# 100+ Free Data Science Books for 2017

Pulled from the web, here is a our collection of the best, free books on Data Science, Big Data, Data Mining, Machine Learning, Python, R, SQL, NoSQL and more.

If you’re looking for even more learning materials, be sure to also check out an online data science course.

Note that while every book here is provided for free, consider purchasing the hard copy if you find any particularly helpful. In many cases you will find Amazon links to the printed version, but bear in mind that these are affiliate links, and purchasing through them will help support not only the authors of these books, but also LearnDataSci. Thank you for reading, and thank you in advance for helping support this website.

# Data Science in General

# An Introduction to Data Science

# An Introduction to Data Science

#### by Jeffrey Stanton, 2013

# School of Data Handbook

# School of Data Handbook

#### by School of Data, 2015

# Data Jujitsu: The Art of Turning Data into Product

# Data Jujitsu: The Art of Turning Data into Product

#### by DJ Patil, 2012

# Art of Data Science

# Art of Data Science

#### by Roger D. Peng & Elizabeth Matsui, 2015

# Interviews with Data Scientists

# The Data Science Handbook

# The Data Analytics Handbook

# The Data Analytics Handbook

#### by Brian Liou, Tristan Tao, & Declan Shener, 2015

# Forming Data Science Teams

# Data Driven: Creating a Data Culture

# Building Data Science Teams

# Understanding the Chief Data Officer

# Understanding the Chief Data Officer

#### by Julie Steele, 2015

# Data Analysis

# The Elements of Data Analytic Style

# Distributed Computing Tools

## Hadoop

# Hadoop Tutorial as a PDF

# Hadoop Tutorial as a PDF

#### by Tutorials Point

# Cloudera Impala

# Data-Intensive Text Processing with MapReduce

# Hadoop Illuminated

# Programming Pig

# Programming Pig

#### by Alan Gates, 2011

# Learning Languages

## Python

# Think Python: How would you Think Like a Computer Scientist

# Python Programming

# Automate the Boring Stuff with Python: Practical Programming for Total Beginners

# Automate the Boring Stuff with Python: Practical Programming for Total Beginners

#### by Al Sweigart, 2015

# Learn Python the Hard Way

# Dive Into Python 3

# Test-Driven Development with Python

# A Byte of Python

# Invent with Python

# Python for Informatics: Exploring Information

# Python Practice Book

# Python Practice Book

#### by Anand Chitipothu, 2014

# Learn Python, Break Python

# Python Cookbook

# Learning with Python 3

# Learning with Python 3

#### by Peter Wentworth, Jeffrey Elkner, Allen B. Downey, & Chris Meyers, 2012

# Python for You and Me

# Python for You and Me

#### by Kushal Das, 2015

## R

# R Programming for Data Science

# R Programming for Data Science

#### by Roger D. Peng,

# R Programming

# R Programming

#### by Wikibooks, 2014

# Advanced R

# A Little Book of R for Time Series

# A Little Book of R for Time Series

#### by Avril Coghlan, 2015

# The R Manuals

# The R Manuals

#### by R Development Core Team

# Learning Statistics with R

# Learning Statistics with R

#### by Daniel Navarro, 2015

# R by Example

# R by Example

#### by Ajay Shah, 2005

# Practical Regression and Anova using R

# Practical Regression and Anova using R

#### by Julian J. Faraway, 2002

# The R Inferno

# Ecological Models and Data in R

# Spatial Epidemiology Notes: Applications and Vignettes in R

# Spatial Epidemiology Notes: Applications and Vignettes in R

#### by Charles DiMaggio, 2014

# SQL, NoSQL, and Databases

## SQL

# Learn SQL The Hard Way

# Learn SQL The Hard Way

#### by Zed. A. Shaw, 2010

# SQL Tutorial as a PDF

# SQL Tutorial as a PDF

#### by Tutorials Point

# SQL for Web Nerds

# SQL for Web Nerds

#### by Philip Greenspun

## Cassandra

# Cassandra Tutorial as a PDF

# Cassandra Tutorial as a PDF

#### by Tutorials Point, 2015

# CouchDB: The Definitive Guide

# CouchDB: The Definitive Guide

#### by J. Chris Anderson, Jan Lehnardt, & Noah Slater

## MongoDB

# The Little MongoDB Book

# MongoDB Succinctly

# MongoDB Succinctly

#### by Agus Kurniawan

## NoSQL in General

# Extracting Data from NoSQL Databases

# Extracting Data from NoSQL Databases

#### by Petter Näsholm, 2012

# NoSQL Databases

# NoSQL Databases

#### by Christof Strauch

## Other Database

# Graph Databases

# Graph Databases

#### by Ian Robinson, Jim Webber, & Emil Eifrem, 2013

# Data Mining and Machine Learning

# Introduction to Machine Learning

# Introduction to Machine Learning

#### by Amnon Shashua, 2008

# Introduction to Machine Learning

# Introduction to Machine Learning

#### by Alex Smola & S.V.N. Vishwanathan, 2008

# Machine Learning

# Machine Learning

#### by Abdelhamid Mellouk & Abdennacer Chebira, 450

# Machine Learning – The Complete Guide

# Machine Learning – The Complete Guide

#### by Wikipedia

# Social Media Mining An Introduction

# Social Media Mining An Introduction

#### by Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014

# Data Mining: Practical Machine Learning Tools and Techniques

# Data Mining: Practical Machine Learning Tools and Techniques

#### by Ian H. Witten & Eibe Frank,2005

# Mining of Massive Datasets

# A Programmer’s Guide to Data Mining

# A Programmer’s Guide to Data Mining

#### by Ron Zacharski, 2015

# Data