health programs. The course discusses special considerations for digitization within low-bandwidth, and low-resource environments and leverages case studies from low and middle-income country settings to orient learners to key concepts. In this course, you will learn: 1. Describe key components of an enabling ecosystem for digitization of population health programs (i.e., leadership and governance; strategy and investment; services and applications; standards and interoperability; infrastructure; legislation, policy, and compliance; and workforce). 2. Discuss the major global initiatives, frameworks, and resources available to support digitization of population health programs and key considerations when using these. 3. Understand how to promote digital ecosystem readiness and the use of maturity models in measuring readiness. 4. Explain key considerations when digitizing population health programs, such as principles of user-centered design, designing for sustainability and scale, and privacy and security of health data. 5. Describe the gender and equity considerations when developing tools and platforms for digital transformation in population health programs, including ways to promote equality and eq...
Human Resources Management Essentials
Johns Hopkins University (via Coursera)
Welcome to Human Resources Management Essentials! This course provides you with a comprehensive understanding of the fundamental concepts and practices involved in managing human resources within an ambulatory care practice. This course is designed for entry-level and beginner learners with little or no background in healthcare, but who may be interested in transitioning into the field. In this course, you will explore the critical role of human resources in healthcare organizations such as understanding HR policy and legal issues, learning strategies for conducting effective interviews, identifying top talent, and making informed hiring decisions. You will delve into change management, discover change leadership models, develop strategies to manage organizational change, and learn how HR supports communication, engagement, and overcoming resistance. You will also gain insights into well-being-centered leadership, including essential skills like active listening, role modeling, and fostering employee strengths. You will learn about change management, effective interviewing and hiring practices (including giving feedback, goal setting, crucial conversations, and performance reviews), HR policy and legal issues, and strategies for creating a culture of excellence. By the end of this course, you will have developed the skills needed to effectively manage HR functions within an ambulatory medical practice. There are no specific prerequisites for this program.
Principles of Management
Johns Hopkins University (via Coursera)
Team leads, managers, and entrepreneurs must juggle team citizenship and leadership, ethics, strategy, and projects with their work in their area of expertise. While an individual contributor’s success may depend on their own direct input -- the sweat of their own brow – managers’ success depends on their ability to enlist the active involvement of others: direct reports, other managers, other team members, other department employees, and those above them on the organizational chart -- sometimes even their suppliers or customers! How do you form, launch, and manage a team such that it has the highest chance of success? How can you ensure your team’s work aligns with the organization’s strategy? What basic project management tools can you utilize right away, without purchasing special software, that will help your team meet its objectives on time, on budget, and to spec? Your success at work will depend on the level of trust your colleagues, those who report to you, and those to whom you report feel toward you – how can you build and maintain a high degree of trust? While AI can support you in managerial actions, it cannot replace you. While AI is advanced enough to coach teams or address performance issues, it lacks the intrinsic connection to the organization that a human manager possesses. An employee who perceives themselves as working for “Company X's AI” will not feel the same level of loyalty or commitment as they would to YOU. Since AI, both literally and figuratively, cannot embody an organization or a manager, managers and leaders who delegate important communication and oversight responsibilities to AI inherit this fundamental limitation. This course will help you succeed in your career.
Major Depression in the Population: A Public Health Approach
Johns Hopkins University (via Coursera)
Public Mental Health is the application of the principles of medicine and social science to prevent the occurrence of mental and behavioral disorders and to promote mental health of the population. This course illustrates the principles of public health applied to depressive disorder, including principles of epidemiology, transcultural psychiatry, health services research, and prevention. It is predicted that by 2020 depressive disorder will be the most important cause of disease burden in the entire world! Every human being suffers from feeling depressed at some point or other, but only about one fifth of the population will experience an episode of depressive disorder over the course of their lives. This course illuminates the public health approach to disease, and the particular complexities of applying this approach to mental disorders, using depression as the exemplar.
Measuring the Success of a Patient Safety or Quality Improvement Project (Patient Safety VI)
Johns Hopkins University (via Coursera)
How will you know if your patient safety and quality project is meeting its objectives? Peter Drucker once said “What gets measured, gets managed.” In this course, students will learn why measurement is critical to quality improvement work. Equally important, they will learn which data sources provide the most meaningful information and tools for how and where to locate them. Finally, students will learn how to interpret data from their patient safety and quality projects to guide and modify them during implementation to maximize their chances of making a difference for patients.
Strategic and Inclusive Leadership
Johns Hopkins University (via Coursera)
The course "Strategic and Inclusive Leadership" empowers you to build the leadership skills necessary for navigating today’s diverse, dynamic organizational environments. It takes a comprehensive approach, exploring critical leadership principles such as inclusive leadership, gender dynamics, team leadership, and crisis management. You will learn how to align strategic thinking with organizational missions, values, and vision, while also refining your leadership profile to unlock your full potential. What makes this course unique is its focus on practical, real-world applications of leadership concepts, ensuring you can apply what you learn immediately to drive change. By analyzing leadership behaviors, team dynamics, and crisis case studies, you’ll gain a deeper understanding of both the challenges and opportunities in leadership today. This course is ideal for professionals looking to lead with impact, fostering inclusive, high-performing teams, and preparing for success in times of crisis. Whether you’re a seasoned leader or aspiring to develop your leadership potential, this course will guide you to the next level in your leadership journey.
Reducing Gun Violence in America: Evidence for Change
Johns Hopkins University (via Coursera)
Course is COMPLETELY FREE and DOES NOT ISSUE CERTIFICATES Reducing Gun Violence in America: Evidence for Change is designed to provide learners with the best available science and insights from top scholars across the country as well as the skills to understand which interventions are the most effective to offer a path forward for reducing gun violence in our homes, schools, and communities. Through this course, you will learn how to: 1. Appreciate the scope of gun violence and the importance of considering the issue across a variety of contexts. 2. Describe the role of law and policy in addressing gun violence at the federal, state, and local levels. 3. Compare the effectiveness of gun violence policies and highlight the importance of changing the way we talk about gun violence. 4. Describe state standards for civilian gun carrying and use and how those standards affect crime and violence. 5. Describe how firearm design is regulated, the effective and just enforcement of firearm laws, and strategies for reducing police-involved shootings. 6. Identify and explain evidence-based programs to reduce gun violence and understand public opinion on gun policy. As a student of this course, it’s important to recognize that you are part of an international learning community. We understand that gun violence can be a difficult issue to discuss and we all have our own set of opinions and beliefs. However, we ask you to please remember that gun violence is something that affects every person differently. It’s important to understand that this course is intended to generate productive and meaningful conversation. We ask students of this course to abide by the rules outlined and expected of you by Coursera’s Code of Conduct Policy. Please help us promote a healthy, productive, and sustainable learning environment for all and practice the following principles: Be polite. Treat your fellow learners with respect. Insulting, conde...
Bioinformatics Methods for Transcriptomics
Johns Hopkins University (via Coursera)
This course will cover bioinformatics methods for analyzing transcriptomic RNA sequencing data generated with the short read (RNA-seq) and long read (PacBio, ONT) sequencing. In its four modules, the course addresses the core transcriptomics questions: What are the genes and transcripts expressed in a given sample or condition of an experiment?, What are their expression levels?, and What are the differences in gene expression and splicing patterns between conditions? It provides hands-on instruction on how to use popular and/or emerging tools such as STAR, PsiCLASS, DESeq2, rMATS, MntJULiP, Minimap2 and IsoQuant. This is an intermediate level course, and assumes basic knowledge on using command line bioinformatics tools in a Unix-type environment.
Introduction to Ambulatory Healthcare Management
Johns Hopkins University (via Coursera)
Welcome to the Introduction to Ambulatory Healthcare Management course! This course provides you with a comprehensive understanding of the fundamental concepts and practices involved in managing ambulatory healthcare settings such as understanding the role of ambulatory care within the broader healthcare system, exploring key settings like outpatient clinics, physician practices, and urgent care centers, and mastering the unique challenges faced in these environments. This course is designed for entry-level and beginner learners with little or no background in healthcare, but who may be interested in transitioning into the field. Whether you are a healthcare professional, an aspiring manager, or simply interested in understanding the complexities of ambulatory care, this course will equip you with the necessary knowledge and insights. In this course, you will explore aspects of ambulatory care and where it fits in the healthcare continuum, become familiar with medical terminology, and explore various career opportunities in the healthcare sphere. There are no specific prerequisites for this program.
Sustainable Transportation Networks and Streetscapes
Johns Hopkins University (via Coursera)
This course will evaluate best practices in transportation networks, thoroughfares, and streetscape designs for the effective movement of people, goods, and services in a region. Sustainable public and private streetscape design and application will be reviewed and evaluated for applications for sustainable cities. Considerations are assessed for smart urban planning, growth, and lifestyle. Strategies for creating equitable, healthy, and sustainable communities are also evaluated. By the end of this course, you will be able to: 1. Survey and evaluate thoroughfare network considerations for connectivity, block size and sidewalk interaction. 2. Compare different complete street design options for application in smart growth planning. 3. Evaluate sidewalk design and planning strategies for public and private sidewalks to include street tree configurations and street light design. 4. Examine issues of water management with specialized curb design, ground water recharge areas and swales as part of the streetscape design and planning. 5. Identify and evaluate the differences between free-flow, slow-flow, and yield-flow thoroughfare design concepts. 6. Assess and evaluate smart urban planning, growth, and lifestyle indicators. The target audience for this course includes: Government Officials involved planning, designing, monitoring, enforcement, and assessment of sustainable project developments at the local, state, and federal level. Private sector companies in the transportation and municipal design and construction business Architects interested in advancing sustainable concepts for cities and communities Foundations, associations, and other NGOs that support smart growth strategies Academic faculty and students studying and researching community sustainability and resilience Private citizens interested in improving their communities and living conditions
Hypothesis Testing in Public Health
Johns Hopkins University (via Coursera)
Biostatistics is an essential skill for every public health researcher because it provides a set of precise methods for extracting meaningful conclusions from data. In this second course of the Biostatistics in Public Health Specialization, you'll learn to evaluate sample variability and apply statistical hypothesis testing methods. Along the way, you'll perform calculations and interpret real-world data from the published scientific literature. Topics include sample statistics, the central limit theorem, confidence intervals, hypothesis testing, and p values.
