(programming-language-neutral)
- Hans-Martin von Gaudecker - Effective programming practices for economists
- Jesús Fernández-Villaverde - Computational Methods for Economists
- Richard W. Evans, et al. - Open Source Economics Laboratory (OSE Lab) Boot Camp 2019, 2018, 2017
- Kenneth Judd - Computational Economics 2020
- Gentzkow, M., & Shapiro, J. M. (2014). Code and data for the social sciences: A practitioner’s guide. Chicago, IL: University of Chicago. (Github manual)
- Knittel, C. R., & Metaxoglou, K. (2016). Working with Data: Two Empiricists’ Experience. Journal of Econometric Methods, 7(1).
- Christensen, Garret S. and Edward Miguel, "Transparency, Reproducibility, and the Credibility of Economics Research," Journal of Economic Literature, 56:3 pp. 920-980 (Sep. 2018).
- Coding for Economists: A Language-Agnostic Guide to Programming for Economists - Ljubica Ristovska
- Templates for Reproducible Research Projects in Economics
- Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough practices in scientific computing. PLoS computational biology, 13(6), e1005510.
- The Plain Person’s Guide to Plain Text Social Science - Kieran Healy
- Data and Code Guidance by Data Editors (also see the AEA guidance on Data and Code) (github)
- Best Practices when Writing Code
- Patrick Ball - The Task Is A Quantum Of Workflow and Principled Data Processing
- The Turing Way (a lightly opinionated guide to reproducible data science)
- Fernando Hoces - Accelerating Computational Reproducibility in Economics
- Michael Keane - Practical Issues in Structural Estimation
- Chris Taber - Estimation of Policy Counterfactuals / Structural Estimation
- Tony Smith - Tips for Doing Computational Work in Economics
- QuantEcon - Python (also here)
- QuantEcon DataScience
- Richard W. Evans -
- Perspectives on Computational Modeling for Economics 2020
- Perspectives on Computational Research in Economics 2020
- Structural Estimation 2020
- Git and GitHub tutorial
- Jason DeBacker - Computational Methods for Economists 2017, 2019
- Fedor Iskhakov - Dynamic programming and structural estimation
- Fedor Iskhakov - Foundations of Computational Economics
- Jeppe Druedahl - Introduction to Programming and Numerical Analysis
- Kevin Sheppard - Introduction to Python for Econometrics, Statistics and Numerical Analysis
- NYU-Data-Bootcamp
- Matheus Facure - Python Causality Handbook
- OpenSourceEconomics - Scientific Computing
- OpenSourceEconomics - Microeconometrics
- Jake VanderPlas - Python Data Science Handbook / A Whirlwind Tour of Python
- Nick Eubank - Data Analysis in Python
- Tom Augspurger - Modern Pandas
- Introduction to Python for Science
- Computational Statistics in Python
- Python computational labs
- Real Python Tutorials
- Scipy Lecture Notes
- Claudio Jolowicz - Hypermodern Python
- Nicolas P. Rougier - From Python to Numpy
- Nicolas P. Rougier - Scientific Visualization – Python & Matplotlib / Matplotlib cheat sheet
- Introduction to Mathematical Computing with Python and Jupyter
- Reproducible and Collaborative Data Science
- Introduction to Data Science and Programming
- QuantEcon - Julia
- Paul Schrimpf - Computational Economics with Data Science Applications
- Paul Schrimpf - UBC ECON567 IO
- Ivan Rudik - Environmental and Resource Economics (computational methods for economics)
- Tyler Ransom - Econometrics III
- Michael Creel - Econometrics lecture notes with examples using the Julia language
- Bradley J. Setzler - An Introduction to Structural Econometrics in Julia
- Florian Oswald - Computational Economics for PhDs
- Tutorials on Topics in Julia Programming
- Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence - H.Klok, Y.Nazarathy
- Think Julia: How to Think Like a Computer Scientist - Ben Lauwens
- Introduction to Computational Thinking - MIT
- Maximilian Kasy - Advanced Econometrics and Machine Learning: 2019, 2000 / collection of useful computation links on R and ML
- Nick Huntington-Klein - Economics, Causality, and Analytics / R resources
- Nick Huntington-Klein et al. - Library of Statistical Techniques
- Francis J. DiTraglia - Statistical Learning and Causal Inference for Economics
- Tom O'Grady - Causal-Inference-for-Beginning-Undergraduates
- Grant McDermott - Data science for economists / Big Data in Economics
- Christoph Hanck, et al. - Introduction to Econometrics with R
- Ed Rubin - PhD Econometrics (III) with R / Masters-level applied econometrics with R / Introduction to Econometrics with R
- Tyler Ransom - Econometric Analysis (U)
- Hans H. Sievertsen - Applied Economics with R
- Matt Woerman - Topics in Advanced Econometrics
- Garrett Grolemund & Hadley Wickham - R for Data Science
- Hadley Wickham - Advanced R
- The tidyverse style guide
- dlab-berkeley - R-Fundamentals / ...
