Mindware: Critical Thinking for the Information Age (Coursera)

Mindware: Critical Thinking for the Information Age (Coursera)

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.

Syllabus

WEEK 1
Introduction
Individuals and cultures can make themselves smarter. Since the beginning of the Industrial Revolution, people have become enormously smarter. The Information Age requires a brand-new set of skills involving statistics, probability, cost-benefit analysis, principles of cognitive psychology, logic and dialectical reasoning.
Lesson 1: Statistics
Basic concepts of statistics and probability including the concepts of variable, normal distribution, standard deviation, correlation, reliability, validity, and effect size. Concrete examples are drawn from everyday life and show how the concepts can be used to solve ordinary problems.
Lesson 2: The Law of Large Numbers
How to think about events in such a way that they can be counted and a decision can be made about how much data is enough. You will learn about the concept of error variance and how it can be combatted by obtaining multiple observations. Your will learn that your judgments about people’s personalities are prone to serious errors that are largely avoided for judgments about abilities. And you will discover why it’s usually a mistake to interview job applicants.

WEEK 2
Lesson 3: Correlation
It can be extremely difficult to make an accurate assessment of how two variables are related to one another; prior beliefs can be more important than data in estimating the strength of a given relationship. You will learn simple tools to estimate degree of association. You will learn about the nature of illusory correlations and how to avoid them. You will learn about the concepts of confounded variable and self-selection error.
Lesson 4: Experiments
You will learn that correlations can only rarely provide conclusive evidence about whether one variable exerts a causal influence on another and why experiments provide far better evidence about causality than correlations. You will be shown how to conduct experiments in business settings and experiments on yourself. You will learn the distinction between within subject designs and between subject designs. You will learn about the concept of artifacts and some tricks for avoiding them. You will learn how to discover natural experiments.

WEEK 3
Lesson 5: Prediction
You will learn about the kinds of systematic errors we make when trying to predict the future. You will learn about regression to the mean and why you should assume that extreme values on a variable will be less extreme when next observed. You will learn how to think about observations in terms of true score plus error. You will learn about the concept of base rate and why it must be taken into account when estimating probabilities of specific events.
Lesson 6: Cognitive Biases
We understand the world not through direct perception but through inferential procedures that we are unaware of. Our understanding of the world is heavily influenced by schemas or abstract representations of events. We are prone to serious judgment errors that can be avoided to a degree when we understand their basis. We make guesses about probability and causality by applying the representativeness heuristic based on similarity assessments which can be very misleading. We make judgments about frequency and probability by relying in part on the availability heuristic, judging things as frequent or probable to the degree that instances come readily to mind.

WEEK 4
Lesson 7: Choosing and Deciding
How to conduct a cost-benefit analysis. Why you should throw the analysis away after doing it if the decision is personal and very important. How to avoid throwing good money after bad. How to avoid doing something that will prevent you from doing something more valuable. Why it can be expensive to try to avoid the possibility of loss. Why incentives can backfire.
Lesson 8: Logic and Dialectical Reasoning
The distinction between inductive logic and deductive logic. Syllogisms. Conditional reasoning. The distinction between truth of an argument and validity of an argument. The concepts of necessity and sufficiency. Venn diagrams. Common logical errors. When to avoid contradiction and when to embrace it, how to avoid undue certainty about judgments and decisions, and why attention to context rather than form is crucial for analysis of most real-world problems.
Conclusion

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Outbreaks and Epidemics (Coursera) Coursera
Johns Hopkins University

Outbreaks and Epidemics (Coursera)

Professional epidemiologists are often called on to investigate outbreaks and epidemics. This course serves as an introduction to the essentials of investigation, identifying pathogens, figuring out what's going on, reporting, and responding. You'll learn how to ask precise epidemiologic questions and apply epidemiologic tools to uncover the answers.

Jun 8th 2026
4 Weeks
The City and You: Find Your Best Place (Coursera) Coursera
University of Toronto

The City and You: Find Your Best Place (Coursera)

Welcome to The City and You: Find Your Best Place. I'm excited to have you in the class and look forward to your contributions to the other learners in our community. This free course will provide the knowledge and the tools needed to understand what cities do, why they matter, the forces shaping the greatest wave of urbanization in history, and how to pick the right place for you.

Jun 8th 2026
5-12 Weeks
Exploratory Data Analysis (Coursera) Coursera
Johns Hopkins University

Exploratory Data Analysis (Coursera)

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.

Jun 8th 2026
4 Weeks
Introduction to Probability and Data with R (Coursera) Coursera
Duke University

Introduction to Probability and Data with R (Coursera)

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.

Jun 8th 2026
5-12 Weeks
Inferential Statistics (Coursera) Coursera
Duke University

Inferential Statistics (Coursera)

This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data.

Jun 8th 2026
5-12 Weeks
Pensamiento Científico (Coursera) Coursera
Universidad Nacional Autónoma de México

Pensamiento Científico (Coursera)

¿El pensamiento científico es sólo para científicos? Su utilidad va mucho más allá, ayudando a las personas a tomar mejores decisiones todos los días. El objetivo de este curso es fomentar en pensamiento científico en los alumnos para ayudarles a tomar mejores decisiones profesionales, personales y sociales.

Jun 8th 2026
5-12 Weeks
Statistics for Data Science with Python (Coursera) Coursera
IBM

Statistics for Data Science with Python (Coursera)

This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts.

Jun 8th 2026
5-12 Weeks
Delivery Problem (Coursera) Coursera
University of California, San Diego,Higher School of Economics - HSE University

Delivery Problem (Coursera)

We’ll implement (in Python) together efficient programs for a problem needed by delivery companies all over the world millions times per day — the travelling salesman problem. The goal in this problem is to visit all the given places as quickly as possible. How to find an optimal solution to this problem quickly? We still don’t have provably efficient algorithms for this difficult computational problem and this is the essence of the P versus NP problem, the most important open question in Computer Science.

Jun 8th 2026
3 Weeks
Basic Statistics (Coursera) Coursera
University of Amsterdam

Basic Statistics (Coursera)

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression.

Jun 8th 2026
5-12 Weeks
Statistical Inference (Coursera) Coursera
Johns Hopkins University

Statistical Inference (Coursera)

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

Jun 8th 2026
4 Weeks
Inferential Statistics (Coursera) Coursera
University of Amsterdam

Inferential Statistics (Coursera)

Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population. We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs.

Jun 8th 2026
5-12 Weeks