Overview
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In this Specialization, you’ll strengthen critical thinking and decision science by combining model thinking, computational problem solving, evidence-based reasoning, and practical GenAI prompting. You’ll learn how different models explain the same situation in different ways—so you can choose better assumptions, spot tradeoffs, and test strategies before you act. You’ll also practice turning messy problems into clear steps a computer could execute, using problem identification, decomposition, pattern recognition, abstraction, algorithms, and evaluation.
Across lessons on statistics, the law of large numbers, correlation, experiments, prediction, cognitive biases, and logic, you’ll build habits for judging claims you see at work and in the media. You’ll then use GenAI as a thinking partner for customizing a Critical Thinking Framework, selecting and guiding models with clear constraints, and improving reliability by asking for evidence, alternatives, and checks.
By the end of this Specialization, you will be able to:
Apply model-based reasoning (e.g., networks, games, randomness) to explain outcomes and compare decisions. Decompose real-world problems into algorithms and evaluate solutions against goals and constraints. Judge evidence using statistical and scientific reasoning, including experiments vs. correlations and uncertainty. Build and reuse GenAI prompts that support critical thinking with explicit assumptions, sources, and evaluation steps.
Syllabus
- Course 1: Model Thinking
- Course 2: Problem Solving Using Computational Thinking
- Course 3: Mindware: Critical Thinking for the Information Age
- Course 4: Leveraging GenAI to Develop Critical Thinking Skills
Courses
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We live in a complex world with diverse people, firms, and governments whose behaviors aggregate to produce novel, unexpected phenomena. We see political uprisings, market crashes, and a never ending array of social trends. How do we make sense of it? Models. Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one. Why do models make us better thinkers? Models help us to better organize information - to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians. The models covered in this class provide a foundation for future social science classes, whether they be in economics, political science, business, or sociology. Mastering this material will give you a huge leg up in advanced courses. They also help you in life. Here's how the course will work. For each model, I present a short, easily digestible overview lecture. Then, I'll dig deeper. I'll go into the technical details of the model. Those technical lectures won't require calculus but be prepared for some algebra. For all the lectures, I'll offer some questions and we'll have quizzes and even a final exam. If you decide to do the deep dive, and take all the quizzes and the exam, you'll receive a Course Certificate. If you just decide to follow along for the introductory lectures to gain some exposure that's fine too. It's all free. And it's all here to help make you a better thinker!
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Most professions these days require more than general intelligence. They require in addition the ability to collect, analyze and think about data. Personal life is enriched when these same skills are applied to problems in everyday life involving judgment and choice. This course presents basic concepts from statistics, probability, scientific methodology, cognitive psychology and cost-benefit theory and shows how they can be applied to everything from picking one product over another to critiquing media accounts of scientific research. Concepts are defined briefly and breezily and then applied to many examples drawn from business, the media and everyday life. What kinds of things will you learn? Why it’s usually a mistake to interview people for a job. Why it’s highly unlikely that, if your first meal in a new restaurant is excellent, you will find the next meal to be as good. Why economists regularly walk out of movies and leave restaurant food uneaten. Why getting your picture on the cover of Sports Illustrated usually means your next season is going to be a disappointment. Why you might not have a disease even though you’ve tested positive for it. Why you’re never going to know how coffee affects you unless you conduct an experiment in which you flip a coin to determine whether you will have coffee on a given day. Why it might be a mistake to use an office in a building you own as opposed to having your office in someone else’s building. Why you should never keep a stock that’s going down in hopes that it will go back up and prevent you from losing any of your initial investment. Why it is that a great deal of health information presented in the media is misinformation.
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Have you ever heard that computers "think"? Believe it or not, computers really do not think. Instead, they do exactly what we tell them to do. Programming is, "telling the computer what to do and how to do it." Before you can think about programming a computer, you need to work out exactly what it is you want to tell the computer to do. Thinking through problems this way is Computational Thinking. Computational Thinking allows us to take complex problems, understand what the problem is, and develop solutions. We can present these solutions in a way that both computers and people can understand. The course includes an introduction to computational thinking and a broad definition of each concept, a series of real-world cases that illustrate how computational thinking can be used to solve complex problems, and a student project that asks you to apply what they are learning about Computational Thinking in a real-world situation. This project will be completed in stages (and milestones) and will also include a final disaster response plan you'll share with other learners like you. This course is designed for anyone who is just beginning programming, is thinking about programming or simply wants to understand a new way of thinking about problems critically. No prior programming is needed. The examples in this course may feel particularly relevant to a High School audience and were designed to be understandable by anyone. You will learn: -To define Computational Thinking components including abstraction, problem identification, decomposition, pattern recognition, algorithms, and evaluating solutions -To recognize Computational Thinking concepts in practice through a series of real-world case examples -To develop solutions through the application of Computational Thinking concepts to real world problems
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Our brains process more information each day than any generation in history, making critical thinking one of the most valuable skills we can develop. This course invites you to strengthen your ability to use critical thinking skills and make sound decisions by adopting generative artificial intelligence to support and enhance your reasoning processes. By implementing the practical, actionable framework presented in this course, you’ll discover how AI can help unpack complex ideas, highlight hidden assumptions, and facilitate the confidence and clarity of considering multiple perspectives. This course will help you strengthen your ability to evaluate, assign meaning to, discount, or reject information from a wide range of public and private sources, and better make informed decisions in both your professional and personal life.
Taught by
Chris Quintana, John K. Thompson, Richard E. Nisbett and Scott E. Page