FrontPage › DataScienceBibliography

- Linear Algebra and Its Applications by Gilbert Strang (Cengage Learning)

- Convex Optimization by Stephen Boyd and Lieven Vendenberghe (Cambridge University Press)

- A First Course in Probability (Pearson) and Introduction to Probability Models (Academic Press) by Sheldon Ross

- R in a Nutshell by Joseph Adler (O’Reilly)

- Learning Python by Mark Lutz and David Ascher (O’Reilly)

- R for Everyone: Advanced Analytics and Graphics by Jared Lander (Addison-Wesley)

- The Art of R Programming: A Tour of Statistical Software Design by Norman Matloff (No Starch Press)

- Python for Data Analysis by Wes McKinney (O’Reilly)

- Statistical Inference by George Casella and Roger L. Berger (Cengage Learning)

- Bayesian Data Analysis by Andrew Gelman, et al. (Chapman & Hall)

- Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill (Cambridge University Press)

- Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi (under contract with Cambridge University Press)

- The Elements of Statistical Learning: Data Mining, Inference and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (Springer)

- Pattern Recognition and Machine Learning by Christopher Bishop (Springer)

- Bayesian Reasoning and Machine Learning by David Barber (Cambridge University Press)

- Programming Collective Intelligence by Toby Segaran (O’Reilly)

- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (Prentice Hall)