Imagine a world where your photos don't just capture memories, but also become intelligent assistants, helping you navigate and manage daily tasks. Welcome to "GPT Vision: Seeing the World Through Generative AI", a course designed to revolutionize how you interact with the world around you through the lens of Generative AI and photos.

In this course, you will learn to how take a picture of anything and turn it into:

- a recipe
- a shopping list
- DIY plans to make it
- a plan to reorganize it
- a description for a social media post
- organized text for your notes or an email
- an expense report or personal budget entry

This course will teach you how to harness GPT Vision's power to transform ordinary photos into problem-solving tools for your job and personal life. No experience is required, just access to GPT-4(V) Vision, which is part of the ChatGPT+ subscription. Whether it's ensuring you've ticked off every item on your grocery list or creating compelling social media posts, this course offers practical, real-world applications of Generative AI Vision technology.

Social Media Mastery: Learn to create compelling descriptions for your social media photos with AI, enhancing your digital storytelling.

Capture Your Brainstorming: Take a picture of notes on a marker board or napkin and watch them be turned into well-organized notes and emailed to you.

DIY and Culinary Creations: Explore how to use photos for DIY home projects and cooking. Discover how to generate prompts that guide you in replicating or creating dishes from images or utilizing household items for creative DIY tasks.

Data Extraction and Analysis: Gain expertise in extracting and analyzing data from images for various applications, including importing information into tools like Excel.

Expense Reporting Simplified: Transform the tedious task of expense reporting by learning to read receipts and other documents through GPT Vision, streamlining your financial management.

Progress Tracking: Develop the ability to compare photos of the real world with plans, aiding in efficient monitoring and management of project progress, such as how your construction project is progressing.

Knowledge Discovery: Learn about anything you see. Snap a picture, generate a prompt, and uncover a world of information about objects, landmarks, or any item you encounter in your daily life.

Organizational Mastery: Learn how to organize your personal spaces, like closets or storage areas, by using AI to analyze photos and suggest efficient organization strategies and systems.

GPT Vision: Seeing the World through Generative AI


Generative AI for University Leaders

Generative AI is rapidly reshaping industries and the skills that students are going to be expected to have to be employable. As the pace of technological change accelerates, universities must innovate to stay competitive and ensure that they are giving students the skills that they need to be competitive. At the same time, generative AI offers the potential to transform university operations, from automating course scheduling and content creation to streamlining expense reporting and administrative functions.

This course provides university leaders with a foundation to understand enough of the key fundamentals of how generative AI works and what it can do to make effective strategic decisions. The course provides a primer on the innovative potential of generative AI across all disciplines and the opportunity for higher education to lead the way in reshaping how students learn and how we work in the future. It shows what is happening in industry and the strategic opportunities for institutions to focus on to help their students excel in the future of work.

The course also provides the foundation that an institution needs to get started innovating with generative AI, ranging from policy to platform. The course covers the essential platforms and infrastructure for effective generative AI integration in university settings, offering advice on investment priorities, efficient resource distribution, and pinpointing key advocates to create an interdisciplinary culture of innovation across campus.

Dr. Jules White
Jeff Maggioncalda, Coursera CEO
Andrew Ng


This course will examine the music industry in the United States, with the unique perspective gained by Vanderbilt University’s location in Nashville, TN.  Nashville is a major center of music in the United States, and the music created here has a global reach, particularly in the country, rock, pop, singer-songwriter, and Americana genres.  However, students will learn that the basic principles of recording, marketing, copyright, licensing, and live performance are the same, regardless of musical style or genre.

The music business is one of the most rapidly changing industries in the US today.  It is also an industry filled with contradiction, and media headlines and anecdotal stories often add to the confusion.  Here are just a few statements paraphrased from recent news stories:

* The music business has collapsed
* Demand for music is expanding at the greatest rate in history
* Historic recording studios are closing at an alarming rate
* More people are making recordings than ever before
* Musicians usually lose money touring
* Musicians usually make money touring
* Major artist pulls songs off streaming services because they don’t pay fairly
* Major artist makes a fortune from streaming services
* People won’t buy records anymore
* Vinyl record sales are soaring

This course will attempt to make sense of these seemingly contradictory trends and data, outline the basic structure and mechanisms of today’s music industry, and encourage students to think critically and entrepreneurially about the future of music.  Leaders from various areas of the music business will lend their perspectives through in-depth interviews, and footage from a recording session will give learners a behind the scenes look at how a song goes from the spark of an idea hummed into a cell phone to a finished recording.

Participants will grapple with questions about art and commerce which are both timeless and crucially important today, and will emerge from this course with tools allowing them to make more informed decisions as creators, promoters, and consumers of music.

Course launches February 19, 2016.

Understanding the Music Business: What is Music Worth?


Build AI Agents That Actually Work

AI agents are everywhere—but most of them fail in frustrating, unpredictable ways. They get confused, waste tokens, hit dead ends, and require constant babysitting. This course teaches you the patterns and architectures that separate agents that struggle from agents that succeed.

Using TypeScript and the Model Context Protocol (MCP), you'll learn to build AI agents from the ground up—and more importantly, you'll learn why certain designs work while others fall apart.

What You'll Learn:

- Build MCP Tool Servers — Create the bridge that lets AI agents interact with any system: filesystems, databases, APIs, or your own custom tools

- Master the Agent Loop — Understand the universal pattern every AI agent follows: PERCEIVE → DECIDE → ACT → OBSERVE → REPEAT

- Connect agents to tools — Wire up LLMs to discover, select, and execute tools autonomously

The Patterns That Make Agents Reliable:

- Response-as-Instruction — Your tools don't just return data—they guide agent behavior in real-time. Learn to design tool responses that teach the agent what to do next, when to stop, and how to communicate results.

- Failing Forward — Turn errors from dead ends into stepping stones. Design error messages that teach agents how to recover—automatically, without human intervention. For the first time in computing history, your error messages have a reader that can actually do something about them.

- Intelligence Budget — Every token in the context window is precious attention. Learn to maximize signal and minimize noise—pre-digesting data in tools, using scripted orchestration for mechanical work, and reserving the agent's cognitive resources for decisions that actually require intelligence.

AI Agents with Model Context Protocol & Typescript


After taking this course, learners will be able to effectively integrate generative AI tools (Large Language Models, LLMs, like ChatGPT) into their project management workflows to enhance efficiency, communication, and decision-making while maintaining ethical standards and data security.
•	Learners will be able to apply practical AI-driven solutions to automate repetitive tasks and enhance daily project management operations.
•	Learners will be able to use generative AI to improve the accuracy of team communication
•	Learners will be able to identify and address potential ethical concerns and limitations when implementing AI in project management.
This course teaches how to effectively integrate generative AI into project management to automate tasks, enhance communication, and maintain ethical standards. Learners will understand the balance between AI capabilities and human expertise, ensuring responsible and efficient project outcomes.

Integrating Generative AI into Project Management


Generative AI for Leaders

Leaders are constantly seeking innovative tools to amplify their impact. Dive into the realm of Generative AI, a groundbreaking technology poised to redefine the leadership landscape more profoundly than the Internet itself. This course offers a comprehensive journey into understanding, applying, and mastering Generative AI as a robust tool to amplify leadership capabilities.

You will gain hands-on experience that teaches you to use Generative AI for:

- Agenda Architecting: Let Generative AI craft impeccable agendas for you. Say goodbye to oversight and hello to perfectly planned meetings.

- Proposal Proficiency: Dive deep into complex proposals with AI by your side. Identify risks, ambiguities, and potential pitfalls with laser-sharp precision.

- Your Private Think-Tank: Use Generative AI as a confidential thought partner. Brainstorm, strategize, and refine your ideas with an AI confidant that's always ready.

- Masterful Communication: Draft emails, documents, and crucial materials with eloquence and precision. Let Generative AI be your co-author, ensuring your communications resonate with impact.

- Navigating HR Dynamics: Explore challenging HR conversations with added insight. Generative AI assists you in understanding nuances and guiding delicate conversations towards positive outcomes.

- Seamless Leadership Transitions: Whether it's you moving up the ladder or a team member stepping into a new role, Generative AI aids in ensuring smooth transitions. Plan, strategize, and execute leadership handovers with unparalleled confidence.

In addition to these key hands-on tasks, you will quickly come up to speed on how to innovate with Generative AI, and explore:

- Deep Dive into Generative AI: Grasp the underlying principles of Generative AI and its transformative potential. Equip yourself to make critical decisions about implementing Generative AI in your organization, ensuring you stay ahead in the modern leadership curve.

