51 Free Books on Data Science

Data Science is one of the hottest new professions and academic disciplines in the beginning of the 21st century. People with the necessary skills are in high demand; primarily the discipline is quite new. But the situation is changing, as universities and educational platforms have started to offer different kinds of programs and courses in data science. And a good book could be an excellent addition to such a program or online course. So checkout the blog post below with a list of 51 free books that will help you to get acquainted with data science world.


General Books on Data Science

An Introduction to Data Science (Jeffrey Stanton, 2013)
School of Data Handbook (2015)
Data Jujitsu: The Art of Turning Data into Product (DJ Patil, 2012)
Art of Data Science (Roger D. Peng & Elizabeth Matsui, 2015)

Interviews with Data Scientists

The Data Science Handbook (Carl Shan, Henry Wang, William Chen, & Max Song, 2015)
The Data Analytics Handbook (Brian Liou, Tristan Tao, & Declan Shener, 2015)

How to Build Data Science Teams

Data Driven: Creating a Data Culture (Hilary Mason & DJ Patil, 2015)
Building Data Science Teams (DJ Patil, 2011)
Understanding the Chief Data Officer (Julie Steele, 2015)

Data Analysis

The Elements of Data Analytic Style (Jeff Leek, 2015)

Tools

Hadoop: The Definitive Guide (Tom White, 2011)
Data-Intensive Text Processing with MapReduce (Jimmy Lin & Chris Dyer, 2010)

Development and Machine Learning

Introduction to Machine Learning (Amnon Shashua, 2008)
Machine Learning (Abdelhamid Mellouk & Abdennacer Chebira)
Machine Learning – The Complete Guide (Wikipedia)
Social Media Mining An Introduction (Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014)
Data Mining: Practical Machine Learning Tools and Techniques (Ian H. Witten & Eibe Frank, 2005)
Mining of Massive Datasets (Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014)
A Programmer’s Guide to Data Mining (Ron Zacharski, 2015)
Data Mining with Rattle and R (Graham Williams, 2011)
Data Mining and Analysis: Fundamental Concepts and Algorithms (Mohammed J. Zaki & Wagner Meria Jr., 2014)
Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (Matthew A. Russell, 2014)
Probabilistic Programming & Bayesian Methods for Hackers (Cam Davidson-Pilon, 2015)
Data Mining Techniques For Marketing, Sales, and Customer Relationship Management (Michael J.A. Berry & Gordon S. Linoff, 2004)
Inductive Logic Programming: Techniques and Applications (Nada Lavrac & Saso Dzeroski, 1994)
Pattern Recognition and Machine Learning (Christopher M. Bishop, 2006)
Machine Learning, Neural and Statistical Classification (D. Michie, D.J. Spiegelhalter, & C.C. Taylor, 1999)
Information Theory, Inference, and Learning Algorithms (David J.C. MacKay, 2005)
Data Mining and Business Analytics with R (Johannes Ledolter, 2013)
Bayesian Reasoning and Machine Learning (David Barber, 2014)
Gaussian Processes for Machine Learning (C. E. Rasmussen & C. K. I. Williams, 2006)
Reinforcement Learning: An Introduction (Richard S. Sutton & Andrew G. Barto, 2012)
Algorithms for Reinforcement Learning (Csaba Szepesvari, 2009)
Big Data, Data Mining, and Machine Learning (Jared Dean, 2014)
Modeling With Data (Ben Klemens, 2008)
KB – Neural Data Mining with Python Sources (Roberto Bello, 2013)
Deep Learning (Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015)
Neural Networks and Deep Learning (Michael Nielsen, 2015)
Data Mining Algorithms In R (Wikibooks, 2014)
Data Mining and Analysis: Fundamental Concepts and Algorithms (Mohammed J. Zaki & Wagner Meira Jr., 2014)
Theory and Applications for Advanced Text Mining (Shigeaki Sakurai, 2012)

About Statistics

Think Stats: Exploratory Data Analysis in Python (Allen B. Downey, 2014)
Think Bayes: Bayesian Statistics Made Simple (Allen B. Downey, 2012)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Trevor Hastie, Robert Tibshirani, & Jerome Friedman, 2008)
An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani, 2013)
A First Course in Design and Analysis of Experiments (Gary W. Oehlert, 2010)

Data Visualization

D3 Tips and Tricks (Malcolm Maclean, 2015)
Interactive Data Visualization for the Web (Scott Murray, 2013)

Just about Big Data

Disruptive Possibilities: How Big Data Changes Everything (Jeffrey Needham, 2013)
Real-Time Big Data Analytics: Emerging Architecture (Mike Barlow, 2013)
Big Data Now: 2012 Edition (O’Reilly Media, Inc., 2012)

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