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.

Health Equity Research & Practice: Local & Global Lessons


Decision Making and Ethical Reasoning

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.


Systems Science and Obesity

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

Jessica Jones-Smith
Rahmat Beheshti
Daniel Taber
Kayla de la Haye
Thomas Glass


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 equity.

6. Introduce emerging approaches to the digitization of population health programs, including the use of advanced analytics and artificial intelligence, and critically examine their potential value, limitations, and implications for decision-making, equity, and health system performance.

The course is targeted at public health program planners, policymakers, and implementers who are engaged in decision-making around when and how to digitize population health programs. Learners do not need a technology or an engineering background or experience.

Digitizing Population Health in Low-Resource Settings

Smisha Agarwal
Madhu Jalan
shivani pandya
Katya Saksena


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.

Major Depression in the Population: A Public Health Approach

William Eaton, PhD
Wietse A. Tol, PhD
Ramin Mojtabai, MD


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.

You can go at your own pace! We recommend 2 weeks of study at 5 hours/week = ~10 hours total

Ongoing U.S. Settler Colonialism & Native Peoples Teach-Out

Melissa Horner
Shannon Frattaroli, PhD, MPH


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

Sustainable Transportation Networks and Streetscapes


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 partisan bias to minority vote dilution, and explore strategies for promoting fairness, transparency, and integrity in electoral processes.

Through engaging lectures, interactive discussions, and hands-on exercises, participants will develop a deep understanding of voting theory, electoral processes, and the challenges facing modern democracies. Whether you're a student, a policymaker, or a concerned citizen, join us on this transformative journey as we work towards a more inclusive, equitable, and vibrant democracy for all.

The Mathematics of Democracy, Politics and Manipulation


Advanced Cybersecurity Topics

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.


Bioconductor for Genomic Data Science

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.


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.


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.

Data Science Decisions in Time: Using Data Effectively


The Data Science of Health Informatics

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.

Hadi H. K. Kharrazi, MD, Ph.D
Sam Meiselman


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.

Trustworthy AI: Managing Bias, Ethics, and Accountability


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.

Child Housing Assessment for a Safer Environment Teach-Out


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.

Analysis and Interpretation of Large-Scale Programs

Agbessi Amouzou
Abdoulaye Maïga
Yoko Inagaki, MD
Elizabeth Hazel, PhD
Rosemary Morgan
Neff Walker
Melinda K. Munos


Advanced Network Security and Analysis

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 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.

Honors Algebra 2: Linear and Quadratic Functions


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.

Fundamentals for Implementing a Hypertension Program


Regression Models

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.

Brian Caffo, PhD
Roger D. Peng, PhD
Jeff Leek, PhD


Foundations of Health Equity Research

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.

Lisa A. Cooper, M.D.
Tanjala S. Purnell, PhD MPH


Modeling Data in the Tidyverse

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.

Shannon Ellis, PhD
Stephanie Hicks, PhD
Roger D. Peng, PhD
Carrie Wright, PhD


Precalculus: Mathematical Modeling

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.


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.

Program Design & Evaluation for Health Systems Strengthening


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.

Surveillance Systems: Analysis, Dissemination, and Special Systems


Управление анализом данных

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

1 учебная неделя, 4–6 часов

Jeff Leek, PhD
Brian Caffo, PhD
Roger D. Peng, PhD


Foundations of Neuroscience

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.


Public Health in Humanitarian Crises 1

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. Visit the Learner Help Center for details about session schedules.

If you want to learn more, there is a second course which follows this one entitled, Public Health in Humanitarian Crises 2. We welcome you to take this. The course has a similar set-up and discusses additional topics, such as infectious disease outbreaks, disease epidemiology, maternal and newborn health, mental health, humanitarian project design, humanitarian principles, and many other topics.


HDFS Architecture and Programming

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.


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.


Wrangling Data in the Tidyverse

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.


The Creative Leader

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


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.

Clinical Trials Analysis, Monitoring, and Presentation

Janet Holbrook, PhD, MPH
Elizabeth A. Sugar, PhD
David M. Shade, JD


Leading Change in Health Informatics

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.


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.

Measuring and Maximizing Impact of COVID-19 Contact Tracing

Emily Gurley, PhD, MPH
Elizabeth C. Lee, PhD
Lucy D’Agostino McGowan
Kyra Grantz
Justin Lessler, PhD, MS


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.

