Additional Learning Resources

1 General

• Wilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K. Teal. 2017. “Good Enough Practices in Scientific Computing.” PLOS Computational Biology 13 (6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510
• Best Practices when Writing Code by Daniel M. Sullivan
• Data science for economists by Grant McDermott

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2 R

2.1 General

• R for Reproducible Scientific Analysis by Software Carpentry
• R for Data Science (2e) by Hadley Wickham et al.

2.2 Beginner

• Learn R by Codecademy
• Hands-On Programming with R by Garrett Grolemund

2.3 Intermediate/Advanced

• NCEAS Learning Hub’s coreR Course
• Advanced R by Hadley Wickham
• Efficient R programming by Colin Gillespie

2.4 Visualization

• Data Visualization: A Practical Introduction by Kieran Healy
• ggplot2: Elegant Graphics for Data Analysis (3e) by Hadley Wickham et al.

2.5 GIS

• Geocomputation with R by Robin Lovelace et al. 
• Geographic Data Science with R: Visualizing and Analyzing Environmental Change by Michael Wimberly
• Spatial Data Science by Edzer Pebesma and Roger Bivand
• RFF GIS Learning Lab

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3 Python

• The Python Tutorial
• Python Datascience Handbook by Jake VanderPlas
• Data Science from Scratch by Joel Grus
• Automate the Boring Stuff with Python by Al Sweigart
• Plotting and Programming in Python by Software Carpentry
• Python Web Scraping: Full Tutorial With Examples (2025) by Kevin Sahin
• Fluent Python by Luciano Ramalho

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4 Julia

• Julia Manual
• Julia Academy
• Julia Data Science by Jose Storopoli et al
• Getting Started · DataFrames.jl
• Tutorial · Plots
• Julia Performance Tips

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5 Stata

• Stata Users Guide
• UCLA Stata Learning Modules

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6 MATLAB

• MATLAB Onramp Course
• MATLAB: A Practical Approach by Stormy Attaway
• Numerical Methods using MATLAB by George Lindfield and John Penny