Resources

This is a list of resources for myself primarily, essentially a reminder of things to check out from time to time, or links I’ve gone back to on more than one occasion.

R

• Use R series
• Ropensci Packages for Open Science.
• GitHub trending
• R Views
• RStudio Blog

Stan

• Discourse
• Example models

Python

• Fast AI Teaching and tools for deep learning
• Python Data Science Handbook
• Statsmodels Standard statistical models in Python
• Scikit-learn Efficient tools for data mining and data analysis
• Pandas Dealing with data in an R-like way, sort of

People, Blogs etc.

• Andrew Gelman
• Cosma Shalizi
• RStudio
• R-bloggers
• R Views
• Chris Colah
• Christian Robert
• Awesome List for Machine Learning
• Frank Harrell

Miscellaneous

• Distill
• Mixed Model FAQ
• Factor Analysis vs. Principal Component Analysis

Free Books

Stats & ML

• Deep Learning Book Goodfellow et al.
• Dive into Deep Learning
• Elements of Statistical Learning Bible of ML by Hastie, Tibshirani, & Friedman.
• Probabilistic Machine Learning by Murphy
• Introduction to Statistical Learning Application of the above in R.
• Computer Age Statistical Inference Efron & Hastie.
• Advanced Data Analysis from an Elementary Point of View
• Gaussian Processes for Machine Learning Carl Rasmussen
• Discrete Choice Methods with Simulation Kenneth Train
• An Introduction to Psychometric Theory with Applications in R

Programming

• Advanced R Hadley Wickham
• R for Data Science Hadley Wickham
• R Packages Hadley Wickham
• Bookdown website All R related
• Happy Git and GitHub for the useR