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6 resources
Paper by Andrew Gelman and Cosma Shalizi on how Bayesian statistics is practiced and the philosophy of science behind it. arXiv preprint, not peer reviewed in this version.
Gwern Branwen’s long-form essays and meta-analyses on AI, psychology, statistics, technology and self-experiments.
From the site: Personal website of Gwern Branwen (writer, self-experimenter, and programmer): topics: psychology, statistics, technology, deep learning, anime. This index page is a categorized list of Gwern.net pages.
A large free archive of practitioner-written tutorials and explainers on machine learning, statistics, data engineering and AI, from beginner to advanced.
Why I recommend it: Quality varies by author, but the beginner explainers are among the easiest free routes into data work.
Scott Alexander's free long-form blog on statistics, medicine, forecasting, AI risk and how to reason carefully about contested claims.
Why I recommend it: Read it for the reasoning habits rather than the conclusions — the posts on evaluating evidence are useful in any field.
Free harmonized microdata from the monthly U.S. Current Population Survey (CPS), covering 1962 to the present. Includes demographics, employment, program participation and supplemental topics such as food security, computer and internet use, and voter registration.
Why I recommend it: A public dataset you can use for market research, policy analysis, or building data-driven career and business arguments. Registration is instant and extracts are free.
Statista chart showing how people are using AI tools for education and learning purposes.
Why I recommend it: Useful data point for anyone building or explaining AI-powered learning tools.