A Public Health Approach to Hearing Loss and Aging

Jennifer A. Deal, PhD
Nicholas S. Reed, AuD
Frank R. Lin, MD, PhD


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.

Setting the Stage for Success: An Eye on Safety Culture and Teamwork (Patient Safety II)


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.

Operations and Patient Safety for Healthcare IT Staff

Ashwini S. Davison, M.D.
Sylvia Devlin, (R) (M) (QM), CIIP


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.

Calculus through Data & Modeling: Applying Differentiation


Excellence in Online Teaching

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.

Paul Huckett
Michael J. Reese
Dr. Olysha Magruder


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.

Planning a Patient Safety or Quality Improvement Project (Patient Safety III)


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.

Managing AI Projects: From Strategy to Delivery


Teaching LGBTQ+ Health

This curriculum is designed for faculty members and health professions educators. The course goals are to improve your knowledge, teaching skills, and attitudes pertaining to the provision of health care to LGBTQ+ patients.e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.


Essentials of Palliative Care

This course starts you on your journey of integrating primary palliative care into your daily lives.  You will learn what palliative care is, how to communicate with patients, show empathy, and practice difficult conversations. You will learn how to screen for distress and provide psychosocial support. You will learn about goals of care and advance care planning and how to improve your success with having these conversations with patients.  Finally, you will explore important cultural considerations and improve your cultural competency on the topics covered.

For clinicians, the goal of this course is to help you incorporate primary palliative care into your daily practice or help you know when to seek a specialist.  We will help you improve your patient’s quality of life and provide self-care tips to help you maintain your own. For patients and caregivers, this course will empower you to talk to your provider and get palliative care, if necessary.

Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.


Love as a Force for Social Justice

The objectives of this course are:

-To introduce participants to different concepts of love, to empower them to be conscious of the power of love and the possibility of practicing it in everyday life, and to highlight in particular the idea of love as a force for social justice.

-To communicate a sense of personal strength and empowerment by actively learning from each other and beginning to define how participants can apply their learning in service to society.

This course will explore the concept of agape love (compassion/kindness) as a force for social justice and action and as the inspiration for service and the application of knowledge to positive social change. Biological, psychological, religious, and social perspectives of love will be discussed, drawing on the expertise of people from a variety of disciplines.

During the six-week course, the following topics will be raised and discussed: kinds of love/defining love; non-violent communication; love and the biology of the brain; love as a basic concept of religious and ethical beliefs (e.g., Judaism, Christianity, Islam, Buddhism, Gandhian); love applied in action, and poetic expressions of love as a social force. This curriculum aims to foster a sense of the importance of love as a key phenomenon in creating community, connection, and functional societies among humans.

Course materials will draw from a variety of sources. One of the goals of the class is to provide participants with some knowledge of the literature of love, and readings for the course are listed in the outline of the course on the pages that follow.


Child Nutrition and Cooking

Eating patterns that begin in childhood affect health and well-being across the lifespan. The culture of eating has changed significantly in recent decades, especially in parts of the world where processed foods dominate our dietary intake. This course examines contemporary child nutrition and the impact of the individual decisions made by each family. The health risks associated with obesity in childhood are also discussed. Participants will learn what constitutes a healthy diet for children and adults and how to prepare simple, delicious foods aimed at inspiring a lifelong celebration of easy home-cooked meals. This course will help prepare participants to be the leading health providers, teachers and parents of the present and future.The text and other material in this course may include the opinion of the specific instructor and are not statements of advice, endorsement, opinion, or information of Stanford University.


Cooking for Busy Healthy People

Cooking is one of the most powerful ways in which we can optimize our enjoyment of great quality food while protecting our health. Even on a tight budget, cooking can be a cost-effective, joyful and rewarding way to love the food that will love us back for a lifetime. In this course, you’ll learn some basic recipes from a home cook and two professional chefs who prioritize healthful eating. You’ll also learn some of the fundamentals of principle-based cooking that can help you break free from the chains of having to follow recipes exactly. Better health and creative expression lie in the ability to improvise in the kitchen, using whatever is available to make tasty, simple meals. We can’t wait to welcome you into our kitchens in this mouth-watering course! Here’s to your health and the home cooking that can support it!

