100+ Free Data Science Books

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 through our comprehensive courses list.


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.

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Comma delimit (e.g.,Python,Clustering)
Data Science in General
An introduction to data science
4.5 (4 Ratings)

An Introduction to Data Science

Jeffrey Stanton, Syracuse University
Contributions by Robert W. De Graaf

This book was developed for the Certificate of Data Science pro- gram at Syracuse University’s School of Information Studies.

Data Science in General
School of Data Handbook

School of Data Handbook

School of Data, 2015

The School of Data Handbook is a companion text to the School of Data. Its function is something like a traditional textbook – it will provide the detail and background theory to support the School of Data courses and challenges.

Data Science in General
Data Jujitsu: The Art of Turning Data into Product
3.8 (142 Ratings)

Data Jujitsu: The Art of Turning Data into Product

DJ Patil, 2012
DJ is the "Data Scientist in Residence" at Greylock Partners

Learn how to use a problem's "weight" against itself. Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.

Data Science in General
Art of Data Science
4.4 (5 Ratings)

The Art of Data Science

Roger D. Peng & Elizabeth Matsui, 2015

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...

Interviews with Data Scientists
The Data Science Handbook

The Data Science Handbook

by Carl Shan (Author),‎ William Chen (Author),‎ Henry Wang (Author),‎ Max Song (Author)
25 Data Scientists contributed

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

Interviews with Data Scientists
The Data Analytics Handbook

The Data Analytics Handbook

Brian Liou, Tristan Tao, & Declan Shener 2015
N/A

A free handbook series released by Leada to help promote data analytics literacy.

Forming Data Science Teams
Data Driven: Creating a Data Culture
3.7 (226 Ratings)

Data Driven: Creating a Data Culture

DJ Patil,‎ Hilary Mason
Hilary Mason is the lead scientist at bit.ly, DJ is the "Data Scientist in Residence" at Greylock Partners

In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt.

Forming Data Science Teams
Building Data Science Teams
3.7 (255 Ratings)

Building Data Science Teams

DJ Patil
DJ is the "Data Scientist in Residence" at Greylock Partners

In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.

Forming Data Science Teams
Understanding the Chief Data Officer

Understanding the Chief Data Officer

Julie Steele
Director of Communications at Silicon Valley Data Science

To manage today's flood of available data, a number of high-profile corporations have adopted a new position in addition to existing CTOs and CIOs: the Chief Data Officer, or CDO.

Data Analysis
The Elements of Data Analytic Style
3.7 (139 Ratings)

The Elements of Data Analytic Style

Jeff Leek
Associate Professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health

Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks.

Distributed Computing Tools
Hadoop Tutorial as a PDF

Hadoop Tutorial as a PDF

Tutorials Point
Online Learning Resource

Intro to Hadoop - An open-source framework for storing and processing big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines.

Distributed Computing Tools
Cloudera Impala
Languages: SQL
3.3 (13 Ratings)

Cloudera Impala

John Russell, 2014

Learn about Cloudera Impala--an open source project that's opening up the Apache Hadoop software stack to a wide audience of database analysts, users, and developers.

Distributed Computing Tools
Data-Intensive Text Processing with MapReduce
4.1 (28 Ratings)

Data-Intensive Text Processing with MapReduce

Jimmy Lin & Chris Dyer, 2010

MapReduce [45] is a programming model for expressing distributed computations on massive amounts of data and an execution framework for large-scale data processing on clusters of commodity servers. It was originally developed by Google...

Distributed Computing Tools
Hadoop Illuminated

Hadoop Illuminated

Mark Kerzner & Sujee Maniyam, 2014

'Hadoop illuminated' is the open source book about Apache Hadoop™. It aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical.

Distributed Computing Tools
Programming Pig
3.7 (66 Ratings)

Programming Pig

Alan Gates, 2011
Alan is a member of the Apache Software Foundation and a co-founder of Hortonworks.

This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop.

