Here’s a compilation of resources that I have found useful for learning programming (largely related to data analysis, machine learning, and data visualization), and understanding policy.
Programming
R
- swirl: How I started learning R! An interactive tutorial in R itself.
- Modern R with the tidyverse: A great introduction to R with a focus on the tidyverse
- Efficient R Programming: Explains efficient programming workflows and setting up your R environment
- R for Data Science: Probably the most well known resource, I haven’t used it too much but it has certainly been a useful reference from time to time
- Advanced R: Helpful for understanding how R works which will help you improve your programming skills with it
- Mastering Shiny: Learn to develop web applications using R
- R Packages: Learn how to create R packages
- Geocomputation with R: My go-to reference for anything geospatial
Python
SQL
Statistics/Machine Learning
Statistics and Machine Learning could be its own section, but because a lot of the resources focus on programming too, I’m including it within programming.
- An Introduction to Statistical Learning
- Introduction to Econometrics with R
- Causal Inference The Mixtape
General Programming
Data Visualization
Tools
- coolors.co: a colour palette generator
Books
- The Visual Display of Quantitative Information, by Edwarde Tufte
- Visual Explanations, by Edward Tufte
- W.E.B. Du Bois’s Data Portraits: Visualizing Black America, by Britt Rusert and Whitney Battle-Baptiste
Behavioural Science & Public Policy
Books
- Behavioural Insights, by Michael Hallsworth and Elspeth Kirkman
- Democracy Declined, by Mallory E. SoRelle
- Noise, by Daniel Kahneman, Olivier Sibony, and Cass Sunstein
- Nudge
- Superforecasting
- Truth Decay