Special thanks to course contributors: Israel Garcia, Jacopo Beni, Jesper Baanghaell, Sejal Parekh, William Bottini, Perry Pickert and Friday Films


The AI, Climate, and Energy Connection

This course will help you understand AI's climate implications and identify practical next steps within your organization. The course begins with demystifying the connection between AI, Large Language Models (LLMs), data centers, and energy and water demand. Then you will learn about AI's environmental footprint, the related environmental and community impacts, you will evaluate real-world applications of AI across climate adaptation, energy transition, and nature conservation, and understand the business and policy landscape shaping corporate decisions.

Upon completion, you will have built an Impact Plan identifying actions you can take, whether in procurement (adding sustainability criteria to vendor RFPs), operations (optimizing AI workload scheduling), or strategy (leveraging government incentives for renewable-powered infrastructure).

What makes this unique:
Stanford faculty research combines with industry experts’ insights. You will examine real implementations such as Google's wind forecasting improving renewable value, Brazil's satellite-based deforestation monitoring enabling supply chain compliance, and Sweden's AI building optimization, and then apply frameworks to your context through role-based activities.

The course balances business strategy and tactics with policy context. You will finish with concrete next steps captured in your Impact Plan, not just abstract knowledge.

Ines M. L. Azevedo
William Abraham Tarpeh


This course is designed to help learners around the world become more sustainable eaters. Course videos can be watched in any order. Feel free to explore special areas of interest by skipping ahead and coming back to less familiar topics at a later stage. Together, we’ll explore key topics, like how food production impacts the environment and why meat production and protein consumption are often at the center of the debate around sustainability. We’ll introduce the pros and cons of different kinds of agriculture, fishing and food packaging, with a focus on how we can make more environmentally friendly decisions on a daily basis. We’ll also look ahead and explore some of the technology innovations that could become increasingly important as we look at the future of food for a growing global population. If this is the first course you’ve ever taken on food and sustainable eating, you’ll come away with concrete tips for how you can make food choices that will protect the world we hand over to the next generation. Our planet needs many people making small changes in the right direction and we’re here to help with that. If you’re an expert in food sustainability, we hope to offer you some tools that could help you to communicate key messages to others in simple, digestible ways. Whatever your level, we hope you’ll join this discussion as we explore, together, the ways in which we can all become more sustainable eaters.

The beautiful story animations were scripted by Lucas Oliver Oswald and animated by Janine Van Schoor.

Special thanks to: Lucas Oliver Oswald, William Bottini, Desiree Labeaud, Christopher Gardner, Sejal Parekh, Arielle Wenokur, Janine Van Schoor, Ann Doerr, Perry Pickert and the fantastic team at Friday Films.

Introduction to Food and Our Environment


Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

This course is the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

Probabilistic Graphical Models 2: Inference


This course provides a comprehensive exploration of building decarbonization, equipping professionals with the knowledge and strategies needed to achieve sustainable, energy-efficient buildings. Through expert-led modules, participants will learn how to use energy efficient, electrification, renewable energy, low embodied carbon materials, and market-based approaches in the design of the next generation of high performance buildings. Featuring case studies from the US, Asia, the EU, and global leaders, the course highlights practical approaches, emerging technologies, and policy frameworks to guide building managers, developers, and policymakers toward impactful, scalable solutions for a low-carbon future.
In this course you will hear from industry experts including Dr. Roshan Mehdizadeh Corsiglia, Senior Director at Google. Dr. Corsiglia is also a Stanford Adjunct Lecturer in Civil and Environmental Engineering. You will also gain insights from global experts including world-renowned Amory Lovins, Co-founder and Chairman Emeritus and former Chief Scientist of Rocky Mountain Institute (RMI), where he continues to collaborate. Lovins is also a Senior Precourt Scholar for Integrative Design and Energy Efficiency at Stanford University.

Building Decarbonization and the Clean Energy Transition


Introduction to Statistics

Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.

Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.

This course will take approx. 6-8 hours to complete, depending on prior knowledge and experience.


The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).

Shortest Paths Revisited, NP-Complete Problems and What To Do About Them


This course focuses on women’s health and human rights issues from infancy through old age, including information about positive interventions relating to those issues.  Learners are encouraged to interact with each other through interactive discussions.

It is important to us that this course be available to all learners.  We encourage you to apply for Coursera's financial aid (see link to left) if the cost of the course certificate is difficult for you to afford.  Please note that you may view all materials in this course, and participate in it, without purchasing a certificate.