Learning Languages
Think Python 2nd Edition
Languages: Python
4.2 (114 Ratings)

Think Python 2nd Edition

Allen Downey, 2015
Allen Downey is a Professor of Computer Science at Olin College

This hands-on guide takes you through Python a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. Updated to Python 3.

Learning Languages
Python Programming
Languages: Python

Python Programming

Wikibooks, 2015

This book describes Python, an open-source general-purpose interpreted programming language available for a broad range of operating systems. This book describes primarily version 2, but does at times reference changes in version 3.

Learning Languages
Automate the Boring Stuff with Python: Practical Programming for Total Beginners
Languages: Python
4.3 (807 Ratings)

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

Al Sweigart, 2015

Practical programming for total beginners. In Automate the Boring Stuff with Python, 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.

Learning Languages
Learn Python the Hard Way
Languages: Python
3.8 (254 Ratings)

Learn Python the Hard Way

Zed A. Shaw, 2013

This is a free sample of Learn Python 2 The Hard Way with 8 exercises and Appendix A available for you to review.

Learning Languages
Dive Into Python 3
Languages: Python
3.8 (253 Ratings)

Dive Into Python 3

Mark Pilgrim, 2009
Mark Pilgrim is a developer advocate for open source and open standards

This is a hands-on guide to Python 3 and its differences from Python 2. Each chapter starts with a real, complete code sample, picks it apart and explains the pieces, and then puts it all back together in a summary at the end.

Learning Languages
Test-Driven Development with Python
Languages: Python
4.3 (201 Ratings)

Test-Driven Development with Python

Harry J. W. Percival, 2015

By taking you through the development of a real web application from beginning to end, this hands-on guide demonstrates the practical advantages of test-driven development (TDD) with Python.

Learning Languages
A Byte of Python
Languages: Python
4.1 (15 Ratings)

A Byte of Python

Swaroop C H, 2003

‘A Byte of Python’ is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.

Learning Languages
Invent with Python
Languages: Python
4.4 (237 Ratings)

Invent with Python

Albert Sweigart
Albert Sweigart, is a software developer in San Francisco, California

"Invent Your Own Computer Games with Python" teaches you computer programming in the Python programming language. Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples.

Learning Languages
Python for Informatics: Exploring Information
Languages: Python
4.3 (467 Ratings)

Python for Informatics: Exploring Information

Dr. Charles R Severance, 2013

This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet.

Learning Languages
Python Practice Book
Languages: Python

Python Practice Book

Anand Chitipothu, 2014
Anand conducts Python training classes on a semi-regular basis in Bangalore, India.

This book is prepared from the training notes of Anand Chitipothu.

Learning Languages
Learn Python, Break Python: A Beginner's Guide to Programming
Languages: Python
4.0 (15 Ratings)

Learn Python, Break Python

Scott Grant, 2014

This is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever. After all, everybody has to start somewhere.

Learning Languages
Python Cookbook
Languages: Python
4.5 (340 Ratings)

Python Cookbook

David Beazley & Brian K. Jones, 2013

If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3. For experienced Python developers.

Learning Languages
Learning with Python 3
Languages: Python
4.1 (17 Ratings)

Learning with Python 3

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

Introduction to computer science using the Python programming language. It covers the basics of computer programming in the first part while later chapters cover basic algorithms and data structures.

Learning Languages
Python for You and Me
Languages: Python

Python for You and Me

Kushal Das, 2015

This is a simple book to learn the Python programming language, it is for the programmers who are new to Python.

Learning Languages
R Programming for Data Science
Languages: R

R Programming for Data Science

Roger D. Peng

This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code.

Learning Languages
R Programming
Languages: R

R Programming

Wikibooks, 2014

The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R. It is supposed to be organized by task but not by discipline. We try to make a cross-disciplinary book, i.e. a book that can be used by all.

Learning Languages
Advanced R
Languages: R
4.6 (173 Ratings)

Advanced R

Hadley Wickham, 2014

Useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With ten+ years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.

