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！如果有更多好书推荐，欢迎您在下方留言，谢谢！

以上为全部译文

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。