The course was co-created by Consulting Professor Anne Firth Murray and Kevin Hsu. Anne Firth Murray is the Content Director of the course; Kevin Hsu is the Design Director of the course.

Depending on your pace, this course can be completed in 8-11 weeks.

International Women's Health and Human Rights


This course presents basic principles of cancer survivorship to primary-care physicians. Developed by a team of experts in caring for cancer survivors, and narrated by a primary-care physician, this course provides practical tips and tools that can be easily integrated into medical practice.

You will learn about the complex physical and psychosocial needs and concerns of the growing number of cancer survivors, along with the key role that primary care physicians have in guiding these patients back to health, after cancer.

Materials include story-based videos where you will meet four patients with diverse needs in their care after recovery for cancer; printable reference guides for clinical care, communication, and resources; as well as additional optional cases for extended learning.

This self-paced course takes approximately 90 minutes to complete. If you are interested in taking this course for CME credit, please visit the course site at Stanford Center for Continuing Medical Education located here:
https://stanford.cloud-cme.com/course/courseoverview?P=0&EID=35509

Health After Cancer: Cancer Survivorship for Primary Care


Game Theory II: Advanced Applications

Popularized by movies such as "A Beautiful Mind", game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents.  Over four weeks of lectures, this advanced course considers how to design interactions between agents in order to achieve good social outcomes. Three main topics are covered:  social choice theory (i.e., collective decision making and voting systems), mechanism design, and auctions.

In the first week we consider the problem of aggregating different agents' preferences, discussing voting rules and the challenges faced in collective decision making. We present some of the most important theoretical results in the area: notably, Arrow's Theorem, which proves that there is no "perfect" voting system, and also the Gibbard-Satterthwaite and Muller-Satterthwaite Theorems.  We move on to consider the problem of making collective decisions when agents are self interested and can strategically misreport their preferences. We explain "mechanism design" -- a broad framework for designing interactions between self-interested agents -- and give some key theoretical results. Our third week focuses on the problem of designing mechanisms to maximize aggregate happiness across agents, and presents the powerful family of Vickrey-Clarke-Groves mechanisms.  The course wraps up with a fourth week that considers the problem of allocating scarce resources among self-interested agents, and that provides an introduction to auction theory.

You can find a full syllabus and description of the course here:  http://web.stanford.edu/~jacksonm/GTOC-II-Syllabus.html

There is also a predecessor course to this one, for those who want to learn or remind themselves of the basic concepts of game theory: https://www.coursera.org/learn/game-theory-1

An intro video can be found here:   http://web.stanford.edu/~jacksonm/Game-Theory-2-Intro.mp4

Stanford University
The University of British Columbia

Matthew O. Jackson
Kevin Leyton-Brown
Yoav Shoham


Stories of Infection

This course introduces learners to a variety of infectious diseases using a patient-centered, story-based approach. Through illustrated, short videos, learners will follow the course of each patient’s illness, from initial presentation to resolution. Integrating the relevant microbiology, pathophysiology and immunology, this course aims to engage and entice the learner towards future studies in microbiology, immunology and infectious diseases.

The patient-centered videos included in this course were created as part of the Re-imagining Medical Education initiative, led by Charles Prober MD, Senior Associate Dean of Medical Education at the Stanford School of Medicine. This initiative was the first of its kind to explore the collaborative creation of foundational medical education online content by inter-institutional teams of faculty. The content presented in this course was created by faculty from Stanford University School of Medicine, in collaboration with The University of Washington School of Medicine, Duke University School of Medicine, UCSF School of Medicine, and The University of Michigan Medical School. Support for this initiative was provided by the Robert Wood Johnson Foundation and the Burke Family Foundation.

PLEASE NOTE: Information provided in this course is for educational purposes only and is not intended to be used for diagnostic and/or treatment purposes.


Palliative Care Always Capstone Course

The Palliative Care Always Capstone course is designed to let you test your knowledge about palliative and help others understand the value of palliative care, while showing your creative side. In this course, you will impact community awareness about palliative care, promote self-care and wellness, show-off your communication skills in a virtual environment, and finish the course off by proving your thoughts on ways to offer psychosocial support to a patient and family.


