Recommended Resources to Learn, Grow, and Work Smarter

Here are the resources for data analysts and programmers I personally recommend — including the best R programming books, top data science courses, and productivity tools for developers that I use to stay organized and learn faster.

Whether you’re starting out or growing your career, these tools and learning paths can help you learn R programming faster and build real-world skills.

Some links are affiliate links. They cost you nothing extra and help support my work. Thanks!

My favorites courses

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• 5 Course R-Track: Machine Learning, Web Apps, & Time Series
• Learning Labs Pro
• Data Science for Business Part 1
• Data Science for Business Part 2
• High Performance Time Series
• Shiny Web Applications Part 1
• Shiny Web Applications Part 2

Some of my favorite books

• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• Spatio-Temporal Statistics with R by Christopher K. Wikle, Jeffrey S. Cressie and Chunsheng Zhang
• Statistical Rethinking by Richard McElreath
• Tree-Based Methods for Stastistical Learning by Brandon M. Greenwell
• Explanatory Model Analysis by Przemyslaw Biecek and Tomasz Burzykowski
• Feature Engineering and Selection by Max Kuhn and Kjell Johnson
• Using R for Bayesian Spatial and Spatio-Temporal Modeling by Andrew Zammit Mangion
• Understanding Machine Learning by Shai Shalev-Shwartz and Shai Ben-David
• R for Data Science by Hadley Wickham and Garrett Grolemund
• Applied Spatial Data Analysis with R by Bivand, Pebesma and Gomez-Rubio
• Spatial Data Science with Application in R by Edzer Pebesma and Roger Bivand
• Spatial Analysis with R by Tonny J. Oyana
• Tidy Modelling with R by Max Kuhn and Julia Silge
• Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos
• Geocomputation with R by Robin Lovelace, Jakub Nowosad and Jannes Muenchow
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman
• Time Series Analysis and its Applications by Robert H. Shumway and David S. Stoffer
• Introduction to Statistics and Data Analysis by Christian Heumann and Michael Schomaker
• Applied Predictive Modeling by Max Kuhn and Kjell Johnson
• Practical Guide to Principal Component Methods in R by Alboukadel Kassambara
• Practical Guide to Cluster Analysis in R by Alboukadel Kassambara
• Statistics for Spatio-Temporal Data by Edzer Pebesma and Roger Bivand
• Interpretable Machine Learning by Christoph Molnar
• Practical Time Series Forecasting with R by Galit Shmueli and Kenneth C. Lichtendahl Jr.
• Build a Carreer in Data Science by Emily Robinson and Jacqueline Nolis
• R Packages by Hadley Wickham and Jenny Bryan
• Practical Time Series Analysis by Aileen Nielsen
• Football Analytics with R by Eric A. Eager and Richard A. Erickson

My favorite personal development books

• Atomic Habits by James Clear
• Deep Work by Cal Newport
• Grit by Angela Duckworth
• Mindset by Carol S. Dweck
• The Power of Now by Eckhart Tolle

Disclosure: Some links are affiliate links. It costs you nothing extra and helps support my work. Thanks for your support!

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