Recommended Literature for Data Science Students#
Last update: 2024-05-21. This list is a work in progress.
Freely Available#
Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer-Verlag.
Blum, A., Hopcroft, J., and Kannan, R. (2020). Foundations of Data Science. Cambridge University Press.
Deisenroth, M.P., Faisal, A.A., and Ong, C.S. (2020). Mathematics for Machine Learning. Cambridge University Press.
Gagolewski M. (2024). Deep R Programming.
Gagolewski M. (2024). Minimalist Data Wrangling with Python.
Gentle, J.E. (2020). Theory of Statistics (draft).
Hastie, T., Tibshirani, R., and Friedman, J. (2017). The Elements of Statistical Learning. Springer-Verlag.
Oetiker, T. and others. (2021). The Not So Short Introduction to LaTeX 2ε.
Less Freely Available#
Bartoszyński, R. and Niewiadomska-Bugaj, M. (2007). Probability and Statistical Inference. Wiley.
Cormen, T.H., Leiserson, C.E., Rivest, R.L., and Stein, C. (2009). Introduction to Algorithms. MIT Press and McGraw-Hill.
Devroye, L., Györfi, L., and Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition. Springer.
Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press.
Gentle, J.E. (2003). Random Number Generation and Monte Carlo Methods. Springer.
Gentle, J.E. (2009). Computational Statistics. Springer-Verlag.
Gentle, J.E. (2017). Matrix Algebra: Theory, Computations and Applications in Statistics. Springer.
Koronacki, J. and Ćwik, J. (2008). Statystyczne systemy uczące się. EXIT. 🇵🇱
Knuth, D.E. (1997). The Art of Computer Programming I: Fundamental Algorithms. Addison-Wesley.
Knuth, D.E. (1997). The Art of Computer Programming II: Seminumerical Algorithms. Addison-Wesley.
Knuth, D.E. (1997). The Art of Computer Programming III: Sorting and Searching. Addison-Wesley.
Tufte, E.R. (2001). The Visual Display of Quantitative Information. Graphics Press.
Other#
Goldacre, B. (2008). Bad Science. Fourth Estate.
Spicer, A. (2018). Business Bullshit. Routledge.