Stanford's Short Course on Breastfeeding was co-created with the Philani Maternal Child Health and Nutrition Trust to support new mothers around the world. This engaging, one-week learning experience, provides participants with everything they need to know to more successfully establish breastfeeding – or support a new mother who has decided to breastfeed. We created the course because we recognize that there is a very small window in which successful, exclusive breastfeeding can be established, and that many new mothers are mastering this skill during a busy and sometimes stressful time. Brought to life by beautiful illustrations and interviews with international mothers, we hope to reach the broadest spectrum of mothers, helping them understand the current recommendations, challenges, benefits and practical considerations around breastfeeding -  while simultaneously inspiring them to consider breastfeeding as the first choice for feeding their babies.

And now for the legal stuff...

Disclaimer of Warranty and Limitation of Liability

THE INFORMATION IN THIS COURSE IS PROVIDED "AS IS" WITHOUT ANY REPRESENTATION, OR WARRANTIES, EXPRESS OR IMPLIED. DIGITAL MEDIC AND STANFORD MEDICINE ARE NOT LIABLE FOR ANY TYPE OF LOSS OR INJURY, OR ANY DAMAGES WHETHER DIRECT OR INDIRECT, ARISING FROM USE OF THIS COURSE. This course is not a substitute for the advice, diagnosis or treatment by an appropriately qualified and licensed physician or other health care provider.

Copyright 2018 Stanford University.

The course videos must be used according to the term of our Creative Commons License available at https://creativecommons.org/licenses/by-nc-nd/4.0/: free distribution with attribution, no commercial use, no derivatives.


AI in Healthcare Capstone

This capstone project takes you on a guided tour exploring all the concepts we have covered in the different classes up till now. We have organized this experience around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID19 seeks care with a primary care provider. We will follow the patient's journey from the lens of the data that are created at each encounter, which will bring us to a unique de-identified dataset created specially for this specialization. The data set spans EHR as well as image data and using this dataset, we will build models that enable risk-stratification decisions for our patient. We will review how the different choices you make -- such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline -- affect the care that would be recommended by the model. During this exploration, we will also discuss the regulatory as well as ethical issues that come up as we attempt to use AI to help us make better care decisions for our patient. This course will be a hands-on experience in the day of a medical data miner.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Matthew Lungren
Nigam Shah
Serena Yeung
Tina Hernandez-Boussard
Laurence Baker


Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

This course is the third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.

Probabilistic Graphical Models 3: Learning


Health Across the Gender Spectrum

This course offers an intimate, story-based introduction to the experiences of six transgender children and their families. Through illustrated stories and short teaching videos, learners will gain a better understanding of gender identity and the gender spectrum. Stanford physicians, K-12 educators, and transgender faculty members offer practical tips for parents, teachers, healthcare providers and anyone who wants to help create a more gender-expansive environment - one in which all people can live authentically. As a global community of unique individuals, we can begin to build a world that is ready to nurture and love each and every child.

Due to the sensitive nature of the story-based course content, we have chosen not to offer course certificates for this course. Simply put, we feel that the thoughts, ideas and sentiments of these remarkable children and their families... are priceless. We are confident that, like us, you will end up learning more from them than you could ever imagine. Together, we can lay a stronger foundation for all children. Join us as we explore health, across the gender spectrum.

Additional note: When submitting answers to quizzes, you may be asked to enter your "full legal name". This feature is primarily for verification purposes for courses that offer a certificate on Coursera, so it does not really apply to this course. We recognize that, for some individuals the name they use does not match the one on their legal documents. Please feel free to enter the name you normally use in these boxes.

Three weeks. Roughly one hour every week.


In this course we will seek to “understand Einstein,” especially focusing on the special theory of relativity that Albert Einstein, as a twenty-six year old patent clerk, introduced in his “miracle year” of 1905. Our goal will be to go behind the myth-making and beyond the popularized presentations of relativity in order to gain a deeper understanding of both Einstein the person and the concepts, predictions, and strange paradoxes of his theory. Some of the questions we will address include: How did Einstein come up with his ideas? What was the nature of his genius? What is the meaning of relativity? What’s “special” about the special theory of relativity? Why did the theory initially seem to be dead on arrival? What does it mean to say that time is the “fourth dimension”? Can time actually run more slowly for one person than another, and the size of things change depending on their velocity? Is time travel possible, and if so, how? Why can’t things travel faster than the speed of light? Is it possible to travel to the center of the galaxy and return in one lifetime? Is there any evidence that definitively confirms the theory, or is it mainly speculation? Why didn’t Einstein win the Nobel Prize for the theory of relativity?

