Books

A complete foundation for Statistics, also serving as a foundation for Data Science. Leanpub revenue supports OpenIntro (US-based nonprofit) so we can provide free desk copies to teachers interested in using OpenIntro Statistics in the classroom and expand the project to support free textbooks in other subjects. More resources: openintro.org.

The book is also available in paperback for $25. Paperback royalties go to OpenIntro (US-based nonprofit), and the optional Leanpub PDF contributions go to authors to fund their time on this book.

800 pages. 11 chapters. The full forecasting stack in Python — from ARIMA to foundation models — with production-grade code and proper evaluation. No hype.

Master language models through mathematics, illustrations, and code―and build your own from scratch!

Everything you really need to know in Machine Learning in a hundred pages.

Introduction to Statistics for the Life and Biomedical Sciences is the 4th official OpenIntro book and has been written to be used in conjunction with a set of self-paced learning labs. These labs guide students through learning how to apply statistical ideas and concepts discussed in the text with the R computing language.

Longitudinal Data Analysis Using R is a practical, end-to-end guide to longitudinal data analysis that helps you move from raw data to credible substantive conclusions. It tackles the real pain points researchers face in longitudinal analysis, such as data preparation, exploring change over time, model choice, missing data, and interpretation, using clear explanations, real-world data, and fully reproducible R code.

AI engines are booming, and the more we work with agentic systems, the more we see that we need something to make them work at the enterprise level. We're quite active in exploring ideas around context graphs, decision traces, and supporting explainability—giving agents the ability to make more aware and company-aligned decisions.But this makes sense not only for enterprises, but for users and individuals building personal agents as well. Unfortunately, we have zero-to-none inclination on how to actually build a context graph.I'll try to explain how to build something like a context graph—but go beyond it. I deeply believe that to make this work, we need specific agentic memory and a set of cognitive processes that truly help agents use this memory and learn from experience and data.That's why this is the Book: Beyond Context Graphs—with a focus on real-life enterprise tasks and how to make agents make better decisions and, let's say, hallucinate less.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

This book teaches you how to make machine learning models more interpretable.

Revised for PyTorch 2.x! In 2019, I published a PyTorch tutorial on Towards Data Science and I was amazed by the reaction from the readers! Their feedback motivated me to write this book to help beginners start their journey into Deep Learning and PyTorch. I hope you enjoy reading this book as much as I enjoy writing it.

Most people think they are bad at math. In reality, they were never taught arithmetic properly.This book is a modern English edition of Arithmetic by Alexander P. Kiselev—the text that formed the backbone of mathematical education in Russia and USSR for over a century and helped produce generations of exceptionally strong mathematicians, scientists, and engineers.Unlike modern textbooks that prioritise shortcuts, visuals, and lowered expectations, Kiselev builds arithmetic logically, systematically, and rigorously. Every method is explained. Every operation has meaning. Exercises are carefully sequenced to develop real understanding—not rote pattern-following.This book does not promise “easy math”. It promises something better: clarity, confidence, and competence.Whether you are a student, a parent, a tutor, or an adult rebuilding fundamentals, this book will change how you understand arithmetic—and why so much later mathematics suddenly becomes easier.

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts from probability, statistical inference, linear regression and machine learning and R programming skills. Throughout the book we demonstrate how these can help you tackle real-world data analysis challenges.

This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science. Printed copies of this book are available through Lulu.

This book teaches you to use R to effectively visualize and explore complex datasets. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modeling strategies. This book is based on the industry-leading Johns Hopkins Data Science Specialization.