Recommended literature for data science students (undergraduate)¶
This list is a work in progress.
Last update: 2025-02-12.
See also: the postgraduate version.
Introductory Mathematics¶
Rasiowa, H., Introduction to Modern Mathematics, North Holland, 2014 (Polish 🇵🇱: Wstęp do matematyki współczesnej, PWN, 2013)
Deisenroth, M.P., Faisal, A.A., Ong, C.S., Mathematics for Machine Learning, Cambridge University Press, 2020 🔓
Bishop, C., Pattern Recognition and Machine Learning, Springer, 2006 🔓
Chapters 1–5 give a good overview of the kind of maths you are expected to master
Gentle, J.E., Matrix Algebra: Theory, Computations and Applications in Statistics, Springer, 2024
Probably not for beginners, but is a good source for the second exposure to the topic
Probability and Statistics¶
Bartoszyński, R., Niewiadomska-Bugaj, M. (2007). Probability and Statistical Inference, Wiley, 2007
Efron, B., Hastie, T.,. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science, Cambridge University Press, 2016
Gentle, J.E., Random Number Generation and Monte Carlo Methods, Springer, 2003
Gentle, J.E., Theory of Statistics (draft), 2020 🔓
Gentle, J.E., Computational Statistics, Springer, 2009
Tufte, E.R., The Visual Display of Quantitative Information, Graphics Press, 2001
Programming, Algorithms¶
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., Introduction to Algorithms, MIT Press and McGraw-Hill, 2009
Gagolewski, M., Deep R Programming, 2025 🔓
Gagolewski, M., Minimalist Data Wrangling with Python, 2025 🔓
Knuth, D.E., The Art of Computer Programming I: Fundamental Algorithms, Addison-Wesley, 1997
Knuth, D.E., The Art of Computer Programming II: Seminumerical Algorithms, Addison-Wesley, 1997
Knuth, D.E., The Art of Computer Programming III: Sorting and Searching, Addison-Wesley, 1997
Machine/Statistical Learning and Data Mining¶
Aggarwal, C.C., Data Mining. The Textbook, Springer, 2015
Blum, A., Hopcroft, J., Kannan, R., Foundations of Data Science, Cambridge University Press, 2020 🔓
beginner students should note an interesting chapter on the curse of dimensionality (Chap. 2)
applications of the SVD matrix factorisation in data science (Chap. 3)
Murphy, K.P., Probabilistic Machine Learning: An Introduction, MIT Press, 2022 🔓
Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning, Springer, 2017 🔓
Devroye, L., Györfi, L., Lugosi, G., A Probabilistic Theory of Pattern Recognition, Springer, 1996
Koronacki, J., Ćwik, J., Statystyczne systemy uczące się, EXIT, 2008 🇵🇱
Exploratory Data Analysis, Visualisation, Scientific Writing, etc.¶
Anna Kozak’s lecture notes – Exploratory Data Analysis, Data Visualisation Techniques
Oetiker, T. and others., The Not So Short Introduction to LaTeX 2ε, 2023 🔓
Trzeciak, J., [Writing Mathematical Papers in English: A Practical Guide], EMS Press, 2005 (See also his [https://emis.de/monographs/Trzeciak/](Mathematical English Usage – a Dictionary))
Tufte, E.R., The Visual Display of Quantitative Information, Graphics Press, 2001
Other¶
Ginsberg, B., The Fall of the Faculty: The Rise of the All-Administrative University and Why It Matters, OUP, 2011
Goldacre, B., Bad Science, Fourth Estate, 2008
Goldacre, B., Bad Pharma, Fourth Estate, 2012
Spicer, A., Business Bullshit, Routledge, 2018