My Statistical Bookshelf

Keep up with my reading habits! Feel free to email me recommendations! (Last updated: September 4th, 2025)

Currently Reading:

  • ⭐ An Introduction to Statistical Learning in R (ISLR) by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

    • Accompanied by: ISLR tidymodels labs by Emil Hvitfeldt

In Progress:

  • Python Polars: The Definitive Guide by Jeroen Janssens and Thijs Nieuwdorp

  • Data Science for Business by Foster Provost and Tom Fawcett

  • Spatio-Temporal Statistics with R by Christopher Wikle, Andrew Zammit-Mangion, and Noel Cressie

  • The Visual Display of Quantitative Information by Edward Tufte

Past Favorites (in rough order by frequence of reference):

  • Tidy Modeling with R by Max Kuhn and Julia Silge

  • Mastering Shiny by Hadley Wickham

  • ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham, Danielle Navarro

  • Class Notes - Intro To Database Systems (CMU 15-445/645 - Fall 2024) by Andy Pavlo

  • Advanced R by Hadley Wickham

  • R for Data Science by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund

  • Class Notes - Introduction to Data Analysis and Regression (UCLA Stats 101a - Spring 2025) by Robert Gould

  • Class Notes - Python and Other Technologies for Data Science (UCLA Stats 21 - Fall 2024) by Miles Chen

  • SQL for Data Scientists by Renée Teat

  • Class Notes - Introduction to Statistical Programming with R (UCLA Stats 20 - Spring 2024) by Mike Tsiang

  • Class Notes - Introduction to Probability (UCLA Stats 100a - Spring 2025) by Ying Nian Wu

Waiting For Their Moment:

  • Bayes Rules! by Alicia Johnson, Miles Ott, and Mine Dogucu

    • Accompanied by: Bayesf22 Notebook by Andrew Heiss

  • Beyond Multiple Linear Regression by Paul Roback and Julie Legler

  • Handbook of Spatial Statistics by Alan Gelfand, Peter Diggle, Peter Guttorp, and Montserrat Fuentes

  • Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic

  • Happy Git and GitHub for the useR by Jenny Bryan

  • Outstanding User Interfaces with Shiny by David Granjon

  • Fluent Python by Luciano Ramalho

  • Efficient Machine Learning with R by Simon Couch

  • Applied Machine Learning for Tabular Data by Max Kuhn and Kjell Johnson

  • The Programmer’s Brain by Felienne Hermans

  • Feature Engineering A-Z by Emil Hvitfeldt

  • Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson

  • Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath

  • Probabilistic Machine Learning: An Introduction by Kevin Murphy

  • The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

  • The StatQuest Illustrated Guide To Machine Learning by Josh Starmer

  • Class Notes - Foundations of Machine Learning (Bloomberg ML EDU) by David Rosenberg

  • Supervised Machine Learning for Science by Cristoph Molnar and Timo Freiesleben

  • Probability Theory: The Logic of Science by E.T. Jaynes

  • Bayesian Sports Models in R by Andrew Mack

  • Class Notes - Advanced Database Systems (CMU 15-721 Spring 2024) by Andy Pavlo

  • Linear Model and Extensions by Peng Ding