Data Science

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.

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.

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

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

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.

Modern Computational Statistics with R teaches statistics as a disciplined way of thinking: start with the scientific question, design, data, and uncertainty before reaching for formulas. Through real examples, simulations, and R, readers learn how to turn data into defensible evidence and build the statistical foundations needed for modern data science, machine learning, and AI.

This book teaches you how to assemble and lead a data science enterprise so that your organization can move towards extracting information from big data. This book is based on the acclaimed Johns Hopkins Executive Data Science Specialization. Printed copies of this book are available through Lulu.

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.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

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.

Bridge AI and science with this hands-on guide. Whether you're a researcher learning ML or an engineer entering scientific applications, build real systems across chemistry, biology, physics & climate. Master Transformers, Diffusion Models & GNNs for scientific discovery. 500+ pages, 50+ Colab notebooks. Design molecules, predict proteins, accelerate climate models—all hands-on, zero setup required.

This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science. Printed copies are available through Lulu.

Master machine learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your machine learning applications.

Data analysis is now part of practically every research project in the life sciences. In this book we use data and computer code to teach the necessary statistical concepts and programming skills to become a data analyst. Instead of showing theory first and then applying it to toy examples, we start with actual applications and describe the theory as it becomes necessary to solve specific challenges. The book includes links to computer code that readers can use to follow along as they program.