- Augmented Intelligence - The New Frontier: Discover how Generative AI acts as an 'Exoskeleton for the Mind', enhancing your cognitive capabilities. Witness firsthand how Augmented Intelligence is not just a concept but a revolutionary way to supercharge your leadership style.

- Boost Your Productivity: No more starting from scratch! Learn how Generative AI can be your assistant, creating agendas, drafting emails, generating documents, and much more. Experience a dramatic surge in efficiency, getting your work 50% to 80% completed before you even begin.

- Addressing Staff Anxiety: In a world where AI is often viewed with skepticism, understand how to navigate the waters of staff anxiety. Discover how to effectively communicate the capabilities and limitations of Generative AI, ensuring a seamless integration into your organization.

- Beyond the Internet - The Generative AI Revolution: Understand why experts are heralding Generative AI as a technological marvel surpassing the transformative impact of the Internet. Prepare yourself for the future of leadership in an AI-augmented world.


AI Agents Are the Next Leap in Software. Learn to Build Them in Python.

AI agents aren't passive tools. They think, act, and solve problems—without waiting for instructions. That's the future of software. And in this course, you'll learn how to build it.

Frameworks come and go. Principles last. This course cuts through the noise to teach you how AI agents really work—using Python, the leading language for AI development.

Forget tutorials on trendy APIs that'll be dead by next quarter. You'll learn to build AI agents from the ground up. No fluff. No shortcuts. Just the core architecture that powers intelligent systems—knowledge that stays useful no matter how fast the landscape shifts.

In this course, you will:

- Master Python-based agent architectural fundamentals - Understand the core GAME components (Goals, Actions, Memory, Environment) that make AI agents tick and how they work together in a cohesive Python system

- Leverage Python's strengths for efficient agent development - Use Python's dynamic typing, decorators, and metaprogramming to create flexible, maintainable agent frameworks with minimal boilerplate code

- Rapidly prototype and implement Python agents - Learn techniques to quickly design Python agent capabilities with prompt engineering before writing a single line of code, then efficiently translate your designs into working Python implementations

- Connect Python AI agents to real-world systems - Build Python agents that can interact with file systems, APIs, and other external services

- Create Python-powered tool-using AI assistants - Develop Python agents that can analyze files, manage data, and automate complex workflows by combining LLM reasoning with Python's extensive libraries and ecosystem

- Build Python developer productivity agents - Create specialized Python agents that help you write code, generate tests, and produce documentation to accelerate your software development process

Why Principles Matter More Than Frameworks

The AI landscape is changing weekly, but the core principles of agent design remain constant. By understanding how to build agents from scratch, you'll gain:

- Transferable knowledge that works across any LLM or AI technology
Deep debugging skills because you'll understand what's happening at every level

- Framework independence that frees you from dependency on third-party libraries and allows you to succeed with any of them

- Future-proof expertise that will still be relevant when today's popular tools are long forgotten

By the end of this course, you won't just know how to use AI agents—you'll know how to build them in Python, customize them, and deploy them to solve real business problems.

This course will teach you these concepts using OpenAI's APIs, which require paid access, but the principles and techniques can be adapted to other LLMs.

AI Agents and Agentic AI with Python & Generative AI


Tired of spending hours manually replying to emails? In ChatGPT + Zapier: Automate Email Replies with AI, you’ll learn how to transform your inbox management process by setting up automated AI-driven email replies. This course will show you how to connect labeled emails and folders to workflows that draft responses while you triage your inbox. By the time you’ve finished organizing, ChatGPT will have prepared reply drafts for you to review and send.

You’ll master setting up ChatGPT with customized instructions using a Google Document or another note-taking tool, allowing you to teach ChatGPT how to adjust its responses based on different situations. Enhance your email responses with smart customization by feeding ChatGPT useful instructions like frequently asked questions, typical responses, escalation guidelines, and personal preferences for tone or formality. Provide dynamic information, such as upcoming travel plans, meeting availability, or recent project updates, to ensure replies are accurate, context-aware, and in line with your communication style. Incorporate templates for common scenarios (e.g., scheduling meetings, responding to customer inquiries, or sharing project status updates) to make your automated responses even more efficient and relevant.

You will learn skills to help tackle common email pain points, such as:

	•	Avoiding back-and-forth scheduling: Automatically include preferred meeting times or suggest open slots based on your calendar.
	•	Quickly addressing repeated queries: Generate detailed responses to FAQs or recurring requests without starting from scratch each time.
	•	Efficient escalation handling: Provide clear next steps or contact details for further assistance, using escalation guidelines for common issues.

Whether you’re looking to streamline client communication, speed up internal responses, or simply reduce the time spent on email, this course will equip you with the tools you need to automate your inbox efficiently and effectively.


Change Management for Generative AI

Generative AI is revolutionizing industries and transforming the way we work. As organizations embrace these cutting-edge technologies, effective change management becomes crucial to ensure a smooth transition and maximize the benefits of generative AI. In this course, "Change Management for Generative AI," we will delve into the strategies and frameworks necessary to successfully implement and adapt to these transformative technologies.

You will learn about the FASTER framework, a comprehensive approach to managing change in the era of generative AI. We will explore each component of the framework, including establishing a strong foundation, aligning stakeholders, implementing safeguards, providing training, embracing evolution, and enabling replication. Through real-world examples and practical insights, you will gain the skills to lead change initiatives and drive the adoption of generative AI in your organization.


Imagine turning your database into a conversation partner that not only answers your questions but visualizes insights, finds patterns you've missed, and helps fix your queries. This isn't science fiction—it's the power of Generative AI applied to SQL and data analysis.

Traditional database interaction requires technical expertise, writing complex queries, and switching between multiple tools to analyze and visualize results. But what if you could simply have a conversation with your data? What if you could ask questions in plain language and receive immediate, actionable insights?

This course teaches you how to revolutionize your relationship with databases through ChatGPT-assisted analysis. You'll learn powerful techniques to establish accurate database context, collaboratively explore meaningful questions, generate precise SQL queries, and transform raw results into compelling visualizations—all through natural conversation with AI.

All of these techniques are simple for anyone to use with just ChatGPT and your database.

We'll explore proven patterns like the CLUE framework for establishing database context, the experimental query generation approach for building complex queries, and the TRUST method for verifying your results. You'll discover how to design visualization-first queries, build reproducible analysis pipelines with the CAPTURE pattern, and efficiently troubleshoot SQL issues using the DEBUG framework.
Whether you're a data analyst seeking to accelerate your workflow, a business professional who needs insights without coding expertise, or a database administrator looking to expand your toolkit, this course equips you with immediately applicable skills. You'll learn to extract more value from your existing data while dramatically reducing the time and technical barriers traditionally associated with database analysis.

By the end of this course, you'll transform how you interact with databases—moving from rigid, technical queries to fluid, insightful conversations that reveal the full potential of your data. As Generative AI reshapes the landscape of data analysis, this course ensures you're not just keeping pace but leading the charge with powerful, practical techniques that deliver immediate results.

Chat with Your Data: Generative AI-Powered SQL Data Analysis


Microservice Architectures

Dive into the world of Microservices Architectures with this comprehensive course, designed to provide a clear understanding of both the theoretical and practical aspects of building scalable and robust systems. Beginning with the concept of program slicing, this course will guide you through the intricacies of Microservices, contrasting them against monolithic systems, and demonstrating the art and science behind their design, deployment, and maintenance.

What You'll Learn:

Program Slicing and its Relevance: Dive into the foundational concepts of program slicing, its significance in system design, and its relationship with Microservices.

Monoliths vs. Microservices: Contrast the architectural styles, understanding the strengths, weaknesses, and practical differences of each approach.

Decoupling and Invocation Dynamics: Grasp the intricacies of service decoupling, hidden couplings, and the complexities of service invocation within a Microservices environment.

Deployment, Scalability, and Dependency Management: Delve into the challenges and consequences of adopting Microservices, focusing on deployment strategies, scalability issues, and dependency isolation.

Building Resilient Systems: Explore client-side load balancing, circuit breakers, fallback strategies, and other resilience techniques to ensure system stability and robustness.

Who Should Enroll:

Students should have intermediate Java programming experience.


Honors AI Foundations for High School Students

Prepare yourself for the AI-driven future.

Large language models like ChatGPT, Claude, and Gemini are transforming every field—from science and medicine to business, creative arts, and engineering. They're becoming as essential to your future as smartphones are today. But here's the difference: while everyone will have access to these tools, only those who truly understand how to use them will be prepared to innovate, lead, and succeed.

This Honors AI Foundations course is designed for accomplished high school students ready to develop genuine expertise in applied AI—not just familiarity, but real skill. Think of it as the AI equivalent of an AP course: it positions you to enter college with a competitive edge and a Vanderbilt University certificate from Coursera that signals serious preparation in one of the world's most important emerging fields.

