Modern Statistics

Mike X Cohen's Modern Statistics: Intuition, Math, Python, R is a university-level textbook for students and working professionals who want a solid grounding in statistics for data science and machine learning. At roughly 700 pages, it includes 390 figures and more than 35,000 lines of code. The code, along with a sample chapter, is available on GitHub at no cost.

The book opens with the basics: what data are and how to visualize them. From there it moves through descriptive statistics, data simulation, transformations, and methods for assessing and improving data quality. Probability, sampling, and probability distributions lay the groundwork for hypothesis testing, which the book examines in depth, including the family of t-tests.

Practical application runs throughout. Python and R examples illustrate correlation, confidence intervals, ANOVA, and regression. Later chapters address permutation tests, power and sample size calculations, sources of bias, and how to communicate findings clearly, with an emphasis on sound methodology in research and professional work.

The text connects statistical theory with the software tools used on real datasets today, preparing readers to handle complex analytical problems. It suits both newcomers to statistics and experienced professionals who want to sharpen their skills and think carefully about how data should be interpreted.