A Data Science Central Community

Here are 13 books on Machine Learning and Data Mining that are great resources, references, and refreshers for Data Scientists. (This is definitely a small selective subsample of the many excellent books available.)

- The Top Ten Algorithms in Data Mining, by Xindong Wu and Vipin Kumar (editors)
- Learning from Data, by Y.Abu-Mostafa, M.Magdon-Ismail, H-S.Lin
- Mining of Massive Datasets, by Jeffrey David Ullman and Anand Rajaraman
- Handbook of Statistical Analysis and Data Mining Applications, by G.Miner, J.Elder, R.Nisbet
- Machine Learning for Hackers, by Drew Conway and John Myles White
- Mahout in Action, by S.Owen, R.Anil, T.Dunning, E.Friedman
- Statistical and Machine-Learning Data Mining: Techniques for Better..., by Bruce Ratner
Networks, Crowds, and Markets: Reasoning About a Highly Connected W..., by David Easley and Jon Kleinberg

- Bayesian Reasoning and Machine Learning, by David Barber
Ensemble Methods in Data Mining: Improving Accuracy Through Combini..., by Giovanni Seni and John Elder (Older Edition is also available)

- Data Mining with R: Learning with Case Studies, by Luis Torgo
- Using R for Data Management, Statistical Analysis, and Graphics, by Nicholas Horton and Ken Kleinman
- Introduction to Data Mining, by P-N.Tan, M.Steinbach, V.Kumar

And for my astronomer friends, here are a couple of additional suggestions:

14. Statistics, Data Mining, and Machine Learning in Astronomy: A Pract..., by Z.Ivezic, A.Connolly, J.VanderPlas, A.Gray

15. Advances in Machine Learning and Data Mining for Astronomy, by M.Way, J.Scargle, K.Ali, A.Srivastava