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51 Free Data Science Books

A great collection of free data science books covering a wide range of topics from Data Science, Business Analytics, Data Mining and Big Data to Machine Learning, Algorithms and Data Science Tools.

Data Science Overviews

• 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)

Data Scientists Interviews

• 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 O€fficer (Julie Steele, 2015)

Data Analysis

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

Distributed Computing Tools

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

Data Mining 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)

Statistics and Statistical Learning

• 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)

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)

51 Free Data Science Books

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原文地址：http://www.cnblogs.com/yymn/p/4823869.html