Gestion de l’analyse des données
Johns Hopkins University (via Coursera)
Ce cours d’une semaine décrit le processus d’analyse des données ainsi que la manière de gérer ce processus. Nous décrivons la nature répétitive de l’analyse des données et le rôle de la formulation d’une question précise, l’analyse exploratoire des données, l’inférence, la modélisation statistique formelle, l’interprétation et la communication. De plus, nous décrirons la façon d’orienter les activités analytiques au sein d’une équipe et de conduire le processus d’analyse des données vers des résultats cohérents et utiles. Ce cours est conçu de manière à vous former rapidement au processus de l’analyse des données et à la manière dont il peut être géré. Notre objectif est de faire en sorte que cette formation soit aussi pratique que possible pour vous, sans pour autant sacrifier le contenu essentiel. Nous avons laissé les informations techniques de côté afin que vous puissiez vous concentrer sur l'encadrement et la progression de votre équipe. Après avoir terminé ce cours, vous saurez comment... 1. Décrire la répétition de base de l’analyse des données 2. Identifier les différents types de questions et les traduire en ensembles de données spécifiques 3. Décrire les différents types d’extraction des données 4. Étudier les ensembles de données afin de déterminer si les données sont appropriées pour une question particulière 5. Orienter les initiatives de construction des modèles dans les analyses des données communes 6. Interpréter les résultats issus des analyses des données communes 7. Intégrer les résultats statistiques afin de créer des présentations d’analyse des données cohérentes Engagement : 1 semaine de formation, 4 à 6 heures de cours Image de couverture du cours par fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD
Business Analytics with Excel: Intermediate to Advanced
Johns Hopkins University (via Coursera)
This Business Analytics course in Excel is the second of a three part series, with the intended audience business professionals, MBA students, advanced undergraduates, and analysts who already understand basic analytics and want to upskill with techniques used in operations, finance, logistics, and strategy.. Upon completing this course, learners will be able to build and solve advanced optimization models in Excel, including linear, integer, network, and nonlinear programs; apply matrix functions to scale and streamline analysis; and use VBA macros to implement multi-goal programming and explore trade-offs through Pareto-efficient solutions. This intermediate-to-advanced course is a continuation of Business Analytics: Elementary to Advanced and is designed for learners who want to move from using Excel for analysis to using it for sophisticated decision-making. Rather than treating Excel as a passive calculation tool, the course shows how it can function as a powerful optimization and modeling environment for real business problems. Learners will benefit by gaining practical, immediately applicable skills that allow them to model constraints, manage competing objectives, and justify decisions quantitatively. What makes this course unique is its hands-on, problem-driven approach and its emphasis on automation and multi-objective thinking. By combining Solver, matrix methods, and macros, learners not only solve harder problems faster, but they also gain a flexible toolkit that is engaging, creative, and directly transferable to real-world decision environments. This course will prepare you for the third course of the Business Analytics Specialization, Simulation and Optimization.
Algebra: Elementary to Advanced - Functions & Applications
Johns Hopkins University (via Coursera)
After completing this course, students will learn how to successfully apply functions to model different data and real world occurrences. This course reviews the concept of a function and then provide multiple examples of common and uncommon types of functions used in a variety of disciplines. Formulas, domains, ranges, graphs, intercepts, and fundamental behavior are all analyzed using both algebraic and analytic techniques. From this core set of functions, new functions are created by arithmetic operations and function composition. These functions are then applied to solve real world problems. The ability to picture many different types of functions will help students learn how and when to apply these functions, as well as give students the geometric intuition to understand the algebraic techniques. The skills and objectives from this course improve problem solving abilities.
Civil Registration & Vital Statistics in Population Health
Johns Hopkins University (via Coursera)
In this course, you will learn about the role and importance of civil registration and vital statistics (CRVS) systems that are used to track birth, death, and life events. Experts from around the world will explain what CRVS systems are, how they are used, the legal bases for registering vital events, and challenges experienced with CRVS systems. You will learn about how CRVS data can be used to inform population-level decision-making, including around specific methods such as medical certification of cause of death (MCCOD) and verbal autopsy, and the application of a gender and equity lens to data systems to ensure they are responsive to the needs of populations. Our overarching goals for the course are to support the collection of country-level death and birth data, to improve the use of data to inform policy priorities, track trends, and plan interventions, and enhance monitoring of major risk factors for early death, especially from noncommunicable diseases. The course is the result of a collaboration among multiple partners, including Vital Strategies, the Centers for Disease Control and Prevention (CDC), the CDC Foundation, the Johns Hopkins Bloomberg School of Public Health, Global Health Advocacy Incubator, and University of New South Wales, Sydney. This course was funded by Bloomberg Philanthropies, with co-funding from the Australian government and Bill and Melinda Gates Foundation.
Introduction to Reproducibility in Cancer Informatics
Johns Hopkins University (via Coursera)
The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research and have not had training in reproducibility tools and methods. This course is written for individuals who: Have some familiarity with R or Python - have written some scripts. Have not had formal training in computational methods. Have limited or no familiar with GitHub, Docker, or package management tools. Motivation Data analyses are generally not reproducible without direct contact with the original researchers and a substantial amount of time and effort (BeaulieuJones et al, 2017). Reproducibility in cancer informatics (as with other fields) is still not monitored or incentivized despite that it is fundamental to the scientific method. Despite the lack of incentive, many researchers strive for reproducibility in their own work but often lack the skills or training to do so effectively. Equipping researchers with the skills to create reproducible data analyses increases the efficiency of everyone involved. Reproducible analyses are more likely to be understood, applied, and replicated by others. This helps expedite the scientific process by helping researchers avoid false positive dead ends. Open source clarity in reproducible methods also saves researchers' time so they don't have to reinvent the proverbial wheel for methods that everyone in the field is already performing. Curriculum This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses. The course also introduces tools relevant to reproducibility including analysis notebooks, package managers, git and GitHub. The course includes hands-on exercises for how to apply reproducible code concepts to their code. Individuals who take this course are encouraged to complete these activities as they follow alo...
Road Safety & Indigenous Communities: Safe Systems Approach
Johns Hopkins University (via Coursera)
Motor vehicle traffic deaths affect indigenous communities at a disproportionate rate as compared to the overall United States population. For children and youth ages 0-19, motor vehicle traffic death rates among American Indian and Alaska Native children and youths are up to 8 times higher than those of other racial and ethnic groups. This is often due to structural barriers in tribal communities such as lack of lighting, potholes, cattle, etc. This course dives specifically into the effectiveness of taking a Safe Systems approach to road safety in tribal communities. Perspectives from Safe Systems experts and tribal partners on safe systems approaches to road safety are highlighted as examples from specific communities. The course provides historical context to road safety and specific considerations for roads in tribal nations. Next, the course focuses on how to intervene to create a safe system in communities. In addition, this course will prepare you with information on how to apply for an SS4A grant, with examples from specific communities who have successfully obtained the grant. Throughout the course, resources are provided with tools on how to apply for SS4A grant and how to implement a safe systems approach.
The Mathematics of Democracy, Politics and Manipulation
Johns Hopkins University (via Coursera)
Welcome to The Mathematics of Politics, Democracy, and Manipulation, an inspiring course that explores the intricate landscape of democracy and decision-making. In today's world, understanding the mechanisms that shape our electoral systems and influence democratic outcomes is more crucial than ever. This comprehensive course delves deep into five key pillars of electoral theory and practice, empowering participants with the knowledge and tools to navigate the complexities of voting and representation. Voting Theory: Discover the theoretical foundations of voting systems, from classic methods like plurality voting to innovative approaches such as ranked-choice and approval voting. Explore the principles of fairness, efficiency, and representation that underpin different voting methods, and gain insights into their real-world applications and implications. US Electoral College: Journey through the history, mechanics, and controversies of the United States Electoral College. Unravel the mysteries of presidential elections, from the allocation of electoral votes to the winner-takes-all system, and examine the impact of the Electoral College on American politics and governance. Weighted Voting: Unlock the secrets of weighted voting systems and their role in decision-making. From corporate boards to international organizations, explore how weighted voting structures allocate power and influence, and learn how to analyze and evaluate the distribution of voting power within diverse coalitions. Apportionment: Delve into the principles and practices of apportionment, the process of allocating political representation. Examine the challenges of balancing population equality, geographic representation, and minority rights, and explore the methodologies used to divide legislative seats in democratic societies. Gerrymandering: Confront the complexities and controversies of electoral boundary manipulation. Investigate the tactics and consequences of gerrymandering, from par...
YARN MapReduce Architecture and Advanced Programming
Johns Hopkins University (via Coursera)
The course "YARN MapReduce Architecture and Advanced Programming" provides an in-depth understanding of YARN and MapReduce architectures, focusing on their components and capabilities. Students will explore the MapReduce programming model and learn essential optimization techniques such as combiners, partitioners, and compression to improve job performance. The course covers Mapper and Reducer parallelism in MapReduce, along with practical steps for writing and configuring MapReduce jobs. Advanced topics such as multithreading, speculative execution, and input/output formats are also explored. By the end of the course, participants will have hands-on experience in optimizing and writing efficient MapReduce jobs, preparing them to apply best practices in real-world scenarios. This course is unique as it not only covers the foundational aspects of YARN and MapReduce but also delves into optimization strategies, offering learners the tools to enhance data processing efficiency. Whether you're new to MapReduce or looking to deepen your knowledge, this course provides valuable insights for mastering large-scale data processing.
Planning a Patient Safety or Quality Improvement Project (Patient Safety III)
Johns Hopkins University (via Coursera)
This course provides students with a set of tools and methodologies to plan and initiate a Problem Solving or Quality Improvement project. The first module presents methods for selecting, scoping and structuring a project before it is even initiated. It also introduces the project classifications of implementation and discovery. The second module describes the A3 problem solving methodology and the tool itself. Further in that same module, the student is shown tools to identify problems in flow, defects, and waste and to discover causes, brainstorm, and prioritize interventions. Module 3 shows a methodology within the implementation class. These methods are designed to overcome emotional and organizational barriers to translating evidence-based interventions into practice. The fourth and last module looks at one more way to approach improvement projects in the discovery class. These tools are specifically for new, out-of-the-box design thinking.
Algebra: Elementary to Advanced - Polynomials and Roots
Johns Hopkins University (via Coursera)
This course is the final course in a three part algebra sequence, In this course, students extend their knowledge of more advanced functions, and apply and model them using both algebraic and geometric techniques. This course enables students to make logical deductions and arrive at reasonable conclusions. Such skills are crucial in today's world. Knowing how to analyze quantitative information for the purpose of making decisions, judgments, and predictions is essential for understanding many important social and political issues. Quantitative Skills and Reasoning provides students the skills needed for evaluating such quantitatively-based arguments. This class is important as the mathematical ideas it treats and the mathematical language and symbolic manipulation it uses to express those ideas are essential for students who will progress to calculus, statistics, or data science.
Data Science Decisions in Time:Sequential Hypothesis Testing
Johns Hopkins University (via Coursera)
This is part of our specialization on Making Decision in Time. For this second course we start with a landmark paper from Chernoff and build new insights into the ideas that his paper sparked. The ending point should bring new code and new algorithm insights into perspective, and use, by many computer and data scientists.
Advanced Cybersecurity Topics
Johns Hopkins University (via Coursera)
This course is primarily aimed at cybersecurity professionals, advanced students, and individuals with foundational knowledge in cybersecurity looking to expand their expertise in ethical hacking and defense strategies. Advanced Cybersecurity Topics provides in-depth coverage of sophisticated security challenges, including rootkits, operating system security, buffer overflow vulnerabilities, race conditions, and post-exploitation techniques. It’s ideal for those who want to deepen their understanding of modern cybersecurity threats and defenses, and are ready to tackle complex scenarios involving system vulnerabilities, privilege escalation, and mitigation strategies. Through hands-on labs and real-world case studies, participants will gain practical experience using the MITRE ATT&CK Enterprise Framework and other ethical hacking methodologies to analyze and defend against advanced attacks. Whether you are a seasoned professional aiming to advance your skills or a cybersecurity student looking to deepen your knowledge, this course equips you with critical tools to face complex security challenges.
