摘要：这是一份关于数据科学、商业分析、大数据、机器学习、算法、数据科学工具和相关程序语言的福利书单。又骗你买书？不，我们还有电子书！心动不如行动，赶快进来看看吧！
这份书单源自网络。虽然所列图书都是免费提供的，但如果您有深入学习的打算，我还是推荐您购买纸质版书籍。作者花费大量时间整合这些资源，希望得到您的支持与喜爱！
数据科学概论
An Introduction to Data Science
Jeffrey Stanton, 2013
School of Data Handbook
School of Data, 2015
Data Jujitsu: The Art of Turning Data into Product
DJ Patil, 2012
数据科学家访谈
The Data Science Handbook
Carl Shan, Henry Wang, William Chen, & Max Song, 2015
The Data Analytics Handbook
Brian Liou, Tristan Tao, & Declan Shener, 2015
创建数据科学团队
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
数据分析
The Elements of Data Analytic Style
Jeff Leek, 2015
分布式计算工具
Hadoop：权威指南
Tom White, 2011
Data-Intensive Text Processing with MapReduce
Jimmy Lin & Chris Dyer, 2010
程序语言学习
Python
像计算机科学家一样思考Python
Allen Downey, 2012
Python Programming
Wikibooks, 2015
Python编程快速上手 ——让繁琐工作自动化
Al Sweigart, 2015
“笨办法”学Python
Zed A. Shaw, 2013
R语言
R Programming for Data Science
Roger D. Peng
R Programming
Wikibooks, 2014
高级R语言编程指南
Hadley Wickham, 2014
SQL
Learn SQL The Hard Way
Zed. A. Shaw, 2010
SQL Tutorial
Tutorials Point
数据挖掘和机器学习
Introduction to Machine Learning
Amnon Shashua, 2008
Machine Learning
Abdelhamid Mellouk & Abdennacer Chebira, 450
Machine Learning – The Complete Guide
Wikipedia
社会媒体挖掘
Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu, 2014
数据挖掘：实用机器学习工具与技术
Ian H. Witten & Eibe Frank, 2005
大数据：互联网大规模数据挖掘与分布式处理
Jure Leskovec, Anand Rajaraman, & Jeff Ullman, 2014
写给程序员的数据挖掘实践指南
Ron Zacharski, 2015
Data Mining with Rattle and R
Graham Williams, 2011
数据挖掘与分析：概念与算法
Mohammed J. Zaki & Wagner Meria Jr., 2014
贝叶斯方法：概率编程与贝叶斯推断
Cam Davidson-Pilon, 2015
数据挖掘技术 ——应用于市场营销、销售与客户关系管理
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
信息论、推理与学习算法
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
深度学习
Yoshua Bengio, Ian J. Goodfellow, & Aaron Courville, 2015
Neural Networks and Deep Learning
Michael Nielsen, 2015
Data Mining Algorithms In R
Wikibooks, 2014
Theory and Applications for Advanced Text Mining
Shigeaki Sakurai, 2012
统计和统计学习
统计思维：程序员数学之概率统计
Allen B. Downey, 2014
贝叶斯思维：统计建模的Python学习法
Allen B. Downey, 2012
统计学习导论：基于R应用
Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani, 2013
A First Course in Design and Analysis of Experiments
Gary W. Oehlert, 2010
数据可视化
D3 Tips and Tricks
Malcolm Maclean, 2015
数据可视化实战：使用D3设计交互式图表
Scott Murray, 2013
大数据
Disruptive Possibilities: How Big Data Changes Everything
Jeffrey Needham, 2013
Real-Time Big Data Analytics: Emerging Architecture
Mike Barlow, 2013
Big Data Now
O’Reilly Media, Inc., 2012
计算机科学
Python自然语言处理
Steven Bird, 2009
计算机视觉：算法与应用
Richard Szeliski, 2010
Concise Computer Vision
Reinhard Klette, 2010
人工智能：一种现代的方法
Stuart Russell, 1995
当看到这里的时候，您即将阅读这些经典的书籍。无论现在处于什么水平，我都希望您有自己的收获！
文章原标题60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more ,译者：Anchor C.,审阅：虎说八道