Data Science.md

title	category
Data Science	Books

Index

• Data Science Introduction
• Data Processing
• Data Analysis
  • Fundamentals
  • Network Analysis
  • Statistics
  • Data Mining
  • Machine Learning
• Data Science Application
  • Data Visualization
• Uncategorized
• MOOCs about Data Science

Data Science Introduction

• 📕 Big Data Now: 2012 Edition - O'Reilly Media Inc.
• 📕 Data Science: An Introduction - Wikibook
• 📕 Disruptive Possibilities: How Big Data Changes Everything - Jeffrey Needham
• 📕 Introduction to Data Science - Jeffery Stanton
• 📕 Real-Time Big Data Analytics: Emerging Architecture - Mike Barlow
• 📕 The Evolution of Data Products - Mike Loukides
• 📕 The Promise and Peril of Big Data - David Bollier

Data Processing

• 📚 Data-Intensive Text Processing with MapReduce - Jimmy Lin and Chris Dyer

Data Analysis

Fundamentals

• 📕 Fundamental Numerical Methods and Data Analysis - George W. Collins
• 📕 Introduction to Metadata - Murtha Baca
• 📕 Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics - W. N. Venables, D. M. Smith, and the R Core Team
• 📕 Modeling with Data: Tools and Techniques for Scientific Computing - Ben Klemens

Network Analysis

• 📕 Network Science - Sarah Morrison
• 📕 The Wealth of Networks - Yochai Benkler
• 📚 Introduction to Social Network Methods - Robert A. Hanneman and Mark Riddle
• 📚 Networks, Crowds, and Markets: Reasoning About a Highly Connected World - David Easley and Jon Kleinberg

Statistics

• 📕 An Introduction to R - W. N. Venables, D. M. Smith, and the R Core Team
• 📕 Analyzing Linguistic Data: a practical introduction to statistics - R. H. Baayan
• 📕 Concepts and Applications of Inferential Statistics - Richard Lowry
• 📕 Introduction to Probability - Charles M. Grinstead and J. Laurie Snell
• 📕 Introduction to Statistical Thought - Michael Lavine
• 📕 OpenIntro Statistics - Second Edition - David M. Diez, Christopher D. Barr, and Mine Cetinkaya-Rundel
• 📕 simpleR - Using R for Introductory Statistics - John Verzani
• 📕 Statistics
• 📕 Think Stats: Probability and Statistics for Programmers - Allen B. Downey
• 📚 Applied Data Science - Ian Langmore and Daniel Krasner
• 📚 Forecasting: Principles and Practice - Rob J. Hyndman and George Athanasopoulos
• 🎓 Advanced Data Analysis from an Elementary Point of View - Cosma Rohilla Shalizi

Data Mining

• 📕 Data Mining and Knowledge Discovery in Real Life Applications - Julio Ponce and Adem Karahoca
• 📕 R and Data Mining: Examples and Case Studies - Yanchang Zhao
• 📚 Knowledge-Oriented Applications in Data Mining - Kimito Funatsu
• 📚 Data Mining and Analysis: Fundamental Concepts and Algorithms - Mohammed J. Zaki and Wagner Meira Jr.
• 📚 The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
• 📚 Mining of Massive Datasets - Anand Rajaraman, Jure Leskovec, and Jeffrey D. Ullman
• 📚 New Fundamental Technologies in Data Mining - Kimito Funatsu
• 📚 Theory and Applications for Advanced Text Mining - Shigeaki Sakurai
• 🎓 Data Mining for Social Network Data - Springer

Machine Learning

• 📕 A Course in Machine Learning - Hal Daume
• 📕 A First Encounter with Machine Learning - Max Welling
• 📕 Thinking Bayes - Allen B. Downey
• 📕 Sklearn Basics
• 📚 Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan
• 📚 Probabilistic Programming & Bayesian Methods for Hackers - Cam Davidson-Pilon (main author)
• 📚 The LION Way: Machine Learning plus Intelligent Optimization - Robert Battiti and Mauro Brunato
• 🎓 Bayesian Reasoning and Machine Learning - David Barber
• 🎓 Gaussian Processes for Machine Learning - Carl Edward Rasmussen and Christopher K. I. Williams

Data Science Application

Information Retrieval

• 📚 Introduction to Information Retrieval - Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze

Data Visualization

• 📕 Interactive Data Visualization for the Web - Scott Murray
• 📕 Plotting and Visualization in Python

Uncategorized

• 📕 Data Journalism Handbook - Jonathan Gray, Liliana Bounegru, and Lucy Chambers
• 📕 Building Data Science Teams - DJ Patil
• 📕 Mathematics for Computer Science - Eric Lehman, Thomas Leighton, and Albert R. Meyer
• 📕 The Field Guide to Data Science
• 📚 Information Theory, Inference, and Learning Algorithms - David MacKay

MOOCs about Data Science

• 📕 Introduction to Data Science - Bill Howe (Coursera)
• 📕 Introduction to Hadoop and MapReduce - Udacity
• 📕 Machine Learning - Andrew Ng (Coursera)
• 📕 Machine Learning Foundatiaons (taught in Chinese) - Hsuan-Tien Lin
• 📚 Data Mining with Weka - Ian H. Witten
• 📚 Machine Learning Video Library - Yaser Abu-Mostafa
• 📚 Natural Language Processing - Dan Jurafsky and Christopher Manning (Coursera)
• 📚 Social and Economic Networks: Models and Analysis - Matthew O. Jackson (Coursera)
• 📚 Social Network Analysis - Lada Adamic (Coursera)