Embajador de la Vacuna COVID: Cómo Hablar con los Padres
Johns Hopkins University (via Coursera)
La vacunación es una estrategia clave para prevenir enfermedades graves y la muerte por COVID-19. Estas vacunas están disponibles para niños de a partir de los 5 años, pero muchos padres tienen preguntas sobre las vacunaciones. Este curso de capacitación prepara a los padres de niños en edad escolar, a las Asociaciones de Padres y Maestros, a los miembros de la comunidad y al personal de la escuela para que sean embajadores de vacunas y promuevan la aceptación de la vacuna en sus comunidades. Después de completar el curso, los embajadores de vacunas podrán compartir conocimientos sobre la COVID-19 y su vacuna, participar en conversaciones sobre la indecisión sobre las vacunas de manera respetuosa y empática y dirigir a las personas a fuentes confiables para obtener más información sobre las vacunas contra la COVID-19.
Advanced Network Security and Analysis
Johns Hopkins University (via Coursera)
The course "Advanced Network Security and Analysis" dives into the essential skills needed to protect and analyze complex network environments. This course covers advanced topics like anonymization techniques, mobile application security, and in-depth analysis of DNS, HTTP, SMTP, and TCP protocols. Learners will gain practical experience in recognizing vulnerabilities and analyzing network traffic to detect potential threats. Each module offers hands-on insights into industry-standard tools and techniques, equipping students to address real-world security challenges confidently. Uniquely focused on practical application, the course empowers students to work with tools like Splunk, TCPDump, and Wireshark, ensuring they can navigate and mitigate complex network security scenarios. Through interactive content, learners will gain a comprehensive understanding of network protocols, explore the mechanics of cyber-attacks, and learn how to implement effective defenses. By the end of the course, students will have developed an advanced toolkit for safeguarding modern network infrastructures, making them valuable assets in any IT security environment.
Honors Algebra 2: Linear and Quadratic Functions
Johns Hopkins University (via Coursera)
Honors Algebra 2: Linear and Quadratic Equations is the first course of a high-level algebra course designed to deepen your mathematical thinking and prepare you for advanced study in math, science, and engineering. Whether you're a high school student looking to accelerate your progress or an adult learner brushing up on foundational skills, this course offers a rich, engaging experience aligned with the Common Core State Standards. You'll explore the essential building blocks of algebra: linear functions, quadratic functions, and systems of equations. Through interactive lessons and real-world applications, you'll develop a deep understanding of how equations model the world around us—from predicting trends to describing physical phenomena. Along the way, you'll strengthen your skills in graphing, solving equations, and interpreting mathematical relationships, with an emphasis on conceptual understanding and mathematical reasoning. Designed with a university-level rigor but paced for online learners, this course invites you to go beyond memorization and truly engage with the power and elegance of algebra.
Mastering Neural Networks and Model Regularization
Johns Hopkins University (via Coursera)
The course "Mastering Neural Networks and Model Regularization" dives deep into the fundamentals and advanced techniques of neural networks, from understanding perceptron-based models to implementing cutting-edge convolutional neural networks (CNNs). This course offers hands-on experience with real-world datasets, such as MNIST, and focuses on practical applications using the PyTorch framework. Learners will explore key regularization techniques like L1, L2, and drop-out to reduce model overfitting, as well as decision tree pruning. What makes this course unique is its emphasis on building neural networks from scratch, allowing learners to grasp the intricate details of model design and training. Additionally, the course covers computational graphs, activation and loss functions, and how to efficiently utilize GPUs for faster computation. Learners will also delve into CNNs for image and audio processing, gaining insights into cutting-edge applications in these fields. By completing this course, learners will develop advanced skills in neural network design, model regularization, and the use of PyTorch for deep learning tasks—empowering them to tackle complex machine learning challenges with confidence.
Building Data Visualization Tools
Johns Hopkins University (via Coursera)
The data science revolution has produced reams of new data from a wide variety of new sources. These new datasets are being used to answer new questions in way never before conceived. Visualization remains one of the most powerful ways draw conclusions from data, but the influx of new data types requires the development of new visualization techniques and building blocks. This course provides you with the skills for creating those new visualization building blocks. We focus on the ggplot2 framework and describe how to use and extend the system to suit the specific needs of your organization or team. Upon completing this course, learners will be able to build the tools needed to visualize a wide variety of data types and will have the fundamentals needed to address new data types as they come about.
Understanding Prostate Cancer
Johns Hopkins University (via Coursera)
Welcome to Understanding Prostate Cancer. My name is Ken Pienta, Professor of Urology and Oncology at the Johns Hopkins School of Medicine. I have been studying prostate cancer and treating patients with prostate cancer for over 25 years. Over 1,000,000 men worldwide and 230,000 men in the United States are diagnosed with prostate cancer every year. Three hundred thousand men worldwide and 30,000 men in the US are dying from prostate cancer every year. As people live longer, the incidence of prostate cancer is rising worldwide and prostate cancer continues to be a major health problem. Thanks to years of dedication and commitment to research we’ve made enormous advances in the treatment of prostate cancer, But there is still a lot of work to be done. In this Understanding Prostate Cancer course, I will provide an introduction to the biology of prostate cancer as well as how it is identified and treated at various stages of the disease. I've put together this course in order to introduce you to the essentials of prostate cancer. By the time you finish this course you'll be able to Define risk factors for prostate cancer Understand current prostate cancer screening guidelines Understand prostate cancer staging Understand treatments for localized prostate cancer Understand treatments for advanced prostate cancer Understand treatments to alleviate the symptoms caused by prostate cancer This Understanding Prostate Cancer Course should be helpful to anyone who wants to develop a deeper understanding of prostate cancer biology and treatment. It should be useful to students who are interested in a deeper understanding of the science of cancer. It should also be helpful to health care providers, data managers, and educators who wish to develop a better understanding of prostate cancer and how it affects individuals. The course is not designed for patients seeking treatment guidance. For those of you who might be thinking about a career in cancer research or...
Command Line Tools for Genomic Data Science
Johns Hopkins University (via Coursera)
Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Business Analytics with Excel: Elementary to Advanced
Johns Hopkins University (via Coursera)
A leader in a data driven world requires the knowledge of both data-related (statistical) methods and of appropriate models to use that data. This Business Analytics class focuses on the latter: it introduces students to analytical frameworks used for decision making though Excel modeling. These include Linear and Integer Optimization, Decision Analysis, and Risk modeling. For each methodology students are first exposed to the basic mechanics, and then apply the methodology to real-world business problems using Excel. Emphasis will be not on the "how-to" of Excel, but rather on formulating problems, translating those formulations into useful models, optimizing and/or displaying the models, and interpreting results. The course will prepare managers who are comfortable with translating trade-offs into models, understanding the output of the software, and who are appreciative of quantitative approaches to decision making. Business analytics makes extensive use of data and modeling to drive decision making in organizations. This class focuses on introducing students to analytical frameworks used for decision making to make sense of the data, starting from the basics of Excel and working up to advanced modeling techniques.
Foundations of Healthcare Systems Engineering
Johns Hopkins University (via Coursera)
Through dynamic video lectures and practical application questions, you will learn about the Foundations of Healthcare Systems Engineering. In this course you will learn about the current lack of synchronized, efficient, and integrated healthcare systems, which are some of the drivers for improvements to healthcare delivery. Also in this course, you will learn about the different types of systems and how they are translated to the healthcare field for appropriate systems engineering process applications, with exemplars. Upon system type articulation and mapping, the systems engineering approach will be introduced to help begin the process of: 1) investigating healthcare challenges, needs, and requirements development; 2) developing system concepts, that are derived from requirements, and then realized in physical and process form; and finally, 3) the establishment of means to verify, validate, and deploy healthcare systems that address the need and meet requirements. Applications and exemplars will be provided.
Data and Health Indicators in Public Health Practice
Johns Hopkins University (via Coursera)
Epidemiology is often described as the cornerstone science in public health. Epidemiology in public health practice uses study design and analyses to identify causes in an outbreak situation, guides interventions to improve population health, and evaluates programs and policies. In this course, we'll define the role of the professional epidemiologist as it relates to public health services, functions, and competencies. With that foundation in mind, we'll introduce you to the problem solving methodology and demonstrate how it can be used in a wide variety of settings to identify problems, propose solutions, and evaluate interventions. This methodology depends on the use of reliable data, so we'll take a deep dive into the routine and public health data systems that lie at the heart of epidemiology and then conclude with how you can use that data to calculate measures of disease burden in populations.
Calculus through Data & Modeling: Precalculus Review
Johns Hopkins University (via Coursera)
This course is an applications-oriented, investigative approach to the study of the mathematical topics needed for further coursework in single and multivariable calculus. The unifying theme is the study of functions, including polynomial, rational, exponential, logarithmic, and trigonometric functions. An emphasis is placed on using these functions to model and analyze data. Graphing calculators and/or the computer will be used as an integral part of the course.
Ein Crashkurs in Datenwissenschaft
Johns Hopkins University (via Coursera)
Inzwischen haben Sie sicher schon von Datenwissenschaft und Big Data gehört. In diesem einwöchigen Kurs werden wir in einem Crashkurs vermitteln, was diese Begriffe bedeuten und inwiefern sie in erfolgreichen Organisationen eine Rolle spielen. Dieser Kurs richtet sich an alle, die mehr über die Datenwissenschaft erfahren möchten, aber auch an jene, die vorhaben, ein Team von Datenwissenschaftlern zu leiten. Das Ziel ist es, Sie so schnell wie möglich pragmatisch mit der Datenwissenschaft vertraut zu machen. Wir haben diesen Kurs so praktisch und zweckmäßig wie möglich angelegt, ohne jedoch auf die Grundlagen zu verzichten. Hierbei handelt es sich um einen zielgerichteten Kurs, der Sie im Bereich der Datenwissenschaft schnell auf den neuesten Stand bringen soll. Unser Ziel war es, dies für Sie so bequem wie möglich zu gestalten, ohne auf wesentliche Inhalte zu verzichten. Wir haben die technischen Informationen beiseitegelassen, damit Sie sich darauf konzentrieren können, Ihr Team zu managen und voranzubringen. Nach Abschluss dieses Kurses werden Sie: 1. Wissen, wie man die Rolle der Datenwissenschaft in verschiedenen Kontexten beschreibt 2. Wissen, inwiefern Statistik, maschinelles Lernen und Software-Engineering in der Datenwissenschaft eine Rolle spielen 3. Wissen, wie man die Struktur eines datenwissenschaftlichen Projekts beschreibt 4. Die wichtigsten Begriffe und Tools kennen, die von Datenwissenschaftlern verwendet werden 5. Wissen, wie man ein erfolgreiches und ein erfolgloses datenwissenschaftliches Projekt erkennt 3. Mehr über die Rolle eines Data Science Managers wissen Kurs-Cover-Bild von r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT
Fundamental Neuroscience for Neuroimaging
Johns Hopkins University (via Coursera)
Neuroimaging methods are used with increasing frequency in clinical practice and basic research. Designed for students and professionals, this course will introduce the basic principles of neuroimaging methods as applied to human subjects research and introduce the neuroscience concepts and terminology necessary for a basic understanding of neuroimaging applications. Topics include the history of neuroimaging, an introduction to neuroimaging physics and image formation, as well as an overview of different neuroimaging applications, including functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, perfusion imaging, and positron emission tomography imaging. Each will be reviewed in the context of their specific methods, source of signal, goals, and limitations. The course will also introduce basic neuroscience concepts necessary to understand the implementation of neuroimaging methods, including structural and functional human neuroanatomy, cognitive domains, and experimental design.