What You'll Learn:

- Use large language models effectively and strategically
- Master prompt engineering—the skill that separates expert users from novices
- Apply prompt patterns and advanced techniques to solve problems across any domain
- Build applications powered by large language models
- Harness AI for innovation in writing, coding, summarization, planning, simulation, tutoring, and problem-solving
- Understand the limitations and risks of AI systems
- Use AI to process information, reason through complex problems, and program solutions

Concrete, practical skills. You'll work through real examples showing how AI can tutor you in math, help you write better essays, generate code for your projects, improve your cybersecurity, plan your life more effectively, and tackle problems in your job or personal interests. You'll build toward writing sophisticated prompts that can handle nuanced, multi-step problems across any field.

No prerequisites required—just basic computer skills (using a browser and accessing ChatGPT, Claude, Gemini, or another equivalent AI tool).

Why This Matters for College and Your Future:

Colleges are beginning to recognize AI literacy as a foundational skill, much like they did with computer science. An Honors AI Foundations certificate from Vanderbilt demonstrates that you've invested in serious preparation, that you understand both the power and the responsibility of AI, and that you're ready to innovate and lead in an AI-driven world. Whether you pursue computer science, business, medicine, engineering, humanities, or any other field, this credential and these skills will distinguish your college applications and prepare you to succeed wherever AI intersects with your interests.

Upon successful completion of this course, you will receive an Honors AI Foundations Certificate from Vanderbilt University via Coursera.


Hot Topics in Criminal Justice

The American criminal justice system is in need of reform. We incarcerate too many people. We pay insufficient attention to scientific advances that can help separate out the most and least culpable and dangerous. We grant the police far too much power to use force and technological surveillance.

As a way of thinking about dealing with these problems, this course examines two over-arching issues: What should we do with people who have committed crime, and how should we identify who they are? On the first issue, we will look closely at the nature and causes of mass incarceration, the death penalty, the lawfulness and usefulness of preventive detention and risk assessment, and the role of the insanity defense and neuroscience in assessing responsibility for crime. On the second issue, we will explore the laws regulating the police use of force, the growing role of technology and big data in facilitating police surveillance and detection and deterrence of crime, and the remedies for police malfeasance, including the exclusionary rule and damages actions.

The overall goal is to help thinking individuals assess today’s criminal justice system and consider ways of making it better. After completing this course, participants should be able to:

1. Describe the mass incarceration problem in the United States and why it might exist;
2. Suggest ways of reducing incarceration;
3. Criticize risk assessment instruments and their use in preventive detention schemes;
4. Discuss the pros and cons of the death penalty, the exclusionary rule, and the insanity defense;
5. Analyze the relevance of neurological evidence in a criminal case;
6.  Identify some of the reasons innocent people are wrongly convicted;
7. Elaborate on the role race plays at sentencing and in police use of force, stop and frisk and consent searches and
8. List the types of surveillance technology available to police and the constitutional principles that govern their use.


Building HTTP APIs with Spring

Unlock the power of building HTTP APIs with Spring, one of the most versatile and widely-used Java frameworks, in this comprehensive course tailored for both beginners and seasoned developers. Dive deep into building robust, scalable, and secure HTTP APIs from scratch and gain mastery over RESTful services with Spring Boot.

What You'll Learn:

Foundational Concepts: Understand the principles of REST and the significance of stateless APIs.

Spring Boot Mastery: Kickstart your applications and reduce the boilerplate with Spring Boot. Dive into data access, security configurations, and more.

Data Persistence: Integrate with databases using Spring Data JPA and discover the magic of CRUD operations without writing SQL.

Security: Safeguard your APIs with Spring Security, implementing authentication and authorization mechanisms.

Testing: Ensure your APIs stand the test of time with unit, integration, and end-to-end testing strategies.

Who Should Enroll:
Students should have a solid understanding of coding in Java.


Trustworthy Generative AI

We see lots of news reports of Generative AI tools, such as ChatGPT, making mistakes and producing inaccurate information. Many of these mistakes happen because humans use the tools in the wrong way - trying to solve unsuitable problems and not thinking about risk. Hallucination isn't a bug, it's a feature when you approach problems correctly. This course teaches techniques for determining if a problem fits Generative AI's capabilities, framing problems to reduce risk, prompt engineering for trust, and appropriate human engagement in the process. Students learn concrete prompt designs, how to check outputs, how to use Generative AI for ideation and creation, ways to augment human skills, and more ethical, beneficial applications.

The course will show how you can:

- Leverage prompt engineering techniques to generate more reliable outputs
- Master methods to verify and validate outputs
- Frame problems in alternative ways to reduce risk
- Apply generative AI for creative ideation
- Use Generative AI in ways that augment rather than replace human reasoning and creativity


Master the Art of Building Intelligent Java Agents That Think, Reason, and Act

Unlock the full potential of Java for creating autonomous AI agents that solve complex problems without constant human direction. In this comprehensive course on AI Agents and Agentic AI with Java & Generative AI, you'll learn how to architect sophisticated agent systems that leverage Java's robust ecosystem and enterprise-grade capabilities.

This course takes you beyond the foundations covered in the AI Agents and Agentic AI with Java & Generative AI course to explore advanced patterns for building truly intelligent agents in Java. You'll delve into specialized techniques like self-prompting, expert personas, document-as-implementation, and multi-agent orchestration - all implemented with Java's powerful frameworks and libraries.

## What You'll Learn:

- **Self-Prompting Patterns in Java**: Build agents that dynamically adopt different thinking modes to handle specialized tasks, transforming unstructured data into structured formats with clean Java implementations

- Java-Based Expert Persona Systems: Implement consultation frameworks where agents can invoke domain experts for specialized knowledge while maintaining clean architecture

- Document-as-Implementation: Use Java's powerful file handling to create systems where human-readable documents become executable business logic

- Multi-Agent Collaboration with Java: Design sophisticated memory sharing and coordination mechanisms between specialized Java agents

- Progress Tracking & Planning: Implement robust planning and reflection capabilities using Java's enterprise-grade tooling

- Java Agent Safety & Trust Systems: Build transaction management and safety mechanisms that leverage Java's exception handling and security features

By the end of this course, you'll be equipped to build complex, enterprise-ready agent systems in Java that can reason across multiple domains, handle complex workflows, and safely interact with real-world systems.

Whether you're building productivity tools, automating complex business processes, or creating intelligent assistants, you'll have the Java-specific knowledge to implement agentic AI solutions that provide genuine business value.

This course will teach you these concepts using OpenAI's APIs, which require paid access, but the principles and techniques can be adapted to other LLMs.

AI Agent Architecture in Java with Generative AI


Struggling to keep up with your data analysis workload? Imagine having an AI assistant that not only helps you explore your Excel data but also automates mundane tasks, uncovers insights, and crafts compelling stories—all with a few prompts. This course will take you on a journey to revolutionize your Excel skills, transforming how you analyze data, make decisions, and tell impactful stories.

What You'll Learn

- Turn Data Chaos into Clear Insights: Use ChatGPT as your AI teammate to explore complex datasets. Quickly upload your data, describe every column, identify potential errors, and understand relationships—just like onboarding a new expert colleague. Gain clarity and avoid costly pitfalls before diving into deeper analysis.

- Unify Your Data in Seconds—No More Headaches: Master the art of merging data from multiple Excel sheets or CSV files. Whether you're connecting job roles with industry skills or combining employee data with payroll, ChatGPT helps identify relationships and ensures your data is cohesive—giving you a complete view without manual effort.

- Never Fear Data Errors Again—Debug Like a Pro: Learn how to identify and resolve inconsistencies in your data, such as jobs appearing in both high and low pay categories. Use ChatGPT to dig deeper and uncover underlying issues, like duplicated entries or mismatched pay periods, making your data analysis smoother and more reliable.

- Say Goodbye to Tedious Data Extraction—Automate with AI: Discover how ChatGPT can automate extracting structured datasets from unformatted information. Whether it’s copying LinkedIn posts or pulling details from messy text, generative AI speeds up data preparation, turning raw material into ready-to-analyze formats in seconds.

- Transform Your Data into Stories That Captivate and Inspire: Elevate your presentations by using ChatGPT to craft compelling narratives from your data. Learn how to highlight key statistics and make them relatable to your audience. Develop engaging titles, add visual placeholders, and ensure every insight tells a story that resonates and drives action.

- Make Your Work Rock Solid—Ensure Replicability & Traceability: Learn best practices to make your analyses replicable and trustworthy. Use ChatGPT to create traceability packages that document every data source, every step of your analysis, and every assumption made—ensuring anyone can replicate your work and understand its integrity.