Mining with Rattle and R

# Data Mining and Analysis: Fundamental Concepts and Algorithms

# Data Mining and Analysis: Fundamental Concepts and Algorithms

#### by Mohammed J. Zaki & Wagner Meria Jr., 2014

# Probabilistic Programming & Bayesian Methods for Hackers

# Machine Learning, Neural and Statistical Classification

# Machine Learning, Neural and Statistical Classification

#### by D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999

# Information Theory, Inference, and Learning Algorithms

# Bayesian Reasoning and Machine Learning

# Gaussian Processes for Machine Learning

# Reinforcement Learning: An Introduction

# Algorithms for Reinforcement Learning

# Modeling With Data

# Modeling With Data

#### by Ben Klemens, 2008

# KB – Neural Data Mining with Python Sources

# Deep Learning

# Deep Learning

#### by Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015

# Neural Networks and Deep Learning

# Neural Networks and Deep Learning

#### by Michael Nielsen, 2015

# Data Mining Algorithms In R

# Data Mining Algorithms In R

#### by Wikibooks, 2014

# Data Mining and Analysis: Fundamental Concepts and Algorithms

# Data Mining and Analysis: Fundamental Concepts and Algorithms

#### by Mohammed J. Zaki & Wagner Meira Jr., 2014

# Theory and Applications for Advanced Text Mining

# Theory and Applications for Advanced Text Mining

#### by Shigeaki Sakurai, 2012

# Understanding Machine Learning: From Theory to Algorithms

# Real-World Active Learning

# Real-World Active Learning

#### by Ted Cuzzillo, 2015

# A Course in Machine Learning

# A Course in Machine Learning

#### by Hal Daumé III, 2014

# A First Encounter with Machine Learning

# A First Encounter with Machine Learning

#### by Max Welling, 2011

# Artificial Intelligence

# The LION Way: Machine Learning plus Intelligent Optimization

# The LION Way: Machine Learning plus Intelligent Optimization

#### by Roberto Battiti & Mauro Brunato, 2013

# Learning Deep Architectures for AI

# Artificial Intelligence A Modern Approach, 1st Edition

# Statistics and Statistical Learning

# Artificial Intelligence: Foundations of Computational Agents

# Artificial Intelligence: Foundations of Computational Agents

#### by David Poole & Alan Mackworth, 2010

# Think Stats: Exploratory Data Analysis in Python

# Think Bayes: Bayesian Statistics Made Simple

# The Elements of Statistical Learning: Data Mining, Inference, and Prediction

# The Elements of Statistical Learning: Data Mining, Inference, and Prediction

#### by Trevor Hastie, Robert Tibshirani, & Jerome Friedman, 2008

# An Introduction to Statistical Learning with Applications in R

# An Introduction to Statistical Learning with Applications in R

#### by Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani, 2013

# A First Course in Design and Analysis of Experiments

# Time Series Analysis and Its Applications: With R Examples

# Time Series Analysis and Its Applications: With R Examples

#### by Robert H. Shumway & David S. Stoffer, 2011

# An Introduction to Statistics with Python

# An Introduction to Statistics with Python

#### by Thomas Haslwanter, 2015

# OpenIntro Statistics

# Intro Stat with Randomization and Simulation

# Intro Stat with Randomization and Simulation

#### by David M Diez, Christopher D Barr, & Mine Çetinkaya-Rundel, 2015

# Data Visualization

# D3 Tips and Tricks

# Interactive Data Visualization for the Web

# Big Data

# Disruptive Possibilities: How Big Data Changes Everything

# Real-Time Big Data Analytics: Emerging Architecture

# Big Data Now: 2012 Edition

# Computer Science Topics

# Natural Language Processing with Python

# Computer Vision

# Programming Computer Vision with Python

# Math Topics

# A First Course in Linear Algebra

# Linear Algebra: An Introduction to Mathematical Discourse

# Linear Algebra: An Introduction to Mathematical Discourse

#### by Wikibooks

# Probability and Statistics Cookbook

# Probability and Statistics Cookbook

#### by Matthias Vallentin

# Linear Algebra, Theory And Applications

# Probabilistic Models in the Study of Language

# Probabilistic Models in the Study of Language

#### by R Levy, 2012

# Linear Algebra

# Linear Algebra

#### by David Cherney, Tom Denton & Andrew Waldron, 2013

# Introduction to Probability

# Elementary Applied Topology

# Ordinary Differential Equations

# Ordinary Differential Equations

#### by Wikibooks

# Elementary Differential Equations

Well, there you have it. Thousands of e-pages to read through. We hope there’s a data science book here for everyone, no matter what level you’re starting at. If you have any suggestions of free books to include or want to review a book mentioned, please comment below and let us know!

**We are against illegal distribution of materials, so if you find that one of these books is a pirated copy, please inform us so that we can remove it from the list immediately.**

“Elementary Applied Topology”, by Robert Ghrist.

This book goes into persistent homology, barcodes, etc.

https://www.math.upenn.edu/~ghrist/notes.html

Great find. This list could use some more in depth math texts.

You should add a few of these links to Hackr.io!

Great! Thank you so much.

You’re welcome! Thanks for reading 🙂

Thank you Brendan! I could improve my skills thanks to your post.

thanks for providing