Learning Languages
A Little Book of R for Time Series
Languages: R

A Little Book of R for Time Series

Avril Coghlan, 2015

This is a simple introduction to time series analysis using the R statistics software.

Learning Languages
The R Manuals
Languages: R

The R Manuals

R Development Core Team

The R Manuals.

Learning Languages
Learning Statistics with R
Languages: R

Learning Statistics with R

Daniel Navarro, 2015

I (Dani) started teaching the introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. These are my own notes for the class which were trans-coded to book form.

Learning Languages
R by Example
Languages: R

R by Example

Ajay Shah, 2005
Learning Languages
Practical Regression and Anova using R
Languages: R

Practical Regression and Anova using R

Julian J. Faraway, 2002

This book is NOT introductory. The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.

Learning Languages
The R Inferno
Languages: R
4.3 (9 Ratings)

The R Inferno

Patrick Burns, 2011

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks.

Learning Languages
Ecological Models and Data in R
Languages: R
4.2 (36 Ratings)

Ecological Models and Data in R

Benjamin M. Bolker, 2008

The first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know to analyze their own data using the R language.

Learning Languages
Spatial Epidemiology Notes: Applications and Vignettes in R
Languages: R

Spatial Epidemiology Notes: Applications and Vignettes in R

Charles DiMaggio, 2014

My intent is to present a relatively brief, non-jargony overview of how practicing epidemiologists can apply some of the extremely powerful spatial analytic tools that are easily available to them.

SQL, NoSQL, and Databases
Learn SQL The Hard Way
Languages: SQL

Learn SQL The Hard Way

Zed. A. Shaw, 2010
SQL, NoSQL, and Databases
SQL Tutorial as a PDF
Languages: SQL

SQL Tutorial as a PDF

Tutorials Point

This tutorial will give you a quick start to SQL. It covers most of the topics required for a basic understanding of SQL and to get a feel of how it works.

SQL, NoSQL, and Databases
SQL for Web Nerds
Languages: SQL

SQL for Web Nerds

Philip Greenspun
SQL, NoSQL, and Databases
Cassandra Tutorial as a PDF
Languages: Cassandra

Cassandra Tutorial as a PDF

Tutorials Point, 2015
SQL, NoSQL, and Databases
CouchDB: The Definitive Guide
Languages: Cassandra
3.2 (66 Ratings)

CouchDB: The Definitive Guide

J. Chris Anderson, Jan Lehnardt, & Noah Slater

Three of CouchDB’s creators show you how to use this document-oriented database as a standalone application framework or with high-volume, distributed applications.

SQL, NoSQL, and Databases
The Little MongoDB Book
Languages: MongoDB

The Little MongoDB Book

Karl Seguin, 2011

MongoDB is an open source NoSQL database, easily scalable and high performance. It retains some similarities with relational databases which, in my opinion, makes it a great choice for anyone who is approaching the NoSQL world.

SQL, NoSQL, and Databases
MongoDB Succinctly
Languages: MongoDB

MongoDB Succinctly

Agus Kurniawan

Essentials of the MongoDB system. Starting with creating a MongoDB database, you'll learn how to make collections and interact with their data, how to build a console application to interact with binary and image collection data, and much more.

SQL, NoSQL, and Databases
Extracting Data from NoSQL Databases
Languages: NoSQL

Extracting Data from NoSQL Databases

Petter Näsholm, 2012
SQL, NoSQL, and Databases
NoSQL Databases
Languages: NoSQL

NoSQL Databases

Christof Strauch
SQL, NoSQL, and Databases
Graph Databases
Languages: Graph DB
3.5 (21 Ratings)

Graph Databases

Ian Robinson, Jim Webber, & Emil Eifrem, 2013

Get started with O'Reilly's Graph Databases and discover how graph databases can help you manage and query highly connected data.

