FrontPage DataScienceBibliography


Math
  • 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

Coding
  • 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)

Data Analysis and Statistical Inference
  • 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)

Artificial Intelligence and Machine Learning
  • 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)

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