Ressources

General

Wickham Hadley, Çetinkaya-Rundel Mine et Grolemund Garrett, R for data science: import, tidy, transform, visualize, and model data, 2e édition., O’Reilly, 2023, 548 p. https://r4ds.hadley.nz/

McKinney Wes, Python for data analysis: data wrangling with pandas, NumPy, and Jupyter, 3e édition., O’Reilly, 2022, 579 p. https://wesmckinney.com/book/

Storopoli, Huijzer and Alonso Julia Data Science, 2021, 260 p. https://juliadatascience.io

Kamiński Bogumił, Julia for data analysis, Manning, 2023, 443 p.

DataViz

Wilke Claus, Fundamentals of data visualization: a primer on making informative and compelling figures, O’Reilly, 2019, 387 p. https://clauswilke.com/dataviz/

Healy Kieran, Data visualization: a practical introduction, Princeton University Press, 2019, 272 p.

Geographic Data Science

Lovelace Robin, Nowosad Jakub et Muenchow Jannes, Geocomputation with R, 2e édition., Chapman & Hall / CRC, 2025, 424 p. https://r.geocompx.org/

Dorman Michael, Graser Anita, Nowosad Jakub et Lovelace Robin, Geocomputation with Python, Chapman & Hall / CRC, 2025, 344 p. https://py.geocompx.org/

Hoffimann Mendes Júlio, Geospatial Data Science with Julia, 2023. https://juliaearth.github.io/geospatial-data-science-with-julia/

Rey Sergio J., Arribas-Bel Daniel et Wolf Levi John, Geographic data science with Python, Chapman & Hall / CRC, 2023, 378 p. https://geographicdata.science/book/

Pebesma Edzer J. et Bivand Roger, Spatial data science: with applications in R, Chapman & Hall / CRC, 2023, 314 p. https://r-spatial.org/book/

Unsupervised Learning

Waggoner Philip D., Modern Dimension Reduction, Cambridge University Press, 2021, 98 p. https://arxiv.org/abs/2103.06885

Waggoner Philip D., Unsupervised Machine Learning for Clustering in Political and Social Research, Cambridge University Press, 2021, 70 p.

Giordani Paolo, Ferraro Maria Brigida et Martella Francesca, An Introduction to Clustering with R, Springer, 2020, 340 p.

Statistics

Johnson Alicia A., Ott Miles Q. et Dogucu Mine, Bayes rules! an introduction to Bayesian modeling with R, Chapman & Hall / CRC, 2022, 544 p. https://www.bayesrulesbook.com/

McElreath Richard, Statistical rethinking: a Bayesian course with examples in R and Stan, 2e édition, Chapman & Hall / CRC, 2020, 612 p.

À ma connaissance, il n’existe pas de version en libre accès de Statistical Rethinking. Le site suivant liste différentes “traductions” des exercices, à partir de différentes librairies et langages de programmation : https://xcelab.net/rm/ Voir notamment : https://github.com/StatisticalRethinkingJulia

Gelman Andrew, Hill Jennifer et Vehtari Aki, Regression and other stories, Cambridge University Press, 2020, 546 p. https://avehtari.github.io/ROS-Examples/

Comme pour Statistical Rethinking, il y a eu une traduction en Julia, par Rob J Goedman : https://github.com/RegressionAndOtherStoriesJulia

Davidson-Pilon Cameron, Bayesian methods for hackers: probabilistic programming and Bayesian inference, Addison-Wesley, 2016, 256 p. http://dataorigami.net/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

Kaplan Daniel T., Statistical modeling: a fresh approach, 2017, 388 p. https://dtkaplan.github.io/SM2-bookdown/

Cette dernière référence, contrairement aux précédentes, est fréquentiste.

Histoire / Philosophie

McGrayne Sharon Bertsch, The theory that would not die: how Bayes’ rule cracked the enigma code, hunted down Russian submarines, & emerged triumphant from two centuries of controversy, New Haven, Yale university press, 2011.

Kennedy-Shaffer Lee, “Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p-Values and Significance Testing”, The American Statistician, 2019, vol. 73, p. 82‑90.

Drouet Isabelle, Le bayésianisme aujourd’hui: fondements et pratiques, Paris, Éditions matériologiques, 2016.

Principales librairies

Réseaux bayésiens

Principales librairies

Maths

Nield Thomas, Essential Math for Data Science, O’Reilly, 2022, 349 p.

Savov Ivan, No Bullshit Guide to Math and Physics, Minireference Co, 2013, 288 p.