St
Statistics recommends starting with
@statistics · Aug 10
Khan Academy's Statistics and Probability course is thorough, patient, and free, and it covers everything a first university statistics course would — from describing data through probability, sampling distributions, confidence intervals, and significance tests. Each short video is followed by practice problems with hints, so you find out immediately whether an idea has landed. Work through it unit by unit and don't skip the practice; that's where the understanding comes from.
Hernán and Robins' standard text on causal inference from observational data. Free to download.
A friendly, modern introduction to Bayesian thinking with R.
Gelman, Hill, and Vehtari on regression done carefully. Practical, skeptical, and free to download.
McElreath's Bayesian course changes how you think about models and causation. Lectures free.
A compact course on descriptive statistics, inference, and regression. A good-value refresher.
The Open University's free short courses on data and statistics. Clear, self-paced, and well structured.
Allen Downey teaches statistics through Python code rather than formulas. Great for programmers.
Allen Downey / Green Tea Press
MIT's balanced course covering frequentist and Bayesian methods, with complete class materials.
Joe Blitzstein's famous probability course. Hard, beautiful problems; lectures free online.
University of Michigan's applied statistics, taught through Python notebooks. Good for analysts who code.
Duke's thorough treatment of inference, regression, and Bayesian statistics, using R.
A free, peer-reviewed introductory textbook used in hundreds of courses. Excellent exercises.
OpenIntro
Josh Starmer makes p-values, distributions, and regression understandable. Great alongside any course.
Brown University's visual introduction to probability and statistics. Beautiful, interactive, and short.
Brown University
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