Who Is This Course For?

Data Analysts: Tired of spending hours on manual data cleaning? Learn how to turbocharge your workflow with AI.

Business Professionals: Need faster, more confident data-driven decisions? Discover how AI can help you make informed choices effortlessly.

Excel Enthusiasts: Want to level up your skills and impress your peers? Integrate AI to enhance your spreadsheets.

Anyone Curious About AI: Find out just how easy it is to bring powerful AI tools into your everyday data tasks and make analysis both insightful and enjoyable.

Why Take This Course?

This isn't just about learning Excel; it's about transforming your entire approach to data. Imagine using ChatGPT to merge customer data in seconds, uncover hidden trends, and create visual narratives that impress stakeholders. With ChatGPT by your side, you'll save time on mundane tasks, avoid common pitfalls, and focus on what truly matters: deriving insights and making decisions that drive real impact. By the end, you'll be equipped with the skills to use AI effectively—from handling complex datasets to telling captivating stories and making your work replicable.

Join thousands of professionals already using AI to enhance their data analysis and storytelling capabilities. Don’t get left behind—enroll today and start mastering the future of Excel!

ChatGPT + Excel: Master Data, Make Decisions, Tell Stories


In "ChatGPT for Project Management: Insight, Planning, & Success," you will learn how to leverage large language models (LLMs) to enhance your project planning and strategy development. This course covers techniques for using LLMs to break down complex projects into manageable tasks, phases, goals, risks, and more. You’ll discover how to utilize AI for organizing project components into timelines, schedules, strategies, dependencies, and Gantt charts to create a cohesive plan.

Explore how prompting can give you instant access to "subject matter expert" insights on any topic, and learn how to control LLM output with the template pattern for precisely formatted plans and project documents. You’ll also master the use of AI to analyze risks, assess alternative approaches, generate metrics and OKRs, and optimize your project plan. By the end of this course, you'll have the skills to integrate LLMs into your project planning process, driving insight, strategy, and success.


This 4 week MOOC builds upon the overview of Java and Android covered in Course 1 by delving deeper into core Android app components, such as intents, activities, and broadcast receivers.  You will learn by example how to program these core Android components together with Android concurrency frameworks and basic Java file I/O classes (such as File and InputStream) and Android storage mechanisms (such as Shared Preferences). You'll also learn how to use the Git source code management system. Throughout this MOOC you'll work incrementally on a project involving downloading, storing, and display images from remote websites. Each week you will add additional capabilities to the project, based on material covered in the lecture videos. You'll spend roughly 4 hours per week watching video lectures, taking quizzes, and programming assignments with Java and Android.

Android App Components - Intents, Activities, and Broadcast Receivers

Dr. Douglas C. Schmidt
Michael Walker
Dr. Jules White


The future of education is being shaped by AI, and this course provides a front-row seat to the conversations happening at the ASU+GSV AI Show—one of the leading global events on innovation in learning. In this course, you’ll hear directly from thought leaders, educators, and industry experts who are driving AI’s impact on education. Through exclusive interviews and discussions, we explore key questions: How is AI transforming access to learning? What skills do students need for the future? How can educators adapt to this rapid shift?

This course will guide you through insights from some of the most voices in this space. Featured speakers include Kip Glazer, Ardis Kadiu, David Miyashiro, Mushina H. Morris, Merissa Sadler-Holder, Eddie Watson, and Jules White. They bring diverse perspectives from K-12, higher education, and ed-tech innovation, offering a comprehensive look at the evolving role of AI in teaching and learning. This course is your opportunity to engage with these critical conversations and stay ahead in the AI-driven educational landscape.

The Future of Education with AI: Exploring Perspectives


This course is designed to teach you about Extended Reality (XR) and how it's used in healthcare education and clinical practice. Using lessons from experts in the field and different case studies, this course shows you how to use this innovative technology in a way that creates sustainable, accessible real-world applications.

The course considers Extended Reality (XR) from a number of perspectives. First of all, you will learn about the hardware and software considerations, then focus on education structures and frameworks and how Extended Reality is applied appropriately within this context. The final two weeks demonstrate how XR has been applied in real-world clinical practice and how you can best make use of the technology by understanding the impact of interdisciplinary expertise in the field.

Overall, this course will help you to advance your use of Extended Reality, whatever your background, in clinical training and technologically-enhanced healthcare education contexts. You will also have the optional pathway of developing a business case alongside fellow learners. The business case can be the first step in promoting your XR idea or project to different stakeholders.

XR in Healthcare Education and Clinical Practice


Interventions and Calibration

This course covers approaches for modelling treatment of infectious disease, as well as for modelling vaccination. Building on the SIR model, you will learn how to incorporate additional compartments to represent the effects of interventions, such the effect of vaccination in reducing susceptibility. You will learn about ‘leaky’ vaccines and how to model them, as well as different types of vaccine and treatment effects. It is important to consider basic relationships between models and data, so, using the basic SIR model you have developed in course 1, you will calibrate this model to epidemic data. Performing such a calibration by hand will help you gain an understanding of how model parameters can be adjusted in order to capture real-world data. Lastly in this course, you will learn about two simple approaches to computer-based model calibration - the least-squares approach and the maximum-likelihood approach; you will perform model calibrations under each of these approaches in R.


Using Data for Healthcare Improvement

In this course,  you will learn about the importance of measuring the quality of care and health outcomes in order to determine whether Quality Improvement(QI ) initiatives have achieved their aims. You will learn about how data is utilised to identify areas of improvement and the importance of using both quantitative and qualitative data in evaluating change. You will learn about the specific methods appropriate for improvement as distinct from methods more suited to research, including how to design measurement schemes suitable for improvement initiatives


This course is all about infectious diseases. We’ve selected four disease areas — HIV, Malaria, Emerging Infectious Diseases (Ebola and Zika), and TB — and we will go through each in turn. We’ve selected these diseases because they span a range of different types of disease and allow us to look at important issues that relevance of other diseases too. We will look at each disease in the same way: we begin by looking at the aetiology and epidemiology of the diseases. We then show how data on this disease can be used to understand important trends and patterns. We then focus on the interventions that can be used to address that disease - typically spanning both prevention and treatment - and consider how policies have been developed to address the disease. We finish by reflecting on the whole topic area of the disease with an external expert.

Global Disease Masterclass: Communicable Diseases Epidemiology, Intervention and Prevention


The Global Diseases Masterclass is part of the full-degree Masters of Public Health that the School of Public Health. By the end of this specialisation, our aim is that students will be able to critically apply epidemiological concepts to major global diseases and be able to appraise and recommend policy options to combat them.

Global Diseases Masterclass: Global Disease Distribution
In this course, we will introduce students to the most important trends and pattern in health and disease on a global scale. We will look at how health has improved over time, examine the trends for the future and look at between and within-country inequality in health. We will look at the methods that lie behind those statistics and think about different ways in which health can be conceptualised and measured. The course ends by considering the reason that might lie behind the patterns that we’ve pointed out and introducing the distinction between direct and structural interventions.The course ends by considering the reasons that might lie behind the patterns that we’ve described and introducing the concept of structural interventions.

Global Disease Masterclass: Global Disease Distribution


Welcome to Logistic Regression in R for Public Health!

Why logistic regression for public health rather than just logistic regression? Well, there are some particular considerations for every data set, and public health data sets have particular features that need special attention. In a word, they're messy. Like the others in the series, this is a hands-on course, giving you plenty of practice with R on real-life, messy data, with predicting who has diabetes from a set of patient characteristics as the worked example for this course. Additionally, the interpretation of the outputs from the regression model can differ depending on the perspective that you take, and public health doesn’t just take the perspective of an individual patient but must also consider the population angle. That said, much of what is covered in this course is true for logistic regression when applied to any data set, so you will be able to apply the principles of this course to logistic regression more broadly too.

By the end of this course, you will be able to:
Explain when it is valid to use logistic regression
Define odds and odds ratios
Run simple and multiple logistic regression analysis in R and interpret the output
Evaluate the model assumptions for multiple logistic regression in R
Describe and compare some common ways to choose a multiple regression model

This course builds on skills such as hypothesis testing, p values, and how to use R, which are covered in the first two courses of the Statistics for Public Health specialisation. If you are unfamiliar with these skills, we suggest you review Statistical Thinking for Public Health and Linear Regression for Public Health before beginning this course. If you are already familiar with these skills, we are confident that you will enjoy furthering your knowledge and skills in Statistics for Public Health: Logistic Regression for Public Health.

We hope you enjoy the course!