Data Mining and Machine Learning
Introduction to Machine Learning

Introduction to Machine Learning

Amnon Shashua, 2008
Data Mining and Machine Learning
Introduction to Machine Learning

Introduction to Machine Learning

Alex Smola & S.V.N. Vishwanathan, 2008
Data Mining and Machine Learning
Machine Learning

Machine Learning

Abdelhamid Mellouk & Abdennacer Chebira
Data Mining and Machine Learning
Machine Learning – The Complete Guide

Machine Learning – The Complete Guide

Wikipedia
Data Mining and Machine Learning
Social Media Mining An Introduction
4.7 (2 Ratings)

Social Media Mining An Introduction

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

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 in social media mining

Data Mining and Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques
4.0 (219 Ratings)

Data Mining: Practical Machine Learning Tools and Techniques

Ian H. Witten & Eibe Frank, 2005

Offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.

Data Mining and Machine Learning
Mining of Massive Datasets
4.4 (30 Ratings)

Mining of Massive Datasets

Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014

Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond.

Data Mining and Machine Learning
A Programmer's Guide to Data Mining

A Programmer's Guide to Data Mining

Ron Zacharski, 2015

A guide to practical data mining, collective intelligence, and building recommendation systems by Ron Zacharski. This work is licensed under a Creative Commons license.

Data Mining and Machine Learning
Data Mining with Rattle and R
Languages: R
4.2 (59 Ratings)

Data Mining with Rattle and R

Graham Williams, 2011

This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built.

Data Mining and Machine Learning
Data Mining and Analysis: Fundamental Concepts and Algorithms
4.3 (19 Ratings)

Data Mining and Analysis: Fundamental Concepts and Algorithms

Mohammed J. Zaki & Wagner Meria Jr., 2014

The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers many more cutting-edge data mining topics.

Data Mining and Machine Learning
Bayesian Methods for Hackers
Languages: Python
4.1 (65 Ratings)

Probabilistic Programming & Bayesian Methods for Hackers

Cam Davidson-Pilon, 2015

illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments.

Data Mining and Machine Learning
Machine Learning, Neural and Statistical Classification
2.8 (1 Ratings)

Machine Learning, Neural and Statistical Classification

D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999
Data Mining and Machine Learning
Information Theory, Inference, and Learning Algorithms
4.5 (310 Ratings)

Information Theory, Inference, and Learning Algorithms

David J.C. MacKay, 2005

"Essential reading for students of electrical engineering and computer science; also a great heads-up for mathematics students concerning the subtlety of many commonsense questions." Choice

Data Mining and Machine Learning
Bayesian Reasoning and Machine Learning
3.9 (139 Ratings)

Bayesian Reasoning and Machine Learning

David Barber, 2014

For final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.

Data Mining and Machine Learning
Gaussian Processes for Machine Learning
4.3 (57 Ratings)

Gaussian Processes for Machine Learning

C. E. Rasmussen & C. K. I. Williams, 2006

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Data Mining and Machine Learning
Reinforcement Learning: An Introduction
4.4 (232 Ratings)

Reinforcement Learning: An Introduction

Richard S. Sutton & Andrew G. Barto, 2012

A clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.

Data Mining and Machine Learning
Algorithms for Reinforcement Learning
3.5 (5 Ratings)

Algorithms for Reinforcement Learning

Csaba Szepesvari , 2009

This book gives a very quick but still thorough introduction to reinforcement learning, and includes algorithms for quite a few methods. This is everything a graduate student could ask for in a text.

Data Mining and Machine Learning
Modeling With Data

Modeling With Data

Ben Klemens, 2008

Modeling with Data offers a useful blend of data-driven statistical methods and nuts-and-bolts guidance on implementing those methods. --Pat Hall, founder of Translation Creation

Data Mining and Machine Learning
KB – Neural Data Mining with Python Sources

KB – Neural Data Mining with Python Sources

Roberto Bello, 2013
Data Mining and Machine Learning
Deep Learning

Deep Learning

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

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.