Computing for Cancer Informatics
Johns Hopkins University (via Coursera)
One of the key cancer informatics challenges is dealing with and managing the explosion of large data from multiple sources that are often too large to work with on typical personal computers. This course is designed to help researchers and investigators to understand the basics of computing and to familiarize them with various computing options to ultimately help guide their decisions on the topic. This course aims to provide research leaders with awareness and guidance about: Basic computing terminology Concepts about how computers and computing systems work Differences between shared computing resources Appropriate etiquette for shared computing resources Computing resources designed for cancer research Considerations for computing resource decisions Target audience: This course is intended for researchers (including postdocs and students) with limited to intermediate experience with informatics research. The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines. Curriculum: We will provide you with familiarity with fundamental computing terms. We will also discuss relevant concepts about how computers and shared computing resources work. We will explore the differences between various computing resource options, as well as provide guidance on how to make important computing discussions. This course is part of a series of courses for the Informatics Technology for Cancer Research (ITCR) called the Informatics Technology for Cancer Research Education Resource. This material was created by the ITCR Training Network (ITN) which is a collaborative effort of researchers around the United States to support cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: National Cancer Institute (NCI) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators...
Technology and Product Planning
Johns Hopkins University (via Coursera)
Through the voices of start-up and product leaders this course makes transparent what it takes to build a digital health product for healthcare customers large and small. It outlines the planning, resources, process, and team needed to get a healthcare technology product from idea to operational at a customer site. Get to know the range of possible software products and team roles associated with them, walk through the healthcare software design process, learn the key steps for healthcare software product planning, and then find out what it takes to deploy at a customer site. Start-up leaders share moments that made them pause, question what they knew, and knowledge they’d bestow on future start-up leaders throughout the course. Product leaders in healthcare reframe the software development process from the perspective of the healthcare industry, a space with unique stakeholders, customers, and users. Leave with a map of how to build technology and a product out of your digital health software concept.
Visualizing Data in the Tidyverse
Johns Hopkins University (via Coursera)
Data visualization is a critical part of any data science project. Once data have been imported and wrangled into place, visualizing your data can help you get a handle on what’s going on in the data set. Similarly, once you’ve completed your analysis and are ready to present your findings, data visualizations are a highly effective way to communicate your results to others. In this course we will cover what data visualization is and define some of the basic types of data visualizations. In this course you will learn about the ggplot2 R package, a powerful set of tools for making stunning data graphics that has become the industry standard. You will learn about different types of plots, how to construct effect plots, and what makes for a successful or unsuccessful visualization. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
Health for All Through Primary Health Care
Johns Hopkins University (via Coursera)
This course explores why primary health care is central for achieving Health for All. It provides examples of how primary health care has been instrumental in approaching this goal in selected populations and how the principles of primary health care can guide future policies and actions. Two of the most inspiring, least understood, and most often derided terms in global health discourse are “Health for All” and “Primary Health Care.” In this course, we will explore these terms in the context of global health, their origins and meanings, the principles upon which they rest, and examples of how these principles have been implemented at both small and large scale. We will also explore some ultra-low-cost approaches to Health for All through primary health care, and the promise that primary health care holds for eventually achieving Health for All. Each module of this course consists of approximately one hour or lecture, approximately one hour of additional readings or video presentations, and two additional hours devoted to studying for each of the quizzes, writing and evaluating two short peer-review assignments, and participating in the discussion forums. Developed in collaboration with Johns Hopkins Open Education Lab.
Using Sensors With Your Raspberry Pi
Johns Hopkins University (via Coursera)
This course on integrating sensors with your Raspberry Pi is course 3 of a Coursera Specialization and can be taken separately or as part of the specialization. Although some material and explanations from the prior two courses are used, this course largely assumes no prior experience with sensors or data processing other than ideas about your own projects and an interest in building projects with sensors. This course focuses on core concepts and techniques in designing and integrating any sensor, rather than overly specific examples to copy. This method allows you to use these concepts in your projects to build highly customized sensors for your applications. Some of the ideas covered include calibrating sensors and the trade-offs between different mathematical methods of storing and applying calibration curves to your sensors. We also discuss accuracy, precision, and how to understand uncertainty in your measurements. We study methods of interfacing analog sensors with your Raspberry Pi (or other platform) with amplifiers and the theory and technique involved in reducing noise with spectral filters. Lastly, we borrow from the fields of data science, statistics, and digital signal processing, to post-process our data in Python.
Measuring and Maximizing Impact of COVID-19 Contact Tracing
Johns Hopkins University (via Coursera)
This course aims to provide managers and developers of contact tracing programs guidance on the most important indicators of performance of a contact tracing program, and a tool that can be used to project the likely impact of improvements in specific indicators. Students who complete the course will be proficient in using the Contact Tracing Evaluation and Strategic Support Application (ConTESSA) to estimate the impact of their contact tracing program on transmission and strategizing about how to increase their program’s impact. A secondary audience for the course will be decision makers interested in knowing more about the characteristics of effective contact tracing programs, and strategies to improve. The course is designed for individuals who are already leading contact tracing programs who have significant experience with epidemiology and public health. We strongly recommend completing this course on a laptop or a desktop rather than a phone as you’ll need to complete worksheets and open the course and the application simultaneously.
Data Use for Disease Control & Global Health Decision-Making
Johns Hopkins University (via Coursera)
Conduct epidemiologic surveillance to inform decision-making Apply best practices for healthcare data collection and analysis Apply the lessons learned from the polio eradication effort This informative three-week course will prepare you to meet the challenges of conducting epidemiologic surveillance to gather data to inform decision-making and planning. Using the polio eradication effort as a case study, you’ll address the application of surveillance systems in a wide variety of settings. Throughout the course, you’ll reflect on and apply the lessons learned from the global polio eradication initiative, an effort led by the World Health Organisation resolved to eradicate the disease poliomyelitis. You will learn lessons from this initiative - the largest of its kind in history -and will apply data for decision-making going forward. You’ll also discuss the challenges and strategies that can be presented when applying data. You will identify various different epidemiological systems in countries, such as the Democratic Republic of Congo and Nigeria, and on how to conduct data collection in remote areas. Then, you will discuss the best practices for conducting epidemiologic surveillance in a wide range of locations and will come to a range of solutions for the conclusion of how to conduct and use data in a variety of different situations.
Calculus through Data & Modelling: Techniques of Integration
Johns Hopkins University (via Coursera)
In this course, we build on previously defined notions of the integral of a single-variable function over an interval. Now, we will extend our understanding of integrals to work with functions of more than one variable. First, we will learn how to integrate a real-valued multivariable function over different regions in the plane. Then, we will introduce vector functions, which assigns a point to a vector. This will prepare us for our final course in the specialization on vector calculus. Finally, we will introduce techniques to approximate definite integrals when working with discrete data and through a peer reviewed project on, apply these techniques real world problems.
Foundations of Data Visualization
Johns Hopkins University (via Coursera)
In the course "Foundations of Data Visualization", you will build a solid foundation in data visualization, learning how to effectively transform raw data into meaningful insights. The course covers fundamental concepts, including basic data types, human visual perception, and essential design principles that influence how we interpret and understand visual information. You will also explore various visualization techniques suitable for different data types, from bar charts to scatter plots, and learn how to select the best approach for specific data sets. The course emphasizes how human visual perception impacts how we process and interpret data, helping you create visuals that resonate with your audience. What makes this course unique is its combination of practical, hands-on exercises and theoretical knowledge, enabling you to apply what you’ve learned in real time. You’ll gain experience in designing intuitive, impactful visualizations that not only display data but also tell a compelling story.
Machine Learning and Emerging Technologies in Cybersecurity
Johns Hopkins University (via Coursera)
The course "Machine Learning and Emerging Technologies in Cybersecurity" offers an in-depth exploration of machine learning applications in cybersecurity, focusing on techniques for threat detection and prevention. Participants will gain a solid grounding in machine learning fundamentals, including neural networks, clustering, and support vector machines, tailored specifically for cybersecurity contexts. Unique to this course is the integration of machine learning with Intrusion Detection Systems (IDS), equipping learners with practical skills to enhance threat detection capabilities. Additionally, the course examines Tor networking, providing insights into secure and anonymous communication systems, as well as the critical role of IDS within Cyber Security Incident Response Teams (CSIRTs) in enterprise environments. By the end of the course, learners will not only understand how to apply advanced machine learning techniques but also be proficient in tools like RapidMiner and Security Onion. This blend of theory and hands-on application ensures that participants leave with the skills needed to tackle real-world cybersecurity challenges effectively, making this course a vital resource for those looking to advance their careers in cybersecurity and data science.
Calculus through Data & Modelling: Vector Calculus
Johns Hopkins University (via Coursera)
This course continues your study of calculus by focusing on the applications of integration to vector valued functions, or vector fields. These are functions that assign vectors to points in space, allowing us to develop advanced theories to then apply to real-world problems. We define line integrals, which can be used to fund the work done by a vector field. We culminate this course with Green's Theorem, which describes the relationship between certain kinds of line integrals on closed paths and double integrals. In the discrete case, this theorem is called the Shoelace Theorem and allows us to measure the areas of polygons. We use this version of the theorem to develop more tools of data analysis through a peer reviewed project. Upon successful completion of this course, you have all the tools needed to master any advanced mathematics, computer science, or data science that builds off of the foundations of single or multivariable calculus.
Understanding and Strengthening Health Systems
Johns Hopkins University (via Coursera)
Welcome to our course on Understanding and Strengthening Health Systems for Global Health. During the course we will provide you with an overview of the main elements or building blocks of a health system based on the World Health Organization’s guidance. You will have the opportunity to explore four main areas of health systems in global health with particular reference to low and middle income countries. The first area focuses on understanding health service organizations, the challenges. Our second module looks at WHO’s six major building blocks or health systems components with particular reference to primary health care and the need for community participation in planning, delivery and assessment of these systems components. in our third module we examine the specific systems component of human resource development and capacity building. The fourth area consists of health policy making and advocacy with stakeholders. This course is geared toward learners who are already involved in managing health and development programs on the ground in low and middle income countries or who are preparing for such a management role. The main lectures will span a four-week period with approximately 2-4 hours of viewing learning materials per week. We have one peer graded essay wherein you will use skills in ‘organizational’ diagnosis to better understand a challenge in an organization where you are or have worked. There are also quizzes. We hope you will engage with your fellow learners in discussion forums to learn from each other.
Introduction to CSS3
Johns Hopkins University (via Coursera)
Do you realize that the only functionality of a web application that the user directly interacts with is through the web page? Implement it poorly and, to the user, the server-side becomes irrelevant! Today’s user expects a lot out of the web page: it has to load fast, expose the desired service, and be comfortable to view on all devices: from a desktop computers to tablets and mobile phones. This course provides a comprehensive introduction to webpage styling and layout using CSS. It will discuss how one can select and target specific HTML elements, apply styles using CSS properties, and interact with the CSS box model. Additionally, the course teaches key CSS concepts, including selectors, specificity, inheritance, units of measurement, positioning, and floats. The course will also cover responsive design principles and how to use media queries to create websites that adapt to different screen sizes. Additionally, the course will introduce Bootstrap, a popular CSS framework, and demonstrates how to use its grid system to build responsive layouts efficiently. By the end of the course, learners have a solid foundation in CSS and be able to create visually appealing and responsive websites.
