A comprehensive guide to modern statistics with hands-on implementations in both Python and R.
690 pages | 19 chapters | 390 figures | 150 exercises | 45,000 lines of code
This book teaches statistics from the ground up, combining mathematical rigor with practical coding implementations. Every concept is explained with clear intuition, formal mathematics, and working code in both Python and R.
Preview Table of Contents | Free sample chapter: T-tests
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This repository contains all Python and R code from the book:
- Complete implementations for all 390 figures
- Simulations demonstrating statistical concepts
- Solutions to all 150 exercises
- Side-by-side Python and R examples
| Format | Link |
|---|---|
| Paperback | Amazon |
| PDF version | Gumroad |
Listen to the complete book read by the author—perfect for learning on the go, following along as you read, or even falling asleep to statistics.
Available on all major podcast platforms:
Watch detailed walkthroughs of exercise instructions and Python solutions on YouTube:
- Foundational statistical concepts and inference
- Hypothesis testing, regression, and ANOVA
- Bayesian and frequentist approaches
- Practical implementation in Python and R
- Real-world applications and simulations
Mike X Cohen, PhD - Former neuroscience professor, full-time educator, and Udemy bestselling instructor with 25 years of experience teaching statistics, mathematics, and data science.
All code in this repository is free to use for learning. The book provides the context, theory, and exercises that bring the code to life.