About the instructor: Dr. Larry Lagerstrom is the Director of Academic Programs at Stanford University’s Center for Professional Development, which offers graduate certificates in subjects such as artificial intelligence, cyber security, data mining, nanotechnology, innovation, and management science. He holds degrees in physics, mathematics, and the history of science, has published a book and a TED Ed video on "Young Einstein: From the Doxerl Affair to the Miracle Year," and has had over 30,000 students worldwide enroll in his online course on the special theory of relativity (this course!).

Understanding Einstein: The Special Theory of Relativity


Introduction to Healthcare

Solving the problems and challenges within the U.S. healthcare system requires a deep understanding of how the system works. Successful solutions and strategies must take into account the realities of the current system.

This course explores the fundamentals of the U.S. healthcare system. It will introduce the principal institutions and participants in healthcare systems, explain what they do, and discuss the interactions between them. The course will cover physician practices, hospitals, pharmaceuticals, and insurance and financing arrangements.

We will also discuss the challenges of healthcare cost management, quality of care, and access to care. While the course focuses on the U.S. healthcare system, we will also refer to healthcare systems in other developed countries.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.


Rebuilding Our Relationship with Food

Have you ever made a conscious effort to change the way you eat (for health or other reasons) and then felt frustrated when your plans were derailed? You’re not alone. The best laid plans are often sabotaged by a food environment that makes it increasingly hard to make healthier food choices. This can leave many people feeling mistrustful of food or feeling that our relationship with food is somehow broken. In this course, we’ll explore the history of our changing food environment, the science behind cravings for unhealthy foods AND most importantly, you’ll learn some concrete strategies for rebuilding your relationship with food. You’ll learn to practice mindful eating and self-compassion (proven strategies for supporting healthier food choices) as well as designing a customized plan to protect your relationship with food and improve the health of your greatest asset - you! I can’t wait to start on this adventure together.

Special thanks to: William Bottini, Sejal Parekh, Janine Van Schoor, Ann Doerr, Perry Pickert and the fantastic team at Friday Films.


How Software Ate Finance

Software is eating the world, with radical consequences for financial services. This course gives you a foundation for understanding the future of financial services, and provides guidance for creating fintech businesses in the 2020s and beyond.


The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

Graph Search, Shortest Paths, and Data Structures


Giving 2.0: The MOOC

A philanthropist is anyone who gives anything — time, money, experience, skills, and networks — in any amount, to create a better world. This course will empower you to practice philanthropy more effectively and make your giving more meaningful to both you and those you strive to help.

Giving 2.0: The MOOC, is a Stanford University-sponsored online course intended to teach givers of all ages, backgrounds, incomes and experiences to give more effectively. Taught by social entrepreneur, philanthropist and bestselling author Laura Arrillaga-Andreessen, Giving 2.0: The MOOC will teach you how to assess nonprofits, create a high-impact philanthropic strategy, volunteer more effectively, use existing, free technology for good and more.

Giving 2.0: The MOOC is a six-module course. Each module has a particular theme and 5-10 content-packed and activity-rich, videos exploring that theme. Videos will include lectures from Laura Arrillaga-Andreessen as well as interviews, discussions and lectures given by guest speakers. Guest speakers are renowned leaders in multiple industries including philanthropy, technology and business, who will provide unique insights into course topics. Course participants will have the opportunity to join Talkabouts – small virtual meeting groups created to discuss class-related topics. By the course’s conclusion, course participants will have created an Individual Giving Action Plan to guide their future giving in a highly effective and meaningful way. Course participants will also complete a formal nonprofit assessment and be provided with ongoing, post-MOOC philanthropy education content that will support continued development and execution of their philanthropic goals.

8.5 hours of videos, quizzes, and peer review


Supporting Families and Caregivers

This course takes a deep dive into the challenges families and friends of a patient with serious illness face and how you can care for and support them as a provider, social worker or family friend.  Supporting Families and Caregivers especially focuses on the children of a patient with serious illness and their caregiver, and teaches you the best way to empower them to get the support they need. By the end of this course, you will be able to provide critical avenues of support for the people who are instrumental to your patients care, wellbeing and quality of life.

Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.


