R/Python/Latex
- Data Visualization with R, by Martin Schweinberger
- Hadley Wickham's website
- Andrew Heiss' blog
- The R Workshop Book, by Rebecca L. Barter
- Python Numpy Tutorial (with Jupyter and Colab), by Justin Johnson
- A Practical Introduction to Python Programming, by Brian Heinold
- Causal Inference Lectures Compilation, by Matteo Courthoud
- Beamer Theme Matrix, by Matthew Petroff
- Reinforcement Learning (Stanford CS234), by Emma Brunskill
- Beamer User Guide, by Till Tantau, Joseph Wright and Vedran Miletić
- Getting Started with Quarto, by Tom Mock
- Principles of Econometrics with R, by Constantin Colonescu
- Applied Econometrics with R, by Christian Kleiber and Achim Zeileis
- Big Book of R, by Oscar Baruffa
- Using R, Python and Julia for Introductory Econometrics, by Florian Heiss and Daniel Brunner
- Michael Minn's Tutorials
- From R to Python, by Rebecca Barter
- Statistical Methods, by Rob Kabacoff
- Comprehensive Latex Symbols, by Scott Pakin
- R for Data Science, by Hadley Wickham and Garrett Grolemund
- Efficient R Programming, by Colin Gillespie and Robin Lovelace
- Stata to R Dictionary, by Jan Zilinsky
- Get started with Julia
- Making maps in R, by Milos Popovic
- Geocomputation with R, by Robin Lovelace, Jakub Nowosad, and Jannes Muenchow
Econometrics/Stats/ML/Maths
- Intuitive MetriX – Ben Elsner's Youtube Chanel
- How the (Econometric) Sausage is Made, by Daniel L. Millimet
- The Mixtape, by Scott Cunningham
- Applied Causal Inference Powered by ML and AI, by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis (book)
- Data Analysis for Social Sciences, a practice guide for Instructors, by Elena Llaudet
- Machine Learning and Causal Inference: A short course (Stanford's Youtube Chanel)
- Stochastic Calculus, by Michael R. Tehranchi
- Bayesian Nonparametric Stats, by Ismael Castillo
- Holger Paul Keeler's website
- Bayesian Statistics Resources, by Andrew Heiss
- Probably Overthinking It: Data science, Bayesian Statistics, and other ideas, by Allen Downey
- The 100% CI, by Anne Scheel, Ruben Arslan, Malte Elson, and Julia Rohrer
- The Matrix Calculus You Need For Deep Learning, by Terence Parr and Jeremy Howard
- Machine Learning for Economists, by Itamar Caspi
- Modern Statistics with R: From wrangling and exploring data to inference and predictive modelling, by Måns Thulin
- Statistical Modeling, Causal Inference, and Social Science, by Andrew Gelman
- Decision Making and Modelling Under Uncertainty, by Oliver Maclaren
- Probabilistic Machine Learning: An Introduction, by Kevin Patrick Murphy
- Metrix, by Peter Hull
- Jonathan Klein's website
- Mastering Mostly Harmless Econometrics
- Asjad Naqvi on Medium
- A Course on Causal Inference and Machine Learning, by Guido Imbens
- Asjad Naqvi's Blog
- Machine Learning and Econometrics, by Emmanuel Flachaire
- Amine Charles-Louis Ouazad's website
- Bruce Hansen's website
- Michael Betancourt's website
- Christoph Kronenberg's Resources
- Tyler Ransom's Econometrics III (PhD)
- Dan Shiebler's blog
- Mathematics for Economists, by Nathan Barczi and Ufuk Akcigit
- Math in Graduate Economics Resources, by David R. Wilkins
- Theory and Practice of Econometrics, by Jeffrey Parker
- Francisco Carpena's website
- Jonathan Newton's blog
Applied/Micro/Game Theory
Papers I Think You Should Read
- A Practical Guide to Weak Instruments, by Michael P. Keane and Timothy Neal
- Everything Wrong with P-Values Under One Roof, by William M Briggs
- The Cult of Statistical Significance, by Stephen T. Ziliak and Deirdre N. McCloskey
- Which Findings Should Be Published?, by Alexander Frankel and Maximilian Kasy
- I Just Ran Four Million Regressions, by Xavier X. Sala-i-Martin
- Statistical Control Requires Causal Justification, by Anna C. Wysocki, Katherine M. Lawson, and Mijke Rhemtulla
- Empirical Strategies in Economics: Illuminating the Path from Cause to Effect, by Joshua Angrist
- Sinning in the Basement: What Are The Rules? The Ten Commandments of Applied Econometrics, by Peter E. Kennedy
- Transparency, Reproducibility, and the Credibility of Economics Research, by Garret Christensen and Edward Miguel
- The State of Applied Econometrics: Causality and Policy Evaluation, by Susan Athey and Guido W. Imbens
How to Econ
- Models, Measurement, and the Language of Empirical Economics, by Phil Haile
- List of Economics Conferences, maintaned by Anne M. Burton and Barton Willage
- Coding for Economists: A Language-Agnostic Guide to Programming for Economists, by Ljubica “LJ” Ristovska
- Academic Publishing in Agricultural Economics: Getting it Right and Making it Work, by David Ubilava
- Data Science for Economists, by Grant McDermott
- Git for Economists, by Arieda Muço
- Grad School Advice, by Anthony Lee Zhang
- Creating a data cleaning workflow, by Crystal Lewis
- Why You Need an Academic Website, by Benjamin Noble
- Advice for Phd Students in Economics, by Chris Roth and David Schindler
- How to Run Surveys: A Guide to Creating Your Own Identifying Variation and Revealing the Invisible, by Stefanie Stantcheva
- Code and Data for the Social Sciences: A Practitioner's Guide, by Matthew Gentzkow and Jesse Shapiro
- Public Speaking for Academic Economists, by Rachel Meager
- Working Paper Template, by Fabien Petit
- Code, Data, and Version Control: Best Practices for Economic Research, by Brendan Price
- Academic Presenting: How to get it right and make it work, by David Ubilava
- A long guide to giving a short academic talk, by Benjamin Noble