- Jeffrey Arnold - R Code for Mastering ’Metrics
- Gina Reynolds - R Flipbook Textbook
- R for Health Data Science
- A ModernDive into R and the Tidyverse
- Andrew Heiss - Program Evaluation for Public Service / Data Visualization
- R Data Science Tutorials (resources)
- Dario Sansone - Machine Learning for Economists (Resources)
- Tyler Ransom - Data Science for Economists 2020 (R) / Introduction to machine learning for social scientists
- Ed Rubin and Connor Lennon - Prediction and machine-learning in econometrics (R)
- Itamar Caspi - A course in machine learning for economists (R)
- Stephen Hansen - machine_learning_economics / text-mining-tutorial (Python)
- Jérémy L'Hour - Machine Learning for Econometrics (R)
- Le Wang - Machine Learning and Causal Inference (R)
- Kathy Baylis et al. - Machine learning in applied economics (Python)
- Dawie van Lill - Data Science for Economics (Python)
- Economics and Data Science (Resources)
- Jonathan Hersh - Introduction to Machine Learning
- Andreas C. Müller - Applied Machine Learning Spring 2020 (python)
- Tyler Folkman - Applied Machine Learning (python)
- Chris Albon - Data Science & Artificial Intelligence (Resources)
- Practical tips for machine learning practitioners
- Hands-On Machine Learning with R
- Deep Learning Drizzle (Links)
- Fundamentals of data analysis and visualization
- Economics Lesson with Stata
- Statistical Analysis - UCLA
- Statistics cheatsheet (R, Python, Stata) - QuantEcon
- Stata to Python Equivalents - Daniel M. Sullivan
- Pandas comparison with Stata
- Stata Coding Guide - Julian Reif
- pure bash bible
- useful bash scripts shared by economists: Ed Rubin, John Horton
- macOS Setup Guide (somehow outdated)
- The Unix Shell - Software Carpentry
- Corey Schafer's youtube channel (very beginner-friendly videos for almost all basic things about mac, python, git, etc.)
- Terminal: here, here, here
- The Art of Command Line
- git for social science students (not software developers) - Shiro Kuriwaki
- Version Control for Economists - Wei Yang Tham
- Git for Economists - Frank Pinter
- Git, GitHub, and Version Control - QuantEcon
- Git and GitHub tutorial chapter - Richard Evans
- Bryan, J. (2018). Excuse me, do you have a moment to talk about version control?. The American Statistician, 72(1), 20-27.
- Bruno, R. (2016). Version control systems to facilitate research collaboration in economics. Computational Economics, 48(3), 547-553.
- git + LaTeX workflow - stackoverflow
- Pro Git book - Scott Chacon & Ben Straub
- Git Tutorials and Training - Atlassian
- Using Git