This course introduces you to the concepts, theories and application of Quality Improvement (QI) in healthcare from a global perspective. You will hear from patients, clinicians and academics what quality improvement means to them, and how they work together to deliver change. Studying the challenges, they faced and strategies they utilised to overcome those challenges, you will learn to apply and critique core QI methods, from experts in the field.
By the end of this module you will be able to:
1. Recognise the characteristics of different approaches to quality improvement in order to critique them and make decisions as to when to deploy them.
2. Explain the necessary components of a structured approach to quality improvement in order to apply key quality improvement methods to support the planning, design, implementation and evaluation of improvement efforts.

Introduction to Quality Improvement in Healthcare


Types of Vaccine

This course explores the different types of vaccine available. This is a useful framework for understanding the scientific understanding that went into vaccine development. It also gives a perspective of how innovation (e.g. fundamental virology, molecular biology) work in a translational medicine context.

On successful completion of the course, learners will be able to:

1. Identify features of different vaccine platforms
2. Demonstrate an understanding of the role of microbiological and technological advances in the development of vaccines
3. Recognise how different platforms may provide protection against different pathogens.

This is an intermediate series of courses intended for both clinical and non-clinical scientists who want to update their skills for work in clinical, academic, and pharmaceutical/bioscience industries, and is developed and delivered by experts working on viruses and novel vaccines.

To be successful in this series of courses, you should have basic knowledge in biology, genetics, microbiology or related fields.


Welcome to this course on Customising your models with TensorFlow 2!

In this course you will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models.

You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills.

At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop a custom neural translation model from scratch.

TensorFlow is an open source machine library, and is one of the most widely used frameworks for deep learning. The release of TensorFlow 2 marks a step change in the product development, with a central focus on ease of use for all users, from beginner to advanced level.

This course follows on directly from the previous course Getting Started with TensorFlow 2. The additional prerequisite knowledge required in order to be successful in this course is proficiency in the python programming language, (this course uses python 3), knowledge of general machine learning concepts (such as overfitting/underfitting, supervised learning tasks, validation, regularisation and model selection), and a working knowledge of the field of deep learning, including typical model architectures (MLP, CNN, RNN, ResNet), and concepts such as transfer learning, data augmentation and word embeddings.


This course delves into intellectual property (IP) with an introduction into the innovation landscape within the UK and how that compares with other settings. You'll focus on how intellectual property applies to frugal innovations, compulsory licensing, and when and how to apply for intellectual property. This course teaches you about how innovators can secure funding by exploring the types of funding available, the types of organisations involved, as well as the challenges that surround funding. A key part of this course is a focus on 'pitching' skills - what's involved in pitching well, what content should you include, how can you best deliver a pitch.

You'll come away from this course with strong insight into the importance of protecting an innovation's intellectual property,  able to identify IP processes and challenges, and able to confidently pitch an innovation.

Healthcare Entrepreneurship: Taking Ideas to Market


Welcome to this course on the aetiology, epidemiology and interventions for non-communicable diseases of the Global Diseases Masterclass. We’ve selected four disease areas and will go through each in turn. The diseases we’ve chosen are: Colorectal Cancer, Cardiovascular Disease (CVD), Dementia, and Diabetes. We have selected these non-communicable diseases because they span a range of different types of disease process and because of the expertise and experience that our School of Public Health has in these areas. This will provide an introduction to a few of the of most important global non-communicable disease challenges while also providing variation in aetiology, epidemiology and interventions to learn from. We hope that by the end of this course you will be able to describe the basics of the disease aetiology, global epidemic trends and the available interventions. We also hope you’ll be able to use this information to critique public health approaches and policy positions for the four non-communicable diseases we’ve covered as well helping you extend to further disease areas.

Global Disease Masterclass: Non-communicable Diseases


Introduction to Android graphics

This course will bring you up to speed with the fundamentals of 2D graphics and 3D graphics in Android.  This course provides the ideal primer for more advanced courses and applications, for example, OpenGL, as well as Virtual Reality in Android.

This course is unique because it covers the key concepts and theory of 2D and 3D graphics while also showing you how to implement these in Android practically.  This provides a solid understanding and grasp of the subject matter which will be applicable in a variety of settings.

The final assignment will provide you with an artefact which you can use for your professional portfolio to evidence your skills.

This course is 3 weeks long, and will require around 6 hours studying per week.


This course will introduce you to participatory approaches to public health. You will learn about the history of participatory health research and why it is essential to solving contemporary public health challenges. The course will help you to understand the social and cultural context of public health, before introducing you to essential concepts for working with communities: knowledge and power. Finally, you will engage with critical analyses of participatory approaches, to help you to determine if and when such strategies are appropriate. Throughout the course you will analyse real-world case studies of community-based health projects, including historical HIV social movements, public health projects with sex workers, and participatory approaches to the COVID-19 pandemic. The course will equip you to practice public health in partnership with local communities. It is followed by a second course, Applying Participatory Approaches in Public Health Settings, which builds upon the theoretical foundations of this introductory course.

Introduction to Participatory Approaches in Public Health


Getting started with TensorFlow 2

Welcome to this course on Getting started with TensorFlow 2!

In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models.

You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills.

At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop an image classifier deep learning model from scratch.

Tensorflow is an open source machine library, and is one of the most widely used frameworks for deep learning. The release of Tensorflow 2 marks a step change in the product development, with a central focus on ease of use for all users, from beginner to advanced level. This course is intended for both users who are completely new to Tensorflow, as well as users with experience in Tensorflow 1.x.

The prerequisite knowledge required in order to be successful in this course is proficiency in the python programming language, (this course uses python 3), knowledge of general machine learning concepts (such as overfitting/underfitting, supervised learning tasks, validation, regularisation and model selection), and a working knowledge of the field of deep learning, including typical model architectures (MLP/feedforward and convolutional neural networks), activation functions, output layers, and optimisation.


This course describes how viruses interact with their hosts to infect, replicate, spread and cause disease. It looks at what controls the specificity of viruses for different host species and for different tissues within a host, also how this can lead to outbreaks and pandemics. It seeks to show how virus evolution is driven by the pressures to replicate and spread. Finally, it looks at how we can find antivirals and use them to control infections, distinguishing therapeutic approaches to prophylaxis by vaccination.

On successful completion of the course, learners will be able to:

1. Identify key interfaces of virus: host interaction
2. Verify how transmission drives virus pathogenesis and shapes virus evolution
3. Demonstrate an understanding of how virus control with anti-virals differs from protection with vaccines.

This is an intermediate series of courses intended for both clinical and non-clinical scientists who want to update their skills for work in clinical, academic, and pharmaceutical/bioscience industries, and is developed and delivered by experts working on viruses and novel vaccines.

To be successful in this series of courses, you should have basic knowledge in biology, genetics, microbiology or related fields.

What Do Viruses Do And How Do We Control Them


Measuring Disease in Epidemiology

Epidemiological research is ubiquitous. Even if you don’t realise it, you come across epidemiological studies and the impact of their findings every single day. You have probably heard that obesity is increasing in high income countries or that malaria is killing millions of people in low income countries. It is common knowledge that smoking causes cancer and that physical activity is protective against heart disease. These facts may seem obvious today, but it took decades of epidemiological research to produce the necessary evidence. In this course, you will learn the fundamental tools of epidemiology which are essential to conduct such studies, starting with the measures used to describe the frequency of a disease or health-related condition. You will also learn how to quantify the strength of an association and discuss the distinction between association and causation. In the second half of the course, you will use this knowledge to describe different strategies for prevention, identify strengths and weaknesses of diagnostic tests and consider when a screening programme is appropriate.


Mathematics for Machine Learning: PCA

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.

At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge.

The lectures, examples and exercises require:
1. Some ability of abstract thinking
2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis)
3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization)
4. Basic knowledge in python programming and numpy

Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms.


Creativity concerns the development of new ideas. Throughout human history, the application of ideas has led to transformations of our daily lives and society. Modern business activity thrives on recently developed ideas, and it is through creativity that we address both challenges and opportunities. In this module, we will help you develop your implicit understanding and skills in creativity. You will be introduced to a series of creativity tools that can be used to augment the ideas you might come up with independently. Specifically, we will develop your skills in list, sticky-note, grid and alphabet brainstorming through an introduction to the principles of the tool, along with examples and an opportunity for you to have a go. A framework called the creativity diamond is used throughout the course to guide which approach to creativity to use and when.