With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Evaluations of AI Applications in Healthcare


Around the world, we find ourselves facing global epidemics of obesity, Type 2 Diabetes and other predominantly diet-related diseases. To address these public health crises, we urgently need to explore innovative strategies for promoting healthful eating. There is strong evidence that global increases in the consumption of heavily processed foods, coupled with cultural shifts away from the preparation of food in the home, have contributed to high rates of preventable, chronic disease. In this course, learners will be given the information and practical skills they need to begin optimizing the way they eat. This course will shift the focus away from reductionist discussions about nutrients and move, instead, towards practical discussions about real food and the environment in which we consume it. By the end of this course, learners should have the tools they need to distinguish between foods that will support their health and those that threaten it. In addition, we will present a compelling rationale for a return to simple home cooking, an integral part of our efforts to live longer, healthier lives.

View the trailer for the course here: https://www.youtube.com/watch?v=z7x1aaZ03xU

Stanford Introduction to Food and Health


Learn how to model social and economic networks and their impact on human behavior.  How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors?   We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions.

The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks. Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences.

You can find a more detailed syllabus here:  http://web.stanford.edu/~jacksonm/Networks-Online-Syllabus.pdf

You can find a short introductory videao here: http://web.stanford.edu/~jacksonm/Intro_Networks.mp4

Social and Economic Networks:  Models and Analysis


Cryptography I

Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.


This course should be taken after the Symptom Management course and continues building your primary palliative care skills – communication, psychosocial support, goals of care, and symptom management. You will explore transitions in care such as survivorship and hospice. You will learn how to create a survivorship care plan and how to best support a patient. The course also covers spiritual care and will teach you how to screen for spiritual distress. Finally, you will learn the requirements for hospice care and practice discussions difficult conversations related to end-of-life care.

Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Transitions in Care from Survivorship to Hospice


Introduction to Mathematical Thinking

Learn how to think the way mathematicians do – a powerful cognitive process developed over thousands of years.

Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world. This course helps to develop that crucial way of thinking.

Expect to require at least 10 hours of study per week to complete this course satisfactorily.


Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles.

This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.

The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies.

Co-author: Geoffrey Angus

Contributing Editors:
Mars Huang
Jin Long
Shannon Crawford
Oge Marques

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Fundamentals of Machine Learning for Healthcare


The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).

Divide and Conquer, Sorting and Searching, and Randomized Algorithms


Game Theory

Popularized by movies such as "A Beautiful Mind," game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Beyond what we call `games' in common language, such as chess, poker, soccer, etc., it includes the modeling of conflict among nations, political campaigns, competition among firms, and trading behavior in markets such as the NYSE. How could you begin to model keyword auctions, and peer to peer file-sharing networks, without accounting for the incentives of the people using them? The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more. We'll include a variety of examples including classic games and a few applications.

You can find a full syllabus and description of the course here:  http://web.stanford.edu/~jacksonm/GTOC-Syllabus.html

There is also an advanced follow-up course to this one, for people already familiar with game theory:  https://www.coursera.org/learn/gametheory2/

You can find an introductory video here:  http://web.stanford.edu/~jacksonm/Intro_Networks.mp4


Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

Probabilistic Graphical Models 1: Representation


This course explores how the advances in artificial intelligence can and will transform our economy and society in the near future.

You will hear insights from esteemed AI researchers and industry leaders in technology, economics, and business. The curriculum addresses the technological underpinnings of Generative AI, its profound implications for businesses and the broader economy, and the potential risks associated with AI-driven transformations in the workforce. By the end of this course, you will be equipped with the foresight necessary to navigate the transformative landscape of AI.

Notable guest speakers include Eric Schmidt (former CEO of Google), Laura Tyson (esteemed Berkeley professor and former Director of the National Economic Council), Bindu Reddy (CEO and Co-Founder - Abacus.AI), Alex Wang (Founder of Scale AI), Mira Murati (CTO of OpenAI), Jack Clark (Co-founder of Anthropic), and Mustafa Suleyman (Founder of Inflection.AI). They originally joined a conversation with Erik Brynjolfsson and Sebastian Thrun at Stanford University in Spring 2023, and we believe that their insights will be as valuable to your understanding as they were to the Stanford community. Special thanks to James da Costa for his work on the development of this course.

The AI Awakening: Implications for the Economy and Society


Introduction to Clinical Data

This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.


Writing in the Sciences

This course teaches scientists to become more effective writers, using practical examples and exercises. Topics include: principles of good writing, tricks for writing faster and with less anxiety, the format of a scientific manuscript, peer review, grant writing, ethical issues in scientific publication, and writing for general audiences.