Applied Machine Learning: Techniques and Applications
Johns Hopkins University (via Coursera)
The course "Applied Machine Learning: Techniques and Applications" focuses on the practical use of machine learning across various domains, particularly in computer vision, data feature analysis, and model evaluation. Learners will gain hands-on experience with key techniques, such as image processing and supervised learning methods while mastering essential skills in data pre-processing and model evaluation. This course stands out for its balance between foundational concepts and real-world applications, giving learners the opportunity to work with widely-used datasets and tools like scikit-learn. Topics include image classification, object detection, feature extraction, and the selection of evaluation metrics for assessing model performance. By completing this course, learners will be equipped with the practical skills necessary to implement machine learning solutions, enabling them to apply these techniques to solve complex problems in data processing, computer vision, and more.
Introduction to Parallel Programming with CUDA
Johns Hopkins University (via Coursera)
This course will help prepare students for developing code that can process large amounts of data in parallel on Graphics Processing Units (GPUs). It will learn on how to implement software that can solve complex problems with the leading consumer to enterprise-grade GPUs available using Nvidia CUDA. They will focus on the hardware and software capabilities, including the use of 100s to 1000s of threads and various forms of memory.
Foundations of Neuroscience
Johns Hopkins University (via Coursera)
The course "Foundations of Neuroscience" delves into the neural and biological foundations of influence and persuasion, providing a comprehensive understanding of how the brain processes sensory, emotional, and cognitive information. You will explore key brain regions and their role in decision-making, memory, attention, and emotion, essential for effective communication strategies. By gaining hands-on experience with neuroscience measurement techniques, you will learn to assess the impact of influence campaigns through implicit and explicit methods. What sets this course apart is its focus on bridging neuroscience with practical applications in neuromarketing. By studying real-world case studies, you will gain the skills to evaluate and design more effective influence strategies. Whether you're in marketing, advertising, or behavioral science, this course equips you with the knowledge to better understand consumer behavior and tailor marketing efforts for maximum impact. You'll leave with a deep understanding of how to apply cutting-edge neuroscience principles to real-world influence campaigns, making this course a unique and invaluable resource for anyone in the field.
Patient Safety and Quality Improvement: Developing a Systems View (Patient Safety I)
Johns Hopkins University (via Coursera)
In this course, you will be able develop a systems view for patient safety and quality improvement in healthcare. By then end of this course, you will be able to: 1) Describe a minimum of four key events in the history of patient safety and quality improvement, 2) define the key characteristics of high reliability organizations, and 3) explain the benefits of having strategies for both proactive and reactive systems thinking.
Inclusive Online Teaching Teach-Out
Johns Hopkins University (via Coursera)
In this course, higher education faculty will examine students’ barriers to learning, including unconscious bias, physical impairments, and lack of motivation. Participants will explore and discuss how inclusive pedagogy and Universal Design for Learning (UDL) can help to address those barriers and improve student learning outcomes.
Mathematical Biostatistics Boot Camp 1
Johns Hopkins University (via Coursera)
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
The Outcomes and Interventions of Health Informatics
Johns Hopkins University (via Coursera)
For clinical data science to be effective in healthcare—to achieve the outcomes desired—it must translate into decision support of some sort, either at the patient, clinician, or manager level. By the end of this course, students will be able to articulate the need for an intervention, to right size it, to choose the appropriate technology, to describe how knowledge should be obtained, and to design a monitoring plan.
HTML, CSS, and Javascript for Web Developers
Johns Hopkins University (via Coursera)
Do you realize that the only functionality of a web application that the user directly interacts with is through the web page? Implement it poorly and, to the user, the server-side becomes irrelevant! Today’s user expects a lot out of the web page: it has to load fast, expose the desired service, and be comfortable to view on all devices: from a desktop computers to tablets and mobile phones. In this course, we will learn the basic tools that every web page coder needs to know. We will start from the ground up by learning how to implement modern web pages with HTML and CSS. We will then advance to learning how to code our pages such that its components rearrange and resize themselves automatically based on the size of the user’s screen. You’ll be able to code up a web page that will be just as useful on a mobile phone as on a desktop computer. No “pinch and zoom” required! Last but certainly not least, we will get a thorough introduction to the most ubiquitous, popular, and incredibly powerful language of the web: Javascript. Using Javascript, you will be able to build a fully functional web application that utilizes Ajax to expose server-side functionality and data to the end user.
Linear Algebra: Linear Systems and Matrix Equations
Johns Hopkins University (via Coursera)
This is the first course of a three course specialization that introduces the students to the concepts of linear algebra, one of the most important and basic areas of mathematics, with many real-life applications. This foundational material provides both theory and applications for topics in mathematics, engineering and the sciences. The course content focuses on linear equations, matrix methods, analytical geometry and linear transformations. As well as mastering techniques, students will be exposed to the more abstract ideas of linear algebra. Lectures, readings, quizzes, and a project all help students to master course content and and learn to read, write, and even correct mathematical proofs. At the end of the course, students will be fluent in the language of linear algebra, learning new definitions and theorems along with examples and counterexamples. Students will also learn to employ techniques to classify and solve linear systems of equations. This course prepares students to continue their study of linear transformations with the next course in the specialization. .
Summary Statistics in Public Health
Johns Hopkins University (via Coursera)
Biostatistics is the application of statistical reasoning to the life sciences, and it is the key to unlocking the data gathered by researchers and the evidence presented in the scientific literature. In this course, we'll focus on the use of statistical measurement methods within the world of public health research. Along the way, you'll be introduced to a variety of methods and measures, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include summary measures, visual displays, continuous data, sample size, the normal distribution, binary data, the element of time, and the Kaplan-Meir curve.
Network Visualization and Intervention
Johns Hopkins University (via Coursera)
In the "Network Interventions" course, learners will explore the foundational principles of data manipulation, visualization, and the dynamics of networks. This course stands out by seamlessly integrating theoretical knowledge with practical applications. You'll gain expertise in Relational Algebra, empowering you to construct and interpret operations that effectively manage complex datasets. The course also emphasizes the art of Network Visualization, where you will learn to create impactful visual representations of data, making complex information accessible and understandable. Additionally, the course delves into Network Interventions, teaching you how to influence behaviors and ideas within social networks. You will master strategies to identify opinion leaders and implement effective segmentation techniques, essential skills for driving change in various contexts. By the end of the course, you will be equipped not only with analytical and visualization skills but also with the ability to influence social dynamics, preparing you for impactful roles in data-driven environments. This unique combination of skills makes the Network Intervention course an invaluable asset for those looking to thrive in the evolving landscape of data science and social network analysis.
What are the Chances? Probability and Uncertainty in Statistics
Johns Hopkins University (via Coursera)
This course focuses on how analysts can measure and describe the confidence they have in their findings. The course begins with an overview of the key probability rules and concepts that govern the calculation of uncertainty measures. We’ll then apply these ideas to variables (which are the building blocks of statistics) and their associated probability distributions. The second half of the course will delve into the computation and interpretation of uncertainty. We’ll discuss how to conduct a hypothesis test using both test statistics and confidence intervals. Finally, we’ll consider the role of hypothesis testing in a regression context, including what we can and cannot learn from the statistical significance of a coefficient. By the end of the course, you should be able to discuss statistical findings in probabilistic terms and interpret the uncertainty of a particular estimate.
COVID-19 Contact Tracing
Johns Hopkins University (via Coursera)
The COVID-19 crisis has created an unprecedented need for contact tracing across the country, requiring thousands of people to learn key skills quickly. The job qualifications for contact tracing positions differ throughout the country and the world, with some new positions open to individuals with a high school diploma or equivalent. In this introductory course, students will learn about the science of SARS-CoV-2 , including the infectious period, the clinical presentation of COVID-19, and the evidence for how SARS-CoV-2 is transmitted from person-to-person and why contact tracing can be such an effective public health intervention. Students will learn about how contact tracing is done, including how to build rapport with cases, identify their contacts, and support both cases and their contacts to stop transmission in their communities. The course will also cover several important ethical considerations around contact tracing, isolation, and quarantine. Finally, the course will identify some of the most common barriers to contact tracing efforts -- along with strategies to overcome them.
Introduction to AI: Key Concepts and Applications
Johns Hopkins University (via Coursera)
The course "Core Concepts in AI" provides a comprehensive foundation in artificial intelligence (AI) and machine learning (ML), equipping learners with the essential tools to understand, evaluate, and implement AI systems effectively. From decoding key terminology and frameworks like R.O.A.D. (Requirements, Operationalize Data, Analytic Method, Deployment) to exploring algorithm tradeoffs and data quality, this course offers practical insights that bridge technical concepts with strategic decision-making. What sets this course apart is its focus on balancing technical depth with accessibility, making it ideal for leaders, managers, and professionals tasked with driving AI initiatives. Learners will delve into performance metrics, inter-annotator agreement, and tradeoffs in resources, gaining a nuanced understanding of AI's strengths and limitations. Whether you're a newcomer or looking to deepen your understanding, this course empowers you to make informed AI decisions, optimize systems, and address challenges in data quality and algorithm selection. By the end, you'll have the confidence to navigate AI projects and align them with organizational goals, positioning yourself as a strategic leader in AI-driven innovation.
Toxicology 21: Scientific Applications
Johns Hopkins University (via Coursera)
This course familiarizes students with the novel concepts being used to revamp regulatory toxicology in response to a breakthrough National Research Council Report “Toxicity Testing in the 21st Century: A Vision and a Strategy.” We present the latest developments in the field of toxicology—the shift from animal testing toward human relevant, high content, high-throughput integrative testing strategies. Active programs from EPA, NIH, and the global scientific community illustrate the dynamics of safety sciences.
Designing Hardware for Raspberry Pi Projects
Johns Hopkins University (via Coursera)
This is course 4 of this specialization (although it can be taken out of order) and focuses on applying experience and knowledge gained in the first three courses to build physical electronics hardware. Specifically, this course focuses on four areas: circuit simulation, schematic entry, PCB layout, and 3D CAD modeling. There are many excellent commercial applications available in these areas, however to give everyone access we'll be using all free and open-source software. By the end of this course you should feel comfortable using free and open-source software to design your own printed circuit board and any bracketry or case to hold it, customized for your application. Module 1 covers circuit simulation using several open-source projects and simulation methods for simulating transient response of circuits as well as frequency-domain response of filters. Additionally, we'll use open-source filter synthesis tools to help you quickly design and simulation filters. Module 2 is all about creating professional looking electrical schematics. This is both an art and a skill and we'll cover the technical elements of using schematic entry software as well as broad concepts that are portable to any commercial application. Module 3 takes our schematic and turns it into a physical PCB design. Understanding this process of how the schematic and the PCB layout work together is critical. We'll be demonstrating this with open-source software, but again, the concepts apply to any commercial software you may have access to. Module 4 demonstrates the powerful idea of co-designing your electrical and mechanical systems together. We'll create a 3D model of our electrical PCB and bring it into 3D CAD software to design mechanical parts around it. Tying together these two applications opens another dimension in customizing your projects.