WHAT YOU WILL LEARN
•	The principal facets of creativity
•	Principles of creativity
•	A framework that can be used to guide which approach to creativity to use
•	Metrics that can be used in assessing creativity
•	The importance of managing our thinking
•	How to use and apply list, sticky note, grid and alphabet brainstorming

SKILLS YOU WILL GAIN
•	Divergent and convergent creativity
•	Assessing creativity
•	Principles of brainstorming
•	List, sticky-note, grid and alphabet brainstorming

Introduction to Creative Thinking: Tools for Success


In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally  we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.

At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

Mathematics for Machine Learning: Linear Algebra

David Dye
Samuel J. Cooper
A. Freddie Page


Welcome to Introduction to Statistics & Data Analysis in Public Health!

This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series. You'll learn the popular, flexible and completely free software R, used by statistics and machine learning practitioners everywhere. It's hands-on, so you'll first learn about how to phrase a testable hypothesis via examples of medical research as reported by the media. Then you'll work through a data set on fruit and vegetable eating habits: data that are realistically messy, because that's what public health data sets are like in reality. There will be mini-quizzes with feedback along the way to check your understanding. The course will sharpen your ability to think critically and not take things for granted: in this age of uncontrolled algorithms and fake news, these skills are more important than ever.

Prerequisites

Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need only basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. No knowledge of R or programming is assumed.


This specialization is intended for people interested in health systems and how they function. Participants will learn about the global health systems landscape and the challenges and opportunities to achieve better health outcomes.

This specialisation is divided into three courses which are offered as massive online open access courses (Courses 1-3), and a fourth course which is offered as part of the Online MPH degree (capstone). The Health Systems Development specialisation is geared toward learners who have no prior knowledge of health systems or those who are starting to explore this area of study. Overall, sessions in this specialisation span 16 week with approximately 96 hours of viewing learning materials per week. Formative assessments in the form of quizzes and activities are incorporated throughout the weeks to help learners gauge their level of depth of understanding and to prepare them for their summative assessments.

Participants will have the opportunity to explore a range of areas within health systems. The first course will introduce the main building blocks of health systems and shed some light on key components of well-functioning health systems including how health system performance is assessed. In course two, students will learn how to conduct a health impact assessment and how to assess the impacts of policies, plans and projects, as well as how that support decision-makers make choices regarding alternatives and improvements to prevent disease or injury and to actively promote health. The third course explores   human   resources   for   health   and   service   delivery.  In this course, students will learn about and analyse country experiences in transforming health services delivery, and interventions and to address human resources for health challenges at a global level.

By the end of this specialisation, learners should be able to identify key components of, and critically compare, different health systems; analyse country experiences in transforming   services; and apply theoretical framework for assessing the health impacts of policies, plans and projects.

Health Systems Development: Health systems, Policy and Research


Creativity is important in nearly every facet of life. Advances in neuro-science, computing and psychology, along with developments in other domains and cross-disciplinary areas have resulted in ever increasing understanding of creativity. This module will explore some advanced approaches to creativity such as the use of analogy and metaphor, various thinking styles and the role of artificial intelligence. A framework called the creativity diamond is used to guide the selection of approach to creativity relevant to your project or activity.

WHAT YOU WILL LEARN
•	Biomimicry and using insights from nature
•	Use of analogy and metaphor in creativity
•	A range of creative thinking and reasoning approaches
•	The use of AI to generate creative outcomes
•	Attributes of creativity and their assessment
•	Use of the creativity diamond

SKILLS YOU WILL GAIN
•	Biomimicry
•	Use of analogy and metaphor in creativity
•	Accelerated proficiency in creative thinking styles
•	Use of some AI enabled platforms to generate outputs
•	Creativity assessment
•	Use of the creativity diamond in practice to aid in the selection of an approach to creativity relevant to your project or activity

Advanced Creative Thinking and AI: Tools for Success


Welcome to Linear Regression in R for Public Health!

Public Health has been defined as “the art and science of preventing disease, prolonging life and promoting health through the organized efforts of society”. Knowing what causes disease and what makes it worse are clearly vital parts of this. This requires the development of statistical models that describe how patient and environmental factors affect our chances of getting ill. This course will show you how to create such models from scratch, beginning with introducing you to the concept of correlation and  linear regression before walking you through importing and examining your data, and then showing you how to fit models. Using the example of respiratory disease, these models will describe how patient and other factors affect outcomes such as lung function.

Linear regression is one of a family of regression models, and the other courses in this series will cover two further members. Regression models have many things in common with each other, though the mathematical details differ.
This course will show you how to prepare the data, assess how well the model fits the data, and test its underlying assumptions – vital tasks with any type of regression.
You will use the free and versatile software package R, used by statisticians and data scientists in academia, governments and industry worldwide.


This specialization is intended for people interested in health systems and how they function. Participants will learn about the global health systems landscape and the challenges and opportunities to achieve better health outcomes.

This specialisation is divided into three courses which are offered as massive online open access courses (Courses 1-3), and a fourth course which is offered as part of the Online MPH degree (capstone). The Health Systems Development specialisation is geared toward learners who have no prior knowledge of health systems or those who are starting to explore this area of study. Overall, sessions in this specialisation span 16 week with approximately 96 hours of viewing learning materials per week. Formative assessments in the form of quizzes and activities are incorporated throughout the weeks to help learners gauge their level of depth of understanding and to prepare them for their summative assessments.

Participants will have the opportunity to explore a range of areas within health systems. The first course will introduce the main building blocks of health systems and shed some light on key components of well-functioning health systems including how health system performance is assessed. In course two, students will learn how to conduct a health impact assessment and how to assess the impacts of policies, plans and projects, as well as how that support decision-makers make choices regarding alternatives and improvements to prevent disease or injury and to actively promote health. The third course explores   human   resources   for   health   and   service   delivery.   In   this   course, students will learn about and analyse country experiences in transforming health services delivery, and interventions and to address human resources for health challenges at a global level.

By the end of this specialisation, learners should be able to identify key components of, and critically compare, different health systems; analyse country experiences in transforming   services; and apply theoretical framework for assessing the health impacts of policies, plans and projects.

Health Systems Development: Introduction to Health Systems


In today’s ever-growing and changing world, being able to think creatively and innovatively are essential skills. It can sometimes be challenging to step back and reflect in an environment which is fast paced or when you are required to assimilate large amounts of information. Making sense of or communicating new ideas in an innovative and engaging way, approaching problems from fresh angles, and producing novel solutions are all traits which are highly sought after by employers.

This course will equip you with a ‘tool-box’, introducing you to a selection of behaviours and techniques that will augment your innate creativity. Some of the tools are suited to use on your own and others work well for a group, enabling you to leverage the power of several minds.  You can pick and choose which of these tools or techniques suit your needs and interests, focusing on some or all of the selected approaches and in the order that fits best for you.

The practical approach of this course enables you to acquire an essential skill-set for generating ideas, with plenty of:
- Fun e-tivities and exercises;
- Practical lectures and tips;
- Video representations of the techniques in action.

By the end of this course you should be able to:
- Pick a type of brainstorming you think will be useful to apply to a challenge
- Use alphabet brainstorming in tackling a challenge
- Use grid brainstorming in tackling a challenge
- Use a morphological chart to synthesise a solution to a challenge
- Use the TRIZ contradiction matrix to identify recommended inventive principles
- Apply SCAMPER to a range of challenges

The greatest innovators aren’t necessarily the people who have the most original idea. Often, they are people- or teams- that have harnessed their creativity to develop a new perspective or more effective way of communicating an idea. You can train your imagination to seize opportunities, break away from routine and habit, and tap into your natural creativity.

Join this course and a community of practitioners in CREATIVITY!

Creative Thinking: Techniques and Tools for Success


3D Graphics in Android: Sensors and VR

This course is an ideal introduction into creating virtual environments in Android.  This course is unique as it covers a range of tools and techniques to create immersive 3D environments, giving you a rounded skill set in this growing field.  By the end of this course, you will really be able to bring your VR ideas to life!

The first part of the course covers animation, lighting and reflection.  We then move onto textures and handling multiple 3D objects.  Finally, we'll look at housing all of this within a binocular view to create a VR experience.

There are practical exercises throughout the course to apply your understanding, and there is a summative project which can form part of your professional portfolio.

This course assumes a knowledge of Android programming and OpenGL. I recommend completing my two Coursera courses on these topics, as these are the perfect primer.

4 weeks of study, around 6 hours per week.  The final project will take around 10 hours to complete.


The Foundations of Public Health Practice: The Public Health Toolkit builds on public health thinking (introduced in the previous course) and introduces a variety of core public health approaches (the toolkit) to conceptualising problems, conducting analysis and bringing forward recommendations. In this course we cover health needs assessment, evaluation and public health intelligence-approaches.


