| STATISTICAL LEARNING |
|
|
|
 |
An Introduction to Statistical Learning with Applications in R. Free book, nice introduction to the topic. |
 |
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Hastie, Tibshirani and Friedman. Free book, advanced. |
 |
Statistical Models, by A. C. Davison. Recommended by Joe Blitzstein in CS109. |
 |
Statistical Models: Theory and Practice, by David A. Freedman. Recommended by Joe Blitzstein in CS109. |
| MACHINE LEARNING |
|
|
|
 |
Machine Learning. A Probabilistic Perspective, By Kevin P. Murphy. Like a bible |
 |
Pattern Recognition and Machine Learning, by Bishop. Enohasis on bayesian approach. |
| CONCEPTUAL |
|
|
|
 |
The Art of Data Science. A Guide for Anyone Who Works with Data, by Roger D. Peng and Elizabeth Matsui. This is a conceptual review of the data analysis process, but practical. |
 |
Data Science for Business, by Provost and Fawcett |
| PROBABILITY |
|
|
|
| |
|
 |
Introduction to Probability, by Blitzstein and Hwang |
| BAYESIAN |
|
|
|
 |
Probabilistic Graphical Models, by Daphne Koller and Nir Friedman. A good companion to Coursera course on Probabilistic graphical models. |
 |
Mastering Probabilistic Graphical Models Using Python, Ankan and Panda |
| PYTHON |
|
|
|
 |
Python for Data Analysis, by Wes McKinney. A classic for data analysis with Pandas. He announced on twitter that he began writing the second edition to the book! |
|
|
| R language |
|
|
|
 |
R Programming for Data Science, by Roger Peng. Very basic introduction, the companion to their coursera Data Science course |
|
|
| FREAK |
|
|
|
 |
Data Science at the Command Line, by Janssens |
 |
Think Complexity, by Downey. Free and interesting book. |
| |
|
 |
Data Analysis with Open Source Tools, by Janert. I really liked the topics the author touches and how, maybe because we are both physicists! ;) |
| SCIENTIFIC |
|
|
|
 |
Effective Computation in Physics. The book and course I wish I had back at grad school. |
 |
Learning IPython for Interactive Computing and Data Visualization, by Rossant. Necessary introduction to the ipython jupyter notebook |
| PENDING |
|
|
|
 |
Python Machine Learning, by Sebastian Raschka. Very promising! |
 |
Machine Learning for Hackers, by Drew Conway and John Myles White. Based on R. |
 |
Probabilistic Programming & Bayesian Methods for Hackers, by Davidson-Pilon. Distributed as ipython notebooks, and based on python and PyMC |
|
|
