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
Data Scientists Interviews
How To Build Data Science Teams
Data Analysis
Distributed Computing Tools
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
Big Data
51 Free Data Science Books
原文：http://www.cnblogs.com/yymn/p/4823869.html