This capstone project has been designed as an opportunity to practice what you have learned in the first 3 courses of this specialisation. This capstone project consists of 4 assignments. You will have to develop an immersive Android app with the use 3D graphics, sensor control and VR. For the first assignment, you will have to develop a virtual art gallery using the Android Canvas drawing functions introduced in Course 1 "Introduction to Android graphics". The second assignment entails the design and development of a virtual 3D art gallery based on OpenGL ES applying the knowledge and skills acquired in Course 2 "Android Graphics with OpenGL ES". For the third assignment you will create a virtual reality app with your 3D art gallery, using advanced techniques like introducing sensor control, animations, binocular view for VR, lighting effects and texture mapping, based on the knowledge and skills you learned in Course 3 "3D Graphics in Android: Sensors and VR". For the final submission of the capstone project we give you the avenue to combine your creativity with all the knowledge and skills you gained in Android graphics programming from the three first courses. This capstone is intended for learners with basic knowledge in Android app development seeking to develop knowledge in computer graphics and virtual reality in Android. The learners should have completed the 3 courses in this specialisation (i.e.  Course 1 "Introduction to Android graphics", Course 2 "Android Graphics with OpenGL ES", and Course 3 "3D Graphics in Android: Sensors and VR") before starting this capstone project.

Advanced App Development in Android Capstone


Introduction to Digital health

This course introduces the field of digital health and the key concepts and definitions in this emerging field. The key topics include Learning Health Systems and Electronic Health Records and various types of digital health technologies to include mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These technologies are assessed in terms of the key opportunities and challenges to their use and the evidence of their effectiveness in the field of digital health in relation to public health and healthcare globally. The use and application of digital health for COVID-19 forms a case study demonstrating the use of different types of digital health technologies to address key aspects of the response to the virus globally.


Welcome to ‘Science Matters: Let's Talk about COVID-19’, from the Jameel Institute at Imperial College London.

The outbreak of the Novel Coronavirus Disease (COVID-19) is the most significant public health emergency of the 21st century so far. As the epidemic spreads, people around the world want to understand the science behind the most pressing questions: how many people have been infected? How dangerous is the virus? When will a vaccine be available? How can the epidemic be contained, and the damages mitigated? What is the economic impact? What’s the role of social media and local communities in the epidemic response?

Researchers at the Jameel Institute and other research institutes at Imperial College London have been at the forefront of the response to the COVID-19 emergency, with clinical, epidemiological and social science analyses informing the outbreak response. They are generating robust empirical evidence that governments and international agencies are using around the world to plan their responses.

On this course, you will hear directly from our world-class experts about the theory behind the analyses of COVID-19 and its spread, while learning how to interpret new information using core principles of public health, epidemiology, medicine, health economics, and social science. You will be able to watch regular situation reports about the state of the epidemic, provided by the researchers of J-IDEA and its director Professor Neil Ferguson.

If you want to learn even more about these topics, a number of free MOOCs are available from Imperial College London. We also offer a fully online Global Master of Public Health for those of you who want to delve even deeper and join our professional community.

Please note:

This course was launched in February 2020 and we have continued to develop content as the COVID-19 situation progresses and new insights emerge. While we endeavour to include the most recent information, this is a fast-moving situation and information is constantly changing.

This course is to be used for educational purposes only and is open to all and free of charge.

The information in this course does not constitute clinical or other advice and must not be used for the purposes of providing any clinical or other advice. If you have any health concerns, please refer to your regional health authorities’ guidelines and consult a medical professional.

Please note the views expressed by individuals in the course content do not necessarily reflect those of Imperial College London, the Jameel Institute and any other funding partners.

Imperial College London, the Jameel Institute and any other funding partners shall not be liable, to the maximum extent permitted by law, for any loss suffered or for any other adverse or negative consequence arising directly or indirectly from your reliance on the information contained in this course.

Science Matters: Let's Talk About COVID-19

Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA)

Helen Ward
Katharina Hauck
Oliver Geffen Obregon


Building on the SIR Model

The other two courses in this specialisation require you to perform deterministic modelling - in other words, the epidemic outcome is predictable as all parameters are fully known. However, this course delves into the many cases – especially in the early stages of an epidemic – where chance events can be influential in the future of an epidemic. So, you'll be introduced to some examples of such ‘stochasticity’, as well as simple approaches to modelling these epidemics using R. You will examine how to model infections for which such ‘population structure’ plays an important role in the transmission dynamics, and will learn some of the basic approaches to modelling vector-borne diseases, including the Ross-McDonald Model.

Even if you are not designing and simulating mathematical models in future, it is important to be able to critically assess a model so as to appreciate its strengths and weaknesses, and identify how it could be improved. One way of gaining this skill is to conduct a critical peer review of a modelling study as a reviewer, which is an opportunity you'll get by taking this course.


This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

Mathematics for Machine Learning: Multivariate Calculus

Samuel J. Cooper
David Dye
A. Freddie Page


Android Graphics with OpenGL ES

This course will cover the fundamentals of OpenGL and OpenGL ES in Android.  This course is unique because it covers the mechanics of how OpenGL works and also more practical applications to draw 2D and 3D objects.  This is an ideal primer for more complex courses on VR and AR within Android.

We'll begin by covering the OpenGL Pipeline and Shading Language.  Then we'll look at drawing simple 2D objects and increasingly complicated 3D objects in OpenGL and OpenGL ES.

There are practical exercises throughout the course to apply your understanding, and there is a summative project which can form part of your professional portfolio.

4 weeks of study, around 5 hours per week.  The final project will take around 10 hours to complete.


Welcome to this course on Probabilistic Deep Learning with TensorFlow!

This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the  probabilistic approach to deep learning. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets. This is a crucial aspect when using deep learning models in applications such as autonomous vehicles or medical diagnoses; we need the model to know what it doesn't know.

You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library.

You will learn how probability distributions can be represented and incorporated into deep learning models in TensorFlow, including Bayesian neural networks, normalising flows and variational autoencoders. You will learn how to develop models for uncertainty quantification, as well as generative models that can create new samples similar to those in the dataset, such as images of celebrity faces.

You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills.

At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop a variational autoencoder algorithm to produce a generative model of a synthetic image dataset that you will create yourself.

This course follows on from the previous two courses in the specialisation, Getting Started with TensorFlow 2 and Customising Your Models with TensorFlow 2. The additional prerequisite knowledge required in order to be successful in this course is a solid foundation in probability and statistics. In particular, it is assumed that you are familiar with standard probability distributions, probability density functions, and concepts such as maximum likelihood estimation, change of variables formula for random variables, and the evidence lower bound (ELBO) used in variational inference.

Probabilistic Deep Learning with TensorFlow 2


The Health Protection course is the fourth instalment of the wider Foundations of Public Health Practice specialisation from Imperial College London's Global Master of Public Health (MPH). The scope and content of this course has been developed from the ground up by a combined team of academics and practitioners drawing on decades of real-world public health experience as well as deep academic knowledge. Through short video lectures, practitioner interviews and a wide range of interactive activities, learners will be immersed in the world of public health practice.

Designed for those new to the discipline, over three modules (intended for three weeks of learning), learners will become familiar with the scope, principles and nuances of health protection in the context of public health practice. Beginning with the basics of Water, Sanitation and Health (WASH) based interventions, the course will introduce learners to the science and principles of practical microbiology, before examining vaccines, incident management and the threat posed by a wide range of manmade and natural environmental threats. By the end of this course, learners will be familiar and conversant with core health protection principles and approaches, and confident in discussing health protection issues when they move into practice.

Foundations of Public Health Practice: Health Protection


Migration and health

Migration and health is the second instalment of the wider Global Health Challenges and Governance specialisation from Imperial College London's Global Master of Public Health (GMPH). The scope and content of this course has been developed from the ground up by a combined team of academics and practitioners drawing on a wealth of real-world public health experience as well as deep academic knowledge. Through short video lectures, readings and a wide range of interactive activities, learners will be immersed in the intersection of migration and health.

Designed for those new to the discipline, over four modules (intended for one week of learning each), learners will become familiar with the relationship between migration and health. Learners will be introduced to key terms and global trends in migrations, the profound impact of crises in non-health sectors on health, and policy instruments addressing migration. By the end of this course, learners will be able to describe international treaties protecting migrants’ right to health and migrant sensitive health systems. Learners will also be able to critically evaluate whether and how their national health system is providing universal health coverage to migrants.

The subsequent course in this specialisation requires the knowledge from this course, as learners will take a deep dive into climate change by applying their global health analytical skills and knowledge of multilateral policy instruments to this important global health challenge.


This specialization is intended for people interested in health systems and how they function. Participants will learn about the global health systems landscape and the challenges and opportunities to achieve better health outcomes.

