Recommended literature for data science students (postgraduate)

This list is a work in progress.

Last update: 2024-11-12.

See also: the undergraduate version.

Statistics, probability, machine learning, and the like

Aggarwal, C.C., Data Mining. The Textbook, Springer, 2015

Arnold, B.C., Pareto Distributions, CRC Press, 2015

Bishop, C., Pattern Recognition and Machine Learning, Springer-Verlag, 2006

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

Murphy, K.P., Probabilistic Machine Learning: Advanced Topics, MIT Press, 2022

Leskovec, J., Rajaraman, A., Ullman, J., Mining of Massive Datasets

Random graphs / Complex networks

Easley, D., Kleinberg, J., Networks, Crowds, and Markets: Reasoning About a Highly Connected World

Montanari, A., Mézard, M., Information, Physics, and Computation

van der Hofstad, R., Random Graphs and Complex Networks

Durrett, R., Dynamics on Graphs

Newman, M.E.J., Networks, Oxford University Press, 2018

Natural language processing

Aggarwal, C.C., Machine Learning for Text, Springer, 2022

Jurafsky, D., Martin, J.H., Speech and Language Processing (3rd ed. draft)

Eisenstein, J., Natural Language Processing

Goldberg, Y., A Primer on Neural Network Models for Natural Language Processing