A work-in-progress collection of resources I find useful for data science work. These resources are all freely available online unless otherwise noted.
- RStudio’s Blog
- Julia Silge - Lots of great walkthroughs of machine learning problems with tidymodels
- Andrew Gelman - Bayesian statistics
- TJ Mahr - R and Bayesian stats
- Simply Statistics
- David Robinson - R and machine learning
- Monica Alexander - Bayesian statistics and demography
- Frank Harrell - Biostatistics
- Richard McElreath
- Introduction to Statistical Learning - A great practical statistics and data science textbook with a very broad scope. The second edition includes survival analysis and neural network models.
- Statistical Rethinking (Richard McElreath) - Very useful for understanding Bayesian statistical methods. The book is not freely available online, but there are lots of related resources that are such as Statistical rethinking with brms (Solomon Kurz) and video lectures on YouTube.
- R for Data Science - An introduction to R
- Regression and Other Stories - An applied textbook on Bayesian statistics. Makes use of the rstanarm package. Not freely available online, but there are code examples provided on the website.
- Telling Stories With Data (Rohan Alexander) - Practical applications of R and Bayesian statistics
- Forecasting - Principles and Practice - Textbook on time series and forecasting methods
- Text Mining with R
- RWeekly - a blog aggregator for keeping track of what’s going on in the R community.
- Tidymodels Documentation
- Data Science Digest
- Distill - Machine learning concepts explained intuitively
- Biostat Handbook - general statistics reference
- RStudio Cheatsheets
- R Markdown - The Definitive Guide
- R Markdown Cookbook
- Stan User’s Guide - A helpful reference for Bayesian modeling.
- data-to-viz - a general data visualization reference
- “Top 50” ggplot visualizations
- BBC R Cookbook - ggplot code reference
- Data Visualization - A Practical Introduction