The Best Free Data Science eBooks

Every book in this collection of 25 books was either recommended to me by data science leaders, mentors, instructors or I got to them looking for help on a specific project. I hope that they are helpful to you!

Python is my go-to programming language and that is why most of the books are Python-based programming but if you have recommendations of other books in other languages, please share them on the comments or send me a tweet and I will add them.

Math and Statistics

  • An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.

Introduction to Statistical Learning

with Applications in R Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Home Download the book PDF…

Description: This book provides an introduction to statistical learning methods. It is aimed for upper-level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real-life settings and should be a valuable resource for a practicing data scientist.

  • Think Stats by Allen B. Downey

Think Stats 2e

by Allen B. Downey. Download this book in PDF. Code examples and solutions are available from this GitHub repository…

Description: Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.

  • The Elements of Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.

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Description: While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting — the first comprehensive treatment of this topic in any book.

  • Bayesian Statistics Made Simple by Allen B. Downey

Think Bayes

Bayesian Statistics Made Simple by Allen B. Downey Download Think Bayes in PDF. Read Think Bayes in HTML. Order Think…

Description: Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be integral in a math book becomes a summation, and most operations on probability distributions are simple loops.

  • Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilon

Bayesian Methods for Hackers

The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow…

Description: Bayesian Methods for Hackers is designed as an introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course, as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background or one who is not interested in mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.

  • Computer Age Statistical Inference by Bradley Efron and Trevor Hastie

Computer Age Statistical Inference: Algorithms, Evidence and Data Science

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence…

Description: This book takes us on a journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories — Bayesian, frequentist, Fisherian — individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The book integrates methodology and algorithms with statistical inference and ends with speculation on the future direction of statistics and data science.


  • The Elements of Data Analytic Style by Jeffrey Leek

The Elements of Data Analytic Style

A guide for people who want to analyze data. Free! Data analysis is at least as much art as it is science. This book is…

Description: This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. The book is useful as a companion to introductory courses in data science or data analysis. It is also a useful reference tool for people tasked with reading and critiquing data analyses.

Data Mining

  • A Programmer’s Guide to Data Mining by Ron Zacharsk

The Ancient Art of the Numerati

Chapters 1: Introduction 2: Recommendation systems 3: Item-based filtering 4: Classification 5: More on classification…

Description: If you are a programmer interested in learning a bit about data mining you might be interested in a beginner’s hands-on guide as a first step. That’s what this book provides. This guide follows a learn-by-doing approach.

  • Social Media Mining by Cambridge University Press

Download Book

The Social Media Mining book is published by Cambridge University Press in 2014. Please see Cambridge’s page for the…

Description: Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.

Non-Technical Introduction to Data Science

  • The Art of Data Science by Roger D. Peng and Elizabeth Matsui

The Art of Data Science

This book describes the process of analyzing data. The authors have extensive experience both managing data analysts…

Description: 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.

  • Data Science Handbook by Carl, Max, Henry, and Will

The Data Science Handbook

Check out The Data Science Handbook! A compilation of interviews from leading data scientists at Uber, Palantir…

Description: The Data Science Handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice.

  • Conversations On Data Science by Roger D. Peng and Hilary Parker

Conversations On Data Science

Roger Peng and Hilary Parker started the Not So Standard Deviations podcast in 2015, a podcast dedicated to discussing…

Description: Roger Peng and Hilary Parker started the Not So Standard Deviations podcast in 2015, a podcast dedicated to discussing the backstory and day to day life of data scientists in academia and industry. This book collects many of their conversations about data science and how it works (and sometimes doesn’t work) in the real world.

Python Programming for Data Science

  • Object-Oriented Programming with Python by Ashwin Pajankar and Sushant Garg (the image don’t match but it is the right link)

Object Oriented Programming with Python

This book is the simple and definitive guide to the Python 3 Object Oriented Programming. Other book of the similar…

Description: This book is a simple and definitive guide to the Python 3 Object-Oriented Programming. Other books of similar genres make use of complicated writing style and examples to introduce the readers to the OOP in Python 3. However, this book uses simple language to explain concepts. It is aimed at intermediate learners who already know Python.

  • Automate the Boring Stuff with Python by Al Sweigart

Automate the Boring Stuff with Python

Practical programming for total beginners. Written by Al Sweigart. If you’ve ever spent hours renaming files or…

Description: You’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required. Once you’ve mastered the basics of programming

  • Python Data Science Handbook by Jake VanderPlas

Python Data Science Handbook

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and…

Description: Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

  • Learn Python, Break Python by Scott Grant

Learn Python, Break Python

Learn Python, Break Python is a hands-on introduction to the Python programming language, written for people who have…

Description: Learn Python, Break Python starts with a gentle introduction to programming. Slowly, through examples and exercises, we build up to a level of comfort by introducing more complicated program elements and show where they can be used and how we can break them. By building up knowledge in this way, we hope to impart a level of comfort that will make you comfortable trying new things and taking risks; in short, we want you to be comfortable with programming.

  • Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper


Steven Bird, Ewan Klein, and Edward Loper This version of the NLTK book is updated for Python 3 and NLTK 3. The first…

Description: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you’ll learn how to write Python programs that work with large collections of unstructured text. You’ll access richly annotated datasets using a comprehensive range of linguistic data structures, and you’ll understand the main algorithms for analyzing the content and structure of written communication.

  • Data Science in Production by Ben G Weber

Data Science in Production

From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize…

Description: From startups to trillion-dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book is intended for analytics practitioners that want to get hands-on with building data products across multiple cloud environments and develop skills for applied data science.

  • Data-Driven by Hilary Mason, DJ Patil

Data Driven

Succeeding with data isn’t just a matter of putting Hadoop in your machine room, or hiring some physicists with crazy…

Description: Examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century.

Machine Learning

  • Hands-on Machine Learning with Scikit-Learn and TensorFlow
    by Aurélien Géron


This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and…

Description: By using concrete examples, minimal theory, and two production-ready Python frameworks — scikit-learn and TensorFlow — author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks.

  • Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David

Description: These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.

  • Reinforcement Learning: An Introduction by Richard S. Sutton
    and Andrew G. Barto

Reinforcement Learning: An Introduction

Buy from Amazon Errata and Notes Full Pdf Without Margins Code Solutions — send in your solutions for a chapter, get…

Description: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field’s key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

  • Deep Learning by MIT Press book

Deep Learning

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine…

Description: The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.

  • Machine Learning Yearning by

Machine Learning Yearning –

AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to…

Description: AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work.

Data Visualization

  • D3 Tips and Tricks by Malcolm Maclean

D3 Tips and Tricks v3.x

Over 600 pages of tips and tricks for using d3.js, one of the leading data visualization tools for the web. It's…

Description: Over 600 pages of tips and tricks for using d3.js, one of the leading data visualization tools for the web. It’s aimed at getting you started and moving you forward. Includes over 50 downloadable code examples.


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