Data Mining and Machine Learning
Neural Networks and Deep Learning

Neural Networks and Deep Learning

Michael Nielsen, 2015

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you concepts behind neural networks and deep learning.

Data Mining and Machine Learning
Data Mining Algorithms In R
Languages: R

Data Mining Algorithms In R

Wikibooks, 2014
Data Mining and Machine Learning
Theory and Applications for Advanced Text Mining

Theory and Applications for Advanced Text Mining

Shigeaki Sakurai, 2012

This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language.

Data Mining and Machine Learning
Understanding Machine Learning: From Theory to Algorithms
4.3 (43 Ratings)

Understanding Machine Learning: From Theory to Algorithms

Shai Shalev-Shwartz, 2014

The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.

Data Mining and Machine Learning
Real-World Active Learning

Real-World Active Learning

Ted Cuzzillo, 2015

Applications and Strategies for Human-in-the-loop Machine Learning.

Data Mining and Machine Learning
A Course in Machine Learning

A Course in Machine Learning

Hal Daumé III, 2014
Data Mining and Machine Learning
A First Encounter with Machine Learning

A First Encounter with Machine Learning

Max Welling, 2011
Artificial Intelligence
The LION Way: Machine Learning plus Intelligent Optimization
4.2 (9 Ratings)

The LION Way: Machine Learning plus Intelligent Optimization

Roberto Battiti & Mauro Brunato, 2013

Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex and dynamic problems. Learn about increasing the automation level and connecting data directly to decisions and actions.

Artificial Intelligence
Learning Deep Architectures for AI
3.7 (16 Ratings)

Learning Deep Architectures for AI

Yoshua Bengio, 2009

Foundations and Trends(r) in Machine Learning.

Artificial Intelligence
Artificial Intelligence A Modern Approach, 1st Edition
4.1 (464 Ratings)

Artificial Intelligence A Modern Approach, 1st Edition

Stuart Russell, 1995

Comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

Statistics and Statistical Learning
Artificial Intelligence: Foundations of Computational Agents
3.8 (18 Ratings)

Artificial Intelligence: Foundations of Computational Agents

David Poole & Alan Mackworth, 2010

This is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents.

Statistics and Statistical Learning
Think Stats: Exploratory Data Analysis in Python
Languages: Python
3.6 (214 Ratings)

Think Stats: Exploratory Data Analysis in Python

Allen B. Downey, 2014

This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

Statistics and Statistical Learning
Think Bayes: Bayesian Statistics Made Simple
3.9 (59 Ratings)

Think Bayes: Bayesian Statistics Made Simple

Allen B. Downey, 2012

Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.

Statistics and Statistical Learning
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
4.2 (290 Ratings)

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

Trevor Hastie, Robert Tibshirani, & Jerome Friedman, 2008

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

Statistics and Statistical Learning
An Introduction to Statistical Learning with Applications in R
4.6 (881 Ratings)

An Introduction to Statistical Learning with Applications in R

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

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, and much more.

Statistics and Statistical Learning
A First Course in Design and Analysis of Experiments
2.9 (21 Ratings)

A First Course in Design and Analysis of Experiments

Gary W. Oehlert, 2010

Suitable for either a service course for non-statistics graduate students or for statistics majors. Unlike most texts for the one-term grad/upper level course on experimental design, this book offers a superb balance of both analysis and design.

Statistics and Statistical Learning
OpenIntro Statistics
4.5 (93 Ratings)

OpenIntro Statistics

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

Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org.

Statistics and Statistical Learning
Intro Stat with Randomization and Simulation
4.2 (10 Ratings)

Intro Stat with Randomization and Simulation

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

The foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches, where the normal and t distributions are used for hypothesis testing and...

Data Visualization
D3 Tips and Tricks
Languages: JavaScript
4.2 (11 Ratings)

D3 Tips and Tricks

Malcolm Maclean, 2015

D3 Tips and Tricks is a book written to help those who may be unfamiliar with JavaScript or web page creation get started turning information into visualization.

