60本免費的資料科學書籍

資料科學(Data Science)這個名詞是近幾年非常流行的概念，它其實講的是跨領域(Interdisciplinary)的組成，這些學科包含如下：

1. 數學 (Mathematics)
2. 統計 (Statistics)
3. 化學計量學 (Chemometrics)
4. 資訊科學 (Information Science)
5. 電腦科學 (Computer Science)
6. 機率學 (Probability Theory)
7. 機器學習 (Machine Learning)
8. 統計學習 (Statistical Learning)
9. 資料探勘 (Data Mining)
10. 資料庫 (Database)
11. 資料工程 (Data Engineering)
12. 模式識別 (Pattern Recognition)
13. 視覺化 (Visualization)
14. 預測分析 (Predictive Analytics)
15. 不確定性建模 (Uncertainty Modeling)
16. 資料倉儲 (Data Warehousing)
17. 資料壓縮 (Data Compression)、
18. 電腦程式 (Computer Programming)
19. 人工智慧 (Artificial Intelligence)、
20. 高效能運算 (High Performance Computing)

資料科學領域知識包羅萬象，其中更包含特定領域知識的資料挖掘手法，因此資料科學在多個複雜學科中應運而生，既然是資料科學實驗室，就不能不推薦國外的資料科學相關書籍，以下為國外收集的免費資料科學相關書籍，不完全是新書，但希望可以協助讀者能更深入的了解這塊領域，其中幾個學科是必須知道的，像是R、Python這兩個資料科學主流語言，以及人工智慧、機器學習、資料探勘、資料庫等理論與工具書，都是進入資料科學領域的必學知識。

關於資料科學

• An Introduction to Data Science (2013)
• School of Data Handbook (2015)
• Data Jujitsu: The Art of Turning Data into Product (2012)
• Art of Data Science (2015)

關於大數據

• Disruptive Possibilities: How Big Data Changes Everything (2013)
• Real-Time Big Data Analytics: Emerging Architecture (2013)
• Big Data Now: 2012 Edition (2012)

關於資料分析

• The Elements of Data Analytic Style (2015)
• The Data Science Handbook (2015)
• The Data Analytics Handbook (2015)

關於資料科學團隊

• Data Driven: Creating a Data Culture (2015)
• Building Data Science Teams (2011)
• Understanding the Chief Data O€fficer (2015)

關於分散式分析

• Hadoop: The Definitive Guide (2011)
• Data-Intensive Text Processing with MapReduce (2010)

關於Python

• Think Python: How to Think Like a Computer Scientist (2012)
• Python Programming (2015)
• Automate the Boring Stuff with Python: Practical Programming for Total Beginners (2015)
• Learn Python the Hard Way (2013)
• Dive Into Python 3 (2009)

關於R

• R Programming for Data Science
• R Programming (2014)
• Advanced R (2014)

關於SQL

• Learn SQL The Hard Way (2010)
• SQL Tutorial 

關於資料探勘與機器學習

• Introduction to Machine Learning (2008)
• Machine Learning (2009)
• Machine Learning – The Complete Guide
• Social Media Mining An Introduction (2014)
• Data Mining: Practical Machine Learning Tools and Techniques (2005)
• Mining of Massive Datasets (2014)
• A Programmer’s Guide to Data Mining (2015)
• Data Mining with Rattle and R (2011)
• Data Mining and Analysis: Fundamental Concepts and Algorithms (2014)
• Probabilistic Programming & Bayesian Methods for Hackers (2015) 
• Data Mining Techniques For Marketing, Sales, and Customer Relationship Management (2004)
• Inductive Logic Programming: Techniques and Applications (1994)
• Pattern Recognition and Machine Learning (2006)
• Machine Learning, Neural and Statistical Classification (1999) 
• Information Theory, Inference, and Learning Algorithms (2005)
• Data Mining and Business Analytics with R (2013)
• Bayesian Reasoning and Machine Learning (2014)
• Gaussian Processes for Machine Learning (2006)
• Reinforcement Learning: An Introduction (2012)
• Algorithms for Reinforcement Learning (2009)
• Big Data, Data Mining, and Machine Learning (2014)
• Modeling With Data (2008)
• KB – Neural Data Mining with Python Sources (2013)
• Deep Learning (2015)
• Neural Networks and Deep Learning (2015)
• Data Mining Algorithms In R (2014)
• Data Mining and Analysis: Fundamental Concepts and Algorithms (2014)
• Theory and Applications for Advanced Text Mining (2012)

關於統計與統計學習

• Think Stats: Exploratory Data Analysis in Python (2014)
• Think Bayes: Bayesian Statistics Made Simple (2012)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2008)
• An Introduction to Statistical Learning with Applications in R (2013)
• A First Course in Design and Analysis of Experiments (2010)

關於資料視覺化

• D3 Tips and Tricks (2015)
• Interactive Data Visualization for the Web (2013)

關於資料科學工具

• Natural Language Processing with Python (2009)
• Computer Vision (2010)
• Concise Computer Vision (2010) 
• Artificial Intelligence A Modern Approach, 1st Edition (1995)

參考資料：

http://www.learndatasci.com/free-books/