This specialisation is divided into three courses which are offered as massive online open access courses (Courses 1-3), and a fourth course which is offered as part of the Online MPH degree (capstone). The Health Systems Development specialisation is geared toward learners who have no prior knowledge of health systems or those who are starting to explore this area of study. Overall, sessions in this specialisation span 16 week with approximately 96 hours of viewing learning materials per week. Formative assessments in the form of quizzes and activities are incorporated throughout the weeks to help learners gauge their level of depth of understanding and to prepare them for their summative assessments.

Participants will have the opportunity to explore a range of areas within health systems.
The first course will introduce the main building blocks of health systems and shed some light on key components of well-functioning health systems including how health system performance is assessed.
In course two, students will learn how to conduct a health impact assessment and how to assess the impacts of policies, plans and projects, as well as how that support decision-makers make choices regarding alternatives and improvements to prevent disease or injury and to actively promote health.
The third course explores human resources for health and service delivery. In  this course, students will learn about and analyse country experiences in transforming health services delivery, and interventions and to address human resources for health challenges at a global level.

By the end of this specialisation, learners should be able to identify key components of, and critically compare, different health systems; analyse country experiences in transforming   services; and apply theoretical framework for assessing the health impacts of policies, plans and projects.

Health Systems Development: A Focus on Health Service Delivery and Human Resources for Health


TensorFlow 2 시작하기

TensorFlow 2 시작하기 과정에 오신 것을 환영합니다!

이 과정에서는 순차 API를 사용한 모델 구축, 훈련, 평가 및 예측, 모델 검증, 정규화, 콜백 구현, 모델 저장 및 로딩 등 Tensorflow를 사용하여 딥 러닝 모델을 개발하기 위한 완벽한 엔드-투-엔드 워크플로우를 배우게 됩니다.

배운 개념을 실용적인 실습형 코딩 자습서에서 바로 연습할 것이며 이는 대학원 조교에게 안내를 받게 될 것입니다. 또한 기술을 통합할 수 있는 일련의 자동 채점 프로그래밍 과제가 있습니다.\n\n과정이 끝나면 이미지 분류기 딥 러닝 모델을 처음부터 개발하는 Capstone 프로젝트에 많은 개념을 통합할 것입니다.

Tensorflow는 오픈 소스 머신 라이브러리이며 딥 러닝에 가장 널리 사용되는 프레임워크 중 하나입니다. Tensorflow 2의 출시는 초심자에서 고급 수준에 이르기까지 모든 사용자의 사용 편의성에 중점을 둔 제품 개발의 단계적 변화를 나타냅니다. 이 과정은 Tensorflow 1.x에 대한 경험이 있는 사용자뿐만 아니라 경험이 없는 사용자 모두를 대상으로 합니다.

이 과정에서 성공하기 위해서는 파이썬 프로그래밍 언어(이 과정에서는 파이썬 3 사용), 일반적인 머신 러닝 개념(예: 과적합/과소적합, 지도 학습 작업, 검증, 정규화 및 모델 선택), 전형적인 모델 아키텍처(MLP/피드포워드 및 컨볼루션 신경망), 활성화 함수, 출력 레이어 및 최적화를 포함한 딥 러닝 분야의 실무 지식을 갖추고 있어야 합니다.


This course focuses on data, evaluation methods and the economic evaluation of digital health interventions. This module focuses on key data considerations for digital health including data management, data visualisation and methods for evaluating digital health interventions. The key focus is on experimental and quasi-experimental design approaches that can be applied to evaluating digital health interventions and key considerations for the economic evaluation of digital health interventions.

Evaluation of Digital Health Interventions


Globalisation and health governance

Globalisation and health governance is the first instalment of the wider Global Health Challenges and Governance specialisation from Imperial College London's Global Master of Public Health (GMPH). The scope and content of this course has been developed from the ground up by a combined team of academics and practitioners drawing on a wealth of real-world public health experience as well as deep academic knowledge. Through short video lectures, readings and a wide range of interactive activities, learners will be immersed in the world of global health.

Designed for those new to the discipline, over four modules (intended for one week of learning each), learners will become familiar with the scope, history, principles, stakeholders and conceptual frameworks of global health, globalisation and governance. But there is also important foundational content for those coming from more experienced backgrounds, as the course builds upon a strong tradition of advocacy in public health and broad perspective of the global health context by exploring institutional, economic, socio-cultural and ecological determinants of population health. By the end of the course, learners will be able to confidently describe how globalisation impacts health and health governance at local, national and multilateral levels, and craft well-reasoned, evidence-based arguments about global health challenges.

The subsequent courses require the knowledge from this course, as learners will take a deep dive into migration health and climate change by applying their foundational global health knowledge and advocacy skills to these important global health challenges.


Quality improvement methods were first deployed in healthcare in e hospital settings. However, over the past decade particularly, there has been increasing focus on the application of these methods in improving population and public health. In this course, you will apply a combination of both approaches to population health improvement. You will also learn how to study and evaluate large improvement initiatives to capture learning effectively.

Learning Objectives:

Apply quality improvement methods in combination with population health frameworks to design a population health improvement initiative

Understand how the use of geographic information systems contributes to quality improvement

Critique different study designs for studying quality improvement initiatives

Quality Improvement for Population Health


Public Involvement in Research

This course focuses on participatory approaches in research, known as 'public involvement' in the UK. You'll specifically, consider why citizens and patients would be involved in research and explore participatory approaches across and within the research cycle in more detail, diving into questions such as:
- what kinds of participation can be undertaken at each of the 7 stages of the cycle?
- how can you utilise participation in research?
- what examples of using participatory approaches exist in research?

While this course, as with the rest of the specialisation, focuses on public health and ways of involving citizens and patients across and within the research cycle, these concepts apply to other disciplines and kinds of research too. So, you don't have to be a public health specialist or work in healthcare to gain insight from this course.

If you would like to learn more about the theories and core principles of participation within a public health context, we suggest taking Introduction to Participatory Approaches in Public Health. If you're planning a research project and want to include participatory approaches, explore our course Applying Participatory Approaches in Public Health Settings.


This course reflects on global health challenges and the role of innovative solutions in addressing them. By engaging in this course, you will be able to describe the principles and key types of innovation in order to characterise the fundamental features of new models of care and technologies.

This course will review the basic features and principles of healthcare innovation. You'll be examining innovations developed to address global health challenges, ranging from simple low-cost technologies readily deployed in resource constrained settings to more complex combinations of organizational, business model and technology innovations. Throughout this course, you'll also consider how adoption and diffusion is influenced by social, economic and political factors and explore what is required to get an innovation in to practice, effectively, at scale.

4 weeks of study; approx. 4 hours per week

A Guide to Healthcare Innovation: Principles and Practice


How Vaccines Work

This course describes the underpinning science around vaccines and their development. It will look at the immunological underpinnings of how vaccines induce protection. It will also look at the epidemiological basis of how vaccines are rolled out. Finally it will explore how new vaccines are developed.

On successful completion of the course, learners will be able to:

1. Understand how the immune response mediates vaccine protection
2. Explain how vaccines enter public health programs
3. Demonstrate your understanding of the challenges for the generation of a vaccine against disease

This is an intermediate series of courses intended for both clinical and non-clinical scientists who want to update their skills for work in clinical, academic, and pharmaceutical/bioscience industries, and is developed and delivered by experts working on viruses and novel vaccines.

To be successful in this series of courses, you should have basic knowledge in biology, genetics, microbiology or related fields.


Immunology: Adaptive Immune System

In this course, you will learn about the role of immune responses of the adaptive (acquired) immune system which provides specialised immunity against pathogens.

Guided by our researchers in the Department of Immunity and Inflammation, we will take a closer look at the lymphocyte subsets and mechanisms involved in this delayed finely tuned response occurring days to weeks after the initial exposure to microbial antigens.

We will also focus on the versatile cellular components which can distinguish between self- and nonself antigens and on how age affects the immune responses.

Malgorzata (Maggie) Trela
Sophie Rutschmann


This course teaches you about the global health harms caused by tobacco and the efforts underway to combat these. You'll analyse the strategies and tactics used by the tobacco industry, and their allies, to keep people buying their products, and you'll reflect on the role played by public health research in pushing back against this pressure with the ultimate aim of improving health.

No matter your previous experience in this area, by the end of this course, you'll be able to describe the global harm to health caused by tobacco use and how policy is responding to this. You'll also be able to critique tobacco industry strategies that undermine tobacco control and discuss ways in which robust and timely research in strengthening tobacco control is key to policy and practice.

Tobacco Control: Agile Policy, Research and Practice