Data Visualization
Interactive Data Visualization for the Web
4.1 (410 Ratings)

Interactive Data Visualization for the Web

Scott Murray, 2013

Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development. It’s easy and fun with this practical, hands-on introduction.

Big Data
Disruptive Possibilities: How Big Data Changes Everything
3.6 (136 Ratings)

Disruptive Possibilities: How Big Data Changes Everything

Jeffrey Needham, 2013

This book provides an historically-informed overview through a wide range of topics, from the evolution of commodity supercomputing and the simplicity of big data technology, to the ways conventional clouds differ from Hadoop analytics clouds.

Big Data
Real-Time Big Data Analytics: Emerging Architecture
3.5 (119 Ratings)

Real-Time Big Data Analytics: Emerging Architecture

Mike Barlow, 2013

Analysts now demand sub-second, near real-time query results. Fortunately, we have the tools to deliver them. This report examines tools and technologies that are driving real-time big data analytics.

Big Data
Big Data Now
3.3 (175 Ratings)

Big Data Now: 2012 Edition

O’Reilly Media, Inc., 2012

This is not just a technical book or just a business guide. Data is ubiquitous and it doesn't pay much attention to borders, so we've calibrated our coverage to follow it wherever it goes.

Computer Science Topics
Natural Language Processing with Python
Languages: Python
4.1 (372 Ratings)

Natural Language Processing with Python

Steven Bird, 2009

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.

Computer Science Topics
Computer Vision
4.1 (109 Ratings)

Computer Vision

Richard Szeliski, 2010

Challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which you can use on you own personal media

Computer Science Topics
Programming Computer Vision with Python
Languages: Python
4.1 (63 Ratings)

Programming Computer Vision with Python

Jan Erik Solem, 2012

If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, etc

Math Topics
A First Course in Linear Algebra
4.4 (7 Ratings)

A First Course in Linear Algebra

Robert A Beezer, 2012

This is an introduction to the basic concepts of linear algebra, along with an introduction to the techniques of formal mathematics. It has numerous worked examples, exercises and complete proofs, ideal for independent study.

Math Topics
Linear Algebra: An Introduction to Mathematical Discourse

Linear Algebra: An Introduction to Mathematical Discourse

Wikibooks
Math Topics
Probability and Statistics Cookbook

Probability and Statistics Cookbook

Matthias Vallentin

The probability and statistics cookbook is a succinct representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations.

Math Topics
Linear Algebra, Theory And Applications
3.5 (1 Ratings)

Linear Algebra, Theory And Applications

Kenneth Kuttler, 2015

This book gives a self- contained treatment of linear algebra with many of its most important applications. It is very unusual if not unique in being an elementary book which does not neglect arbitrary fields of scalars and the proofs of the theorems

Math Topics
Probabilistic Models in the Study of Language

Probabilistic Models in the Study of Language

R Levy, 2012
Math Topics
Linear Algebra

Linear Algebra

David Cherney, Tom Denton & Andrew Waldron, 2013
Math Topics
Introduction to Probability
4.3 (20 Ratings)

Introduction to Probability

Charles M. Grinstead & J. Laurie Snell, 1997

This book is designed for an introductory probability course at the university level for sophomores, juniors, and seniors in mathematics, physical and social sciences, engineering, and computer science.

Math Topics
Elementary Applied Topology
4.4 (25 Ratings)

Elementary Applied Topology

Robert Ghrist, 2014

This text gives a brisk and engaging introduction to the mathematics behind the recently established field of Applied Topology.

Math Topics
Ordinary Differential Equations

Ordinary Differential Equations

Wikibooks
Math Topics
Elementary Differential Equations
4.4 (6 Ratings)

Elementary Differential Equations

William F. Trench, 2013

This text has been written in clear and accurate language that students can read and comprehend. The author has minimized the number of explicitly state theorems and definitions, in favor of dealing with concepts in a more conversational manner.

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