Probability & Statistics

A list of some available resources.

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• An Introduction to Statistical Learning. Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani.
• Bayesian Methods for Hackers. Cameron Davidson-Pilson.
• Bayesian Optimization. Roman Garnett.
• Bayesian Statistics using Julia and Turing. José Eduardo Storopoli.
• Beyond Multiple Linear Regression. Applied Generalized Linear Models and Multilevel Models in R. Paul Roback and Julie Legler.
• Biostatistics. University of Florida Open Learning Textbook.
• Forecasting in Economics, Business, Finance, and Beyond. Francis X. Diebold. The University of Pennsylvania.
• Handbook on Statistical Distributions for Experimentalists. Christian Walck, University of Stockholm.
• Introduction to Mathematical Statistics, 8 ed.. Rober V. Hogg, Joseph W. McKean, Allen T. Craig.
• Introduction to Modern Statistics. Mine Çetinkaya-Rundel and Johanna Hardin, OpenIntro.org
• Introduction to Probability for Data Science. Stanley H. Chan.
• Introduction to Probability, 2ed. Joseph K. Blitzstein, Jessica Hwang.
• Modern Statistics with R. Mans Thulin.
• Practical Statistics for Data Scientists. Peter Bruce, Andrew Bruce, and Peter Gedeck.
• Practical Statistics in Medicine with R. Konstantinos I. Bougioukas.
• Regression and Other Stories by Andrew Gelman, Jennifer Hill, and Aki Vehtari.
• Think Bayes 2nd. Ed. , Allen B. Downey.

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Other

• Probability Cheat Sheet
• Probability and Statistics Cheat Sheets
• Seeing Theory: A visual introduction to Probability and Statistics
• StatQuest. Bite-sized pieces of Machine Learning and Statistics concepts.
• Understanding Probability Distributions using Python | Jupyter Notebook. Reza Bagheri.

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Updated: 03/22/2023 (C. Lizarraga)