30 Must Read Books in Analytics / Data Science
So many pages have been dedicated to Data Science that it can be hard to pinpoint the best books among the sea of available content. However, we have compiled our own list and perhaps it would be a good source of reference for you too.
This is not a definitive list of all the books that you would probably have to read during your career as a Data Scientist, but it definitely includes classics, beginners books, specialist books (more related to the business of data science or team-building) and of course, some good ones that explain the complexities of certain programs, languages or processes.
So, bring it on! Find yourself a comfortable reclining chair or a desk, good reading glasses (if needed) and a peaceful mindset to cultivate your data-driven mind.
- The Signal and the Noise. Why So Many Predictions Fail- But Some Don’t, by Nate Silver Written by Nate Silver
- Big Data @ Work, by Thomas H. Davenport
- Predictive Analytics, by Eric Siegel
- Privacy in the Age of Big Data, by Theresa M. Payton and Ted Claypoole
- Doing Data Science, by Cathy O’Neil and Rachel Schutt
- Big Data A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier
- Data Science for Business, by Foster Provost and Tom Fawcett
- R Cookbook, by Paul Teetor
- Machine Learning for Hackers, Drew Conway and John Myles White
- R Graphics Cookbook, by Winston Chang
- Programming Collective Intelligence, by Toby Segaran
- The Human Face of Big Data, by Rick Smolan and Jennifer Erwitt
- Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World 1st Edition, by Bruce Schneier
- Smart Cities — Big Data, Civic Hackers, and the Quest for a New Utopia, by Anthony M. Townsend
- Pythin for Data Analysis, by Wes McKinney
- Agile Data Science: Building Data Analytics Applications with Hadoop, by Russell Jurney
- The Visual Display of Quantitative Information, by Edward R. Tufte
- Beautiful Data: The Stories Behind Elegant Data Solutions, by Toby Segaran, Robert Romano
- Hadoop, the Definitive Guide, by Tom White
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems, by Donald Miner
- Internet of Things — Home Projects for Raspberry Pi, Arduino and Beaglebones Black, by Donald Norris
- Building Data Science Teams, by DJ Patil
- The Analytics Revolution: How to Improve Your Business by Making Analytics Operational in the Big Data Era, by Bill Franks
- Data Mining For Dummies, by Meta S. Brown
- Data Science for Dummies, by Lillian Pierson
- When Genius Failed: The Rise and Fall of Long-Term Capital Management, by Roger Lowenstein
- Moneyball, by Michael Lewis
- Envisioning Information, by Edward Tufte
