Bruce P. Practical Statistics for Data Scientists...3ed 2026 Early Release

Textbook in PDF format

Statistical methods are a key part of Data Science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a Data Science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to Data Science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
Many Data Science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
In all cases, this book gives code examples first in R and then in Python. In order to avoid unnecessary repetition, we generally show only output and plots created by the R code. We also skip the code required to load the required packages and data sets. You can find the complete code as well as the data sets for download at GitHub.
Preface
Exploratory Data Analysis (available)
Data and Sampling Distributions (unavailable)
Statistical Experiments and Significance Testing (unavailable)
Regression and Prediction (unavailable)
Classification (unavailable)
Statistical Machine Learning (unavailable)
Unsupervised Learning (unavailable)
Neural Networks (unavailable)
Deep Learning and Reinforcement Learning (unavailable)
LLMs and Generative AI (available)
Caveats and Concerns (unavailable)