Rastreamento de contato da COVID-19
Johns Hopkins University (via Coursera)
A crise da COVID-19 criou uma necessidade sem precedentes de rastreamento de contato em todo o país, exigindo que milhares de pessoas aprendessem rapidamente as principais habilidades. As qualificações para os cargos de rastreamento de contato diferem em todo o país e no mundo, com alguns novos cargos abertos a indivíduos com diploma de ensino médio ou equivalente. Neste curso introdutório, os alunos aprenderão a ciência do SARS-CoV-2, incluindo o período infeccioso, a apresentação clínica da COVID-19, as evidências de como o SARS-CoV-2 é transmitido entre as pessoas e por que o rastreamento de contatos pode ser uma intervenção de saúde pública tão efetiva. Os alunos aprenderão como é feito o rastreamento de contatos, incluindo como criar afinidade com os casos, identificar seus contatos e apoiar tanto os casos quanto seus contatos para interromper a transmissão nas respectivas comunidades. O curso também abordará várias considerações éticas importantes sobre rastreamento de contatos, isolamento e quarentena. Por fim, o curso identificará algumas das barreiras mais comuns aos esforços de rastreamento de contato, assim como estratégias para superá-las.
Foundations of Telehealth
Johns Hopkins University (via Coursera)
Telemedicine has proven itself to be an important part of the future of healthcare. In this course, students will be introduced to the key components and considerations needed to design and implement a successful telemedicine program at both the practice and health system levels. The course emphasizes operational design principles and highlights a team based approach. Key content areas include clinical considerations, patient safety, technology needs, patient satisfaction, legal, government affairs, regulatory and compliance, and billing considerations.
Advanced Network Analysis and Incident Response
Johns Hopkins University (via Coursera)
The course "Advanced Network Analysis and Incident Response" equips learners with critical skills for effectively managing and responding to cyber threats. Through a blend of theoretical concepts and hands-on practice, participants will delve into advanced network situational awareness, network packet analysis, and incident response strategies aligned with organizational security policies. What sets this course apart is its comprehensive approach to both the technical and strategic aspects of cybersecurity. Learners will engage with both government-off-the-shelf (GOTS) and commercial-off-the-shelf (COTS) tools, gaining practical experience in analyzing network traffic and implementing effective incident response protocols. The curriculum also incorporates real-world scenarios through tabletop exercises and emphasizes the application of the NIST Cybersecurity Framework and the SANS Incident Response Cycle. By completing this course, learners will enhance their ability to detect, analyze, and respond to incidents effectively, preparing them for challenges in the dynamic field of cybersecurity. Whether you're aiming to advance your career or reinforce your skills, this course provides the knowledge and confidence needed to excel in network analysis and incident response.
Outbreaks and Epidemics
Johns Hopkins University (via Coursera)
Professional epidemiologists are often called on to investigate outbreaks and epidemics. This course serves as an introduction to the essentials of investigation, identifying pathogens, figuring out what's going on, reporting, and responding. You'll learn how to ask precise epidemiologic questions and apply epidemiologic tools to uncover the answers. You'll also learn about basic epidemic dynamics and the terrible law that cause them to grow, as well as the reasons why they recede and eventually go away. The course concludes with deep dives into some real outbreaks from Ebola, in West Africa, to the opioid epidemic in the United States.
Practical Machine Learning
Johns Hopkins University (via Coursera)
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
International Travel Preparation, Safety, & Wellness
Johns Hopkins University (via Coursera)
Whether you've traveled before or not, living and working overseas can be challenging. Learn how best to prepare and make the most of your time internationally. This course will prepare you to work and live overseas. It explores the epidemiology of common morbidity and mortality among travelers and examines key prevention, safety, and travel medicine principles and services to contextualize risks and maintain wellness. The course reviews applicable interventions, appropriate vaccines, and personal protection methods to prepare you to respond to expected and unexpected situations and will challenge you to examine travel health and safety priorities through case studies and discussions. The Honors Lesson will assist you with personal preparations for travel through the creation of a country-specific profile.
Healthcare Data Security, Privacy, and Compliance
Johns Hopkins University (via Coursera)
In the final course of the Healthcare IT Support program, we will focus on the types of healthcare data that you need to be aware, complexities of security and privacy within healthcare, and issues related to compliance and reporting. As a health IT support specialist, you’ll be exposed to different types of data sources and data elements that are utilized in healthcare. It’s important for you to understand the basic language of healthcare data and for you to recognize the sensitive nature of protected health information (PHI). Maintaining data privacy and security is everyone’s responsibility, including IT support staff! We’ll go into detail about HIPAA and the risks associated with security breaches, ransomware and phishing. We’ll go into detail about some of the key laws and regulations specific to healthcare and the importance of compliance with them. You'll leave this course well versed on the Stark Law, the Joint Commission and the purpose of quality measures. We wrap up the Healthcare IT Support certificate with tips on job interviews, skills that can make you standout, and words of advice on the endless possibilities in this dynamic and growing field. Make sure you talk to others who’ve been there before about the process of being hired at a large health system. Be rest assured that you’ll receive training when you start a new role, and you might even be partnered with someone else for the first few weeks as you get onboarded. Remember, this is not the end--rather, it’s just the beginning of the next step in your journey!
Black Lives Matter
Johns Hopkins University (via Coursera)
The BlackLivesMatter movement is the most significant political movement in African American life in the United States in the last fifty years. BLM leaders denounced anti-black racism, white supremacy, and police brutality and reshaped how we think about gender, sexuality, social justice, economic injustice, and crime. The movement is grounded in a long history of African American activism. From slave revolts to the Black Panther Party, from the founding of the Congressional Black Caucus, to the eruption of the BLM Movements, this course is an interdisciplinary and historical exploration of the BlackLivesMatter movement.
Designing for Sustainment: Keeping Improvement Work on Track (Patient Safety IV)
Johns Hopkins University (via Coursera)
Keeping patient safety and quality improvement projects on track, on time, and on budget is critical to ensuring their success. In this course, students will be introduced and given the opportunity to apply a series of tools to guide and manage patient safety and quality initiatives. These include tools for defining what success looks like, developing a change management plan, and conducting a pre-mortem to identify risks for project failure. This course will also provide tools for engaging stakeholders to ensure key players are invested in your project’s success.
Culminating Project in Health Informatics
Johns Hopkins University (via Coursera)
This capstone course in the Health Informatics Specialization will allow learners to create a comprehensive plan for an informatics intervention of their choosing, and that will demonstrate to current or future employers the new skills obtained through the completion of this series of five courses in Health Informatics.
Measuring and Modeling Impact in Evaluations
Johns Hopkins University (via Coursera)
We want to provide you some information about our course “Measuring and Modeling Impact in evaluations”. The purpose of this course is to give you a better understanding of different measures of impact that could be used in the evaluation of a program in the areas of maternal and child health and nutrition. For each of the measures presented, we will discuss current sources of data you might draw on as well as describe the methods that can be used to measure these. When we describe the methods, we also try to identify the strengths and weakness of the methods as well as their suitably for use in an evaluation. The course also discusses how modeling can be used in evaluations as either a replacement for measuring impact or to supplement measured impact. The last two lessons in this course focus on giving you an introduction and training on how the Lives Saved Tool (LiST) works and how to use it. This model can be used to estimate most – if not all – of the impact measures we describe in the course and can be an important part of both planning and estimating impact in an evaluation of a large-scale program. While this course is self-contained, it is also linked to other courses on evaluation. We developed this course for public health program managers and evaluators and assume the students in the course will have a background in public health with a focus on maternal and child health in low- and middle-income countries. The development of this course was supported by a grant from Government Affairs Canada (GAC) for the Real Accountability, Data Analysis for Results (RADAR) project.
Practical Methodology and Ethics in AI
Johns Hopkins University (via Coursera)
The course "Practical Methodologies and Ethics in AI" equips learners with the essential skills needed to build, evaluate, and deploy deep learning models, while also addressing critical ethical considerations in AI. Through hands-on projects and case studies, you’ll explore the practical methodologies used to train models effectively, troubleshoot issues, and apply structured probabilistic approaches to manage uncertainty. A key highlight of the course is its emphasis on ethics, enabling you to identify and address bias, fairness, and societal implications throughout the AI lifecycle. By integrating structured probabilistic models with deep learning, you’ll gain the ability to create robust, interpretable AI systems that tackle real-world challenges. What sets this course apart is its balanced focus on technical mastery and responsible AI practices. You’ll learn to handle incomplete data, analyze peer presentations, and critically evaluate AI’s broader societal impact. Whether you’re a data scientist or an AI enthusiast, this course will provide a comprehensive foundation to develop impactful and ethical AI solutions.
Emerging Approaches for Measuring Population Health
Johns Hopkins University (via Coursera)
In this course, you will learn about traditional and emerging techniques in population-level data collection that can be used to strengthen public health programs and policies. Experts from around the world will define and explain key concepts in the design and implementation of population-based surveys, focusing on the use of emerging survey techniques such as mobile phone and web-based surveys, health services data and health information systems, and population-based health registries. You will learn about how these data can be used to inform population-level decision-making, and the application of a gender and equity lens to ensure they are tracking and responsive to the needs of populations. Our overarching goals for the course are to support the collection of population-level health data, track trends, plan interventions, and enhance the monitoring of major risk factors for early death - especially from non-communicable diseases. The course is the result of a collaboration among multiple partners, including Vital Strategies, Centers for Disease Control and Prevention (CDC), the Johns Hopkins Bloomberg School of Public Health, Rwanda Ministry of Health, United Nations, University of Colombo, Sri Lanka, and the World Health Organization. This course was funded by Bloomberg Philanthropies, with co-funding from the Australian government and Bill and Melinda Gates Foundation.
Computational and Graphical Models in Probability
Johns Hopkins University (via Coursera)
The course "Computational and Graphical Models in Probability" equips learners with essential skills to analyze complex systems through simulation techniques and network analysis. By exploring advanced concepts such as Exponential Random Graph Models and Probabilistic Graphical Models, students will learn to model and interpret intricate social structures and dependencies within data. What sets this course apart is its emphasis on practical applications using the R programming language, empowering students to simulate random variables effectively and construct sophisticated models for real-world scenarios. Through hands-on projects and exercises, learners will not only deepen their theoretical understanding but also gain valuable experience in solving applied problems across various domains. Upon completion, you will be well-prepared to tackle challenges in data analysis, machine learning, and statistical modeling, making you a valuable asset in any data-driven field. Whether you're looking to enhance your expertise or start a new career, this course offers a unique blend of theory and practical skills that will enable you to excel in today’s data-centric world.
Training AI with Humans
Johns Hopkins University (via Coursera)
In the course "Training AI with Humans", you'll delve into the intersection of machine learning and human collaboration, exploring how to enhance AI performance through effective data annotation and crowdsourcing. You’ll gain a comprehensive understanding of machine learning principles and performance metrics while developing practical skills in using platforms like Amazon Mechanical Turk (AMT) for crowdsourced tasks. This unique approach combines theoretical knowledge with hands-on experience, allowing you to implement Inter-Annotator Agreement (IAA) techniques to ensure high-quality annotated data. By completing this course, you will be well-equipped to design and conduct impactful crowdsourcing studies, improving AI models in real-world applications such as healthcare and research. Whether you're looking to enhance your skills in machine learning, optimize data collection processes, or understand the ethical implications of crowdsourcing, this course offers invaluable insights and tools.
