Resources
The book comes with much more than slides and code. Everything below is free: courses, interactive tools, practice material, and what to read next. Most of it lives in our GitHub organization.
Courses and tools
Coding courses
Full open courses that take you from zero to doing the case studies yourself, in R, Python, or Stata.
Data Analysis with AI
A full open course on doing data analysis with LLMs and agentic tools: eight sessions, assignments, case studies, and a capstone.
Interactive dashboards
Play with the concepts in your browser: distributions, generalization, measurement error, regression, confounders, common support, panel models, and the hotels case study.
Read more
Books
Our recommendations: popular books on data and decisions, sports, dataviz, and advanced books on prediction and causal inference.
Extra reading by chapter
Papers, posts and stories we found after the book came out, organised by chapter.
Blogs, articles, podcasts
Non-technical articles, blog posts and podcasts on data science and statistics.
Beyond the book
Directions to the frontier: advanced topics the book does not cover, and where to learn them.
Data, code and skills
Data source ideas
Where to find data for assignments, term projects, or just practice.
Learning to code: more resources
Our favourite external books and courses for R, Python and Stata.
Data job ads
Real job ads in data analysis, to see what skills employers ask for.
Also here: Gábor’s mantra on research with data and an English–Hungarian data analysis dictionary.
Teaching with the book? See the instructor hub. Found something we should add? Tell us.