Recommended literature for data science students (undergraduate)¶

This list is a work in progress.

Last update: 2024-10-21.

Freely available¶

Blum, A., Hopcroft, J., Kannan, R. (2020). Foundations of Data Science. Cambridge University Press.

Deisenroth, M.P., Faisal, A.A., Ong, C.S. (2020). Mathematics for Machine Learning. Cambridge University Press.

Gagolewski, M. (2024). Deep R Programming.

Gentle, J.E. (2020). Theory of Statistics (draft).

Hastie, T., Tibshirani, R., Friedman, J. (2017). The Elements of Statistical Learning. Springer-Verlag.

Bartoszyński, R., Niewiadomska-Bugaj, M. (2007). Probability and Statistical Inference. Wiley.

Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C. (2009). Introduction to Algorithms. MIT Press and McGraw-Hill.

Devroye, L., Györfi, L., Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition. Springer.

Efron, B., 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. (2024). Matrix Algebra: Theory, Computations and Applications in Statistics. Springer.

Koronacki, J., 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.

Ginsberg, B. (2011). The Fall of the Faculty: The Rise of the All-Administrative University and Why It Matters. OUP.

Goldacre, B. (2008). Bad Science. Fourth Estate.

Goldacre, B. (2012). Bad Pharma. Fourth Estate.

Spicer, A. (2018). Business Bullshit. Routledge.