Leadership for Cancer Informatics Research
Johns Hopkins University (via Coursera)
Informatics research often requires multidisciplinary teams. This requires more flexibility to communicate with team members with distinct backgrounds. Furthermore, team members often have different research and career goals. This can present unique challenges in making sure that everyone is on the same page and cohesively working together. This course aims to provide research leaders with guidance about: How to effectively lead and support team members on informatics projects How to perform informatics projects well How to support informatics collaborators, mentees, and employees How to better support diversity within your team Tools that can help you perform informatics projects well Target audience: The course is intended for researchers who lead research teams or collaborate with others to perform multidisciplinary work. We have especially aimed the material for those with moderate to no computational experience who may lead or collaborate with informatics experts. However this material is also applicable to informatics experts working with others who have less computational experience. Curriculum: We will provide you with an awareness for the specific challenges that your informatics collaborators, employees, and mentees might face, as well as ways to mitigate these challenges. By creating a better work environment for your informatics research team, you will ultimately improve the potential impact of your work. We will also discuss the major pitfalls of informatics research and discuss best practices for performing informatics research correctly and well, so that you can get the most out of your informatics projects. This course is part of a series of courses for the Informatics Technology for Cancer Research (ITCR) called the Informatics Technology for Cancer Research Education Resource. This material was created by the ITCR Training Network (ITN) which is a collaborative effort of researchers around the United States to support cancer informatics and ...
Data Visualization in R with ggplot2
Johns Hopkins University (via Coursera)
Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today. One of the critical tools for data visualization today is the R statistical programming language. Especially in conjunction with the tidyverse software packages, R has become an extremely powerful and flexible platform for making figures, tables, and reproducible reports. However, R can be intimidating for first time users, and there are so many resources online that it can be difficult to sort through without guidance. This course is the second in a specialization in Data Visualization offered by Johns Hopkins. It is intended for learners who have either have some experience with R and data wrangling in the tidyverse or have taken the previous course in the specialization. The focus in this course learning to use ggplot2 to make a variety of visualizations and to polish those visualizations using tools within ggplot as well as vector graphics editing software. The course will not go into detail about how the data management works behind the scenes.
Investigating Epidemics like COVID-19: An Analyst's Guide
Johns Hopkins University (via Coursera)
Do you want to learn how to detect, identify the cause, and decrease the morbidity and mortality from outbreaks or pandemics like COVID-19? Are you considering a career in public health practice, but aren’t sure how health departments collect and use outbreak data? Are you working in public health, but interested in moving into analytical and/or technical roles or curious how health departments investigate outbreaks? If so, this course is for you. After taking this course you be able to define key terms related to outbreaks and describe how surveillance data are collected and analyzed to detect outbreaks. You will be able to create epidemic curves and draw conclusions about transmission and cause from the shape of the curve and median incubation period. You will be able to describe the steps to investigating outbreaks and use that knowledge to guide an outbreak investigation. Using statistical software or excel, you will be able to identify demographic and geographic disparities and key exposures, and to calculate secondary attack rates. You will be able to quantify associations between health outcomes and key exposures using odds ratios and confidence intervals and to interpret and use findings to inform public health responses.
Wrangling Data in the Tidyverse
Johns Hopkins University (via Coursera)
Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This course addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively. The key goal in data wrangling is transforming non-tidy data into tidy data. This course covers many of the critical details about handling tidy and non-tidy data in R such as converting from wide to long formats, manipulating tables with the dplyr package, understanding different R data types, processing text data with regular expressions, and conducting basic exploratory data analyses. Investing the time to learn these data wrangling techniques will make your analyses more efficient, more reproducible, and more understandable to your data science team. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
Teaching Writing Final Project
Johns Hopkins University (via Coursera)
One of the goals of the Teaching Writing specialization has been to help every learner consider ways to adapt what they are learning and apply it to their specific situation, needs and interests. The theories, strategies and practices presented in these courses are sound, and can work with any student of any age and skill level, provided each learner is able to adapt their learning and apply it to their specific students, current or future. In this final project, learners will select one component from each of the four courses that are among the most important things they learned from that course. They will describe what these components are, explain why they are important to the learner, and create a plan for incorporating that new learning into their teaching or their own writing going forward.
Strategies for Assisted Living Communities during COVID-19
Johns Hopkins University (via Coursera)
SARS-CoV-2, the virus that causes COVID-19, poses a high risk for assisted living communities due to residents’ age, health status, and communal living environment. The COVID-19 response has largely focused on nursing homes, leaving assisted living communities in the United States with fragmented guidance on how to respond to COVID-19 challenges. This course provides comprehensive instruction and resources for administrators and direct-care staff of assisted living communities. Learners will hear from experts about best practices to prevent COVID-19 outbreaks and promote well-being. Topics discussed include development of emergency preparedness plans, infection and outbreak prevention, staffing considerations, testing, and contact tracing. The course also covers strategies for communicating with stakeholders, promoting resident and staff well-being, and leveraging health departments and other agency resources, including a collection of resources for COVID-19 vaccination guidance. Learners are encouraged to concurrently develop and enhance their own community’s policies, procedures, and practices. This course was developed in partnership with the Baltimore City Health Department.
Advanced Reproducibility in Cancer Informatics
Johns Hopkins University (via Coursera)
This course introduces tools that help enhance reproducibility and replicability in the context of cancer informatics. It uses hands-on exercises to demonstrate in practical terms how to get acquainted with these tools but is by no means meant to be a comprehensive dive into these tools. The course introduces tools and their concepts such as git and GitHub, code review, Docker, and GitHub actions. Target Audience The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research. It is the follow up course to the Introduction to Reproducibility in Cancer Informatics course. Learners who take this course should: Have some familiarity with R or Python Have take the Introductory Reproducibility in Cancer Informatics course Have some familiarity with GitHub Motivation Data analyses are generally not reproducible without direct contact with the original researchers and a substantial amount of time and effort (BeaulieuJones, 2017). Reproducibility in cancer informatics (as with other fields) is still not monitored or incentivized despite that it is fundamental to the scientific method. Despite the lack of incentive, many researchers strive for reproducibility in their own work but often lack the skills or training to do so effectively. Equipping researchers with the skills to create reproducible data analyses increases the efficiency of everyone involved. Reproducible analyses are more likely to be understood, applied, and replicated by others. This helps expedite the scientific process by helping researchers avoid false positive dead ends. Open source clarity in reproducible methods also saves researchers' time so they don't have to reinvent the proverbial wheel for methods that everyone in the field is already performing. Curriculum The course includes hands-on exercises for how to apply reproducible code concepts to their code. Individuals who take this course are encouraged to complete these a...
Public Health in Humanitarian Crises 1
Johns Hopkins University (via Coursera)
This course, Public Health in Humanitarian Crises 1, introduces discussions about the public health approach to problems experienced by people affected by disasters, both natural and related to conflict. The course discusses the many changes which occur in people’s lives when they are uprooted by a disaster, ranging from changes in disease patterns, access to health care, livelihoods, shelter, sanitary conditions, nutritional status, etceteras. We will explore what humanitarian interventions could look like if we want to mitigate the effects of disasters. The course content is a mix of theoretical knowledge and many practical examples from recent disasters. We think this course is unique because it contains so many practical ‘real-life’ examples and is taught by instructors and guest lecturers who together have over 200 years of experience in this field. The course consists of 10 modules totaling approximately 9-10 hours of delivered content with an additional 2-3 hours of self-work (quizzes and writing and evaluating a short peer-review assignment) as well as lively discussions forums. The course has been designed in a way that each module builds on the lessons of previous modules. However, you may do the modules in any order and some can be done separately. You do not have to pay for this course if you choose to enroll without a certificate. Sometimes referred to as auditing, enrolling without a certificate means that you will have access to all of the videos, quizzes, assignments, and discussions. The only difference is that you will not receive a certificate upon completion. Click the Enroll Without a Certificate link to sign up and begin the course. Even if you enroll in a session that has yet to begin, you may access most of the course materials right away by clicking the Preview Course Materials link. However, you will have to wait for the session to begin before posting your comments on the discussion forum or accessing the final peer-reviewed assessment...
Influencing without Authority
Johns Hopkins University (via Coursera)
Influence is a critical leadership skill - especially when you don’t have formal authority. In this course, learners develop practical strategies to coach, motivate, and influence peers, stakeholders, and cross-functional teams to achieve shared goals. Through real-world frameworks and applied scenarios, learners explore how to build a coaching culture, foster psychological safety, and communicate with clarity and credibility. The course introduces structured coaching processes, including goal setting, feedback, and performance conversations, alongside proven influence tactics grounded in power, persuasion, and social dynamics. Learners will practice using tools such as coaching frameworks, reflection journals, and influence mapping to guide conversations and decision-making. By the end of the course, learners will be able to lead through influence, navigate complex workplace relationships, and drive alignment without relying on positional authority. This course is ideal for professionals seeking to strengthen leadership, coaching, and stakeholder management skills in modern organizations.
Pillar #2: Drug Utilization - Drivers and Consequences
Johns Hopkins University (via Coursera)
The determinants of drug utilization are complex and multifactorial, and understanding how, why, when and where drugs are used is crucial to inform regulatory and payment policy and clinical practice. We begin by reviewing methods of investigating drug utilization and evaluating interventions to prescribing. We then explore common challenges to optimizing medicine use, including non-adherence and off-label drug use. We also consider methods to improve the quality of drug utilization, including value-based insurance designs, audit and feedback, patient education and medication therapy management. We also consider varied patient, provider, practice and system-level determinants of prescription drug utilization, including marketing and promotion, emerging evidence of benefits and harms, regulation and changes in coverage and reimbursement. Finally, we use four case studies to reinforce many of the core topics we’ve explored: antibiotics, prescription opioids, anticoagulants and generics and biosimilars.
Data Science Decisions in Time: Using Causal Information
Johns Hopkins University (via Coursera)
This is the fourth course in the specialization and is aimed at those with basic knowledge of statistics, probability and linear algebra. It will prove to be especially interesting for those with datasets that are being used to make decisions: either business, medical, or technology based.
Black Agricultural Solutions to Food Apartheid: A Teach-Out
Johns Hopkins University (via Coursera)
Black Agricultural Solutions to Food Apartheid is a series where we dive deep into the historical, ancestral, and spiritual connections that Black people have to land and agriculture. Throughout this course, we encourage participants to learn about their ancestral foodways, agrarian practices, and spiritual connections. These sessions share wisdom and highlight the importance of food sovereignty, rebuilding community, and land based living. Participants will gain a deeper understanding of the spiritual and ancestral relationships that many Black people have to a higher power, land, plants, and each other. Participants will walk away from this series with an understanding of the many benefits of gardening and farming, including but not limited to social capital, collective agency, physical wellbeing, deepened spiritual connections, community resilience, economic autonomy, organizing, mobilizing, and improved mental and emotional health. Participants will leave this session with the desire to learn more about their own familial relationships to food and land, as well as a greater understanding of Black agriculture.
Documentation and Usability for Cancer Informatics
Johns Hopkins University (via Coursera)
Introduction: Cancer datasets are plentiful, complicated, and hold information that may be critical for the next research advancements. In order to use these data to their full potential, researchers are dependent on the specialized data tools that are continually being published and developed. Bioinformatics tools can often be unfriendly to their users, who often have little to no background in programming (Bolchini et al. 2008). The usability and quality of the documentation of a tool can be a major factor in how efficiently a researcher is able to obtain useful findings for the next steps of their research. Increasing the usability and quality of documentation for a tool is not only helpful for the researcher users, but also for the developers themselves – the many hours of work put into the product will have a higher impact if the tool is usable by the target user community. 70% of bioinformatics tools surveyed by Duck et al. (2016) were not reused beyond their introductory publication. Even the most well-programmed tool will be overlooked by the user community if there is little to no user-friendly documentation or if they were not designed with the user in mind. Target Audience: The course is intended for cancer informatics tool developers, particularly those creating tools as a part of the Informatics Technology Cancer Research. Learning Objectives: 1. Understanding why usability and documentation is vital 2. Identifying your user community 3. Building documentation and tutorials to maximize the usability of developed tools 4. Obtaining feedback from your users Curriculum: This course will demonstrate how to: Understanding why usability and documentation is vital, Identifying your user community, Building documentation and tutorials to maximize the usability of developed tools, Obtaining feedback from your users The course includes a hands-on exercises with templates for building documentation and tutorials for cancer informatics tools. Individuals who...
Community Change in Public Health
Johns Hopkins University (via Coursera)
In bringing about behavior change in public health, we often focus on the individual mother, student, or farmer. We should not forget the community structure and norms constrain for encouraging individual health behaviors. This course examines the community context of the changes needed to promote the public’s health. We begin by examining the various definitions of ‘community’ and the processes by which we ‘diagnose’ or seek to understand the structure and characteristics of different types of communities. An appreciation of community similarities and differences is necessary lest we fall into the trap of designing one-size-fits-all interventions. We need to recognize that no matter that outsiders may view a community as poor or neglected, we can find strengths and capacities for improvement in each community. Identifying community capacities and resources is the first step in facilitating community change. Different practical and philosophical approaches to change and therefore, examined. Specific to the change process is our recognition of the need for communities to participate in the design, implementation and evaluation of any intervention. We examine the concept of participation in an effort to see how different levels of involvement may affect sustainability of community change efforts. Finally a case study of a community participatory approach to onchocerciasis control in Africa is presented. Community Directed Intervention has subsequently been successfully applied to providing other essential primary health care services by and in the community, such as insecticide treated bednets, malaria treatment, vitamin A distribution, deworming medicines, and pneumonia and diarrhea case management.
Single Page Web Applications with AngularJS
Johns Hopkins University (via Coursera)
Do you want to write powerful, maintainable, and testable front end applications faster and with less code? Then consider joining this course to gain skills in one of the most popular Single Page Application (SPA) frameworks today, AngularJS. Developed and backed by Google, AngularJS is a very marketable skill to acquire. In this course, we will explore the core design of AngularJS 1.x (latest version of AngularJS 1), its components and code organization techniques. We will enhance the functionality of our web app by utilizing dependency injection to reuse existing services as well as write our own. We will create reusable HTML components that take advantage of AngularJS data binding as well as extend HTML syntax with a very powerful feature of AngularJS called directives. We’ll set up routing so our SPA can have multiple views. We will also learn how to unit test our functionality. At the end of this course, you will build a fully functional, well organized and tested web application using AngularJS and deploy it to the cloud.
Advanced Communication Strategies
Johns Hopkins University (via Coursera)
This course "Advanced Communication Strategies" equips learners with advanced communication skills needed to navigate complex technical and business settings. By understanding the dynamics of both constructive and destructive conflict, students will develop strategies to resolve issues, foster innovation, and enhance team performance. Learners will also explore ethical principles and apply them to workplace communication, ensuring that messaging is both impactful and responsible. What makes this course unique is its focus on real-world applications, including negotiation techniques and the power of storytelling to communicate organizational strategy. Students will engage in interactive exercises, such as team brainstorming and conflict resolution scenarios, preparing them to lead with confidence. Upon completing the course, learners will be able to manage communication in diverse settings, drive organizational success, and enhance their leadership effectiveness.
Python for Genomic Data Science
Johns Hopkins University (via Coursera)
This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.
Advanced Techniques in Data Visualization
Johns Hopkins University (via Coursera)
In the course "Advanced Techniques in Data Visualization", you will explore advanced data visualization techniques that will elevate your ability to communicate complex data. Building upon foundational skills, you’ll learn to harness the power of color, interactivity, and specialized visualization methods, such as hierarchical structures, networks, and geospatial data. The course covers the essential role of color theory in visualization, teaching you how to enhance data clarity and accessibility. You will also dive into the world of interactive visualizations, gaining practical experience in creating user-driven data experiences. As you explore hierarchical and network visualizations, you'll discover how to represent complex relationships in a way that is easy to understand. The course will guide you through the principles of mapping data, allowing you to transform spatial data into compelling visual narratives. Finally, you will learn to visualize textual data, uncovering patterns and insights that might otherwise remain hidden. With hands-on experience using popular tools such as Tableau and Power BI, this course prepares you to create sophisticated, effective, and impactful visualizations for any audience.
Enfoques emergentes para medir la salud de la población
Johns Hopkins University (via Coursera)
En este curso, conocerá las técnicas tradicionales y emergentes en la recopilación de datos a nivel de población que se pueden utilizar para fortalecer los programas y políticas de salud pública. Expertos de todo el mundo definirán y explicarán conceptos clave en el diseño y la implementación de encuestas basadas en la población, centrándose en el uso de técnicas de encuesta emergentes, como encuestas por teléfonos celulares e internet, datos de servicios de salud y sistemas de información de salud, y registros de salud basados en la población. Aprenderá cómo se pueden usar estos datos para fundamentar la toma de decisiones a nivel de la población y la aplicación de una perspectiva de género y equidad para garantizar que estén siguiendo y respondan a las necesidades de las poblaciones. Nuestros objetivos generales para el curso son apoyar la recopilación de datos de salud a nivel de la población, rastrear tendencias, planificar intervenciones y mejorar el monitoreo de los principales factores de riesgo de muerte prematura, especialmente por enfermedades no transmisibles. El curso es el resultado de una colaboración entre múltiples socios, incluidos Vital Strategies, los Centros para el Control y la Prevención de Enfermedades (CDC), la Escuela de Salud Pública Bloomberg de Johns Hopkins, el Ministerio de Salud de Ruanda, las Naciones Unidas, la Universidad de Colombo, Sri Lanka y la Organización Mundial de la Salud. Este curso fue financiado por Bloomberg Philanthropies, con cofinanciación del gobierno australiano y la Fundación Bill y Melinda Gates.
Quantifying Relationships with Regression Models
Johns Hopkins University (via Coursera)
This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables. We’ll begin by exploring the components of a bivariate regression model, which estimates the relationship between an independent and dependent variable. Building on this foundation, we’ll then discuss how to create and interpret a multivariate model, binary dependent variable model and interactive model. We’ll also consider how different types of variables, such as categorical and dummy variables, can be appropriately incorporated into a model. Overall, we’ll discuss some of the many different ways a regression model can be used for both descriptive and causal inference, as well as the limitations of this analytical tool. By the end of the course, you should be able to interpret and critically evaluate a multivariate regression analysis.
Application of Health Equity Research Methods for Practice and Policy
Johns Hopkins University (via Coursera)
Intended for students who have completed the introduction to health equity research course and/or have previous experience working in this area. This course will cover innovative methods, practical tools, and skills required to conduct rigorous health equity research and translate evidence-based strategies into practice and policy. Covers topics ranging from conceptual frameworks for stakeholder engagement and behavioral intervention development, to adapting interventions for socially-at-risk populations, and research methods in healthcare services and social epidemiology.
Confronting Gender Based Violence: Global Lessons for Healthcare Workers
Johns Hopkins University (via Coursera)
This course introduces participants from the healthcare sector to gender based violence (GBV), including global epidemiology of GBV; health outcomes; seminal research; and clinical best practices for GBV prevention, support, and management. A core curriculum is supplemented by lectures that contextualize the content with specific examples and programs from around the world. The core curriculum introduces learners to a global perspective on gender based violence (GBV), and includes a review with Dr. Claudia Garcia-Moreno of the new WHO guidelines on responding to violence. Students who wish to receive Honors Recognition will complete the honors module, which expands on the core material and highlights special circumstances and programs. This is an in-depth course with 2 components: 1) Core curriculum introduces GBV from a global perspective, with an emphasis on ensuring a strong health sector response to GBV and teaching key competencies for social workers, physicians, nurses, midwives, community health workers, counselors, and other healthcare workers. Completion of the core content is required for students to pass the course. 2) Honors curriculum offered by experts from around the world helps students dive deeper into certain issues, and touches on unique populations and specialized topics. Completion of Honors curriculum is required for those students who wish to receive a Certificate of Accomplishment with Honors. After taking the course, students will be able to: ● Describe the global epidemiology of leading forms of GBV and the evidence linking GBV to poor health. ● Articulate the challenges, strategies, and WHO guidelines for integrating GBV response within the health sector. ● Describe the components of a comprehensive clinical assessment, treatment, and management of a GBV survivor. ● Describe the appropriate psychosocial support and management of a GBV survivor. Module 1 – Introduction to GBV- Epidemiology and Health Impact GBV comes in a variety of ...
Calculus through Data & Modelling: Series and Integration
Johns Hopkins University (via Coursera)
This course continues your study of calculus by introducing the notions of series, sequences, and integration. These foundational tools allow us to develop the theory and applications of the second major tool of calculus: the integral. Rather than measure rates of change, the integral provides a means for measuring the accumulation of a quantity over some interval of input values. This notion of accumulation can be applied to different quantities, including money, populations, weight, area, volume, and air pollutants. The concepts in this course apply to many other disciplines outside of traditional mathematics. Through projects, we will apply the tools of this course to analyze and model real world data, and from that analysis give critiques of policy. Following the pattern as with derivatives, several important methods for calculating accumulation are developed. Our course begins with the study of the deep and significant result of the Fundamental Theorem of Calculus, which develops the relationship between the operations of differentiation and integration. If you are interested in learning more advanced mathematics, this course is the right course for you.
Introduction to Social Computing
Johns Hopkins University (via Coursera)
The course, "Introduction to Social Computing" offers a comprehensive exploration of the intersection between technology and society, equipping learners with essential skills in social media analytics and influence. By covering a range of topics from data pre-processing to feature extraction and model evaluation, students will gain practical experience in applying machine learning techniques to real-world social media scenarios. Through hands-on modules, learners will delve into the dynamics of socio-technical systems and responsible AI, understanding how digital platforms shape human interactions and behaviors. The unique combination of theoretical insights and practical applications prepares students to navigate the complexities of social media, including analyzing firestorms and mitigating misinformation. What sets this course apart is its focus on gamification and cognitive biases in online environments, providing students with innovative strategies to enhance user engagement and promote critical thinking. Whether you are looking to advance your career in tech or simply understand the social implications of technology, this course will empower you to effectively analyze and leverage social computing in today’s digital landscape.
Bioconductor for Genomic Data Science
Johns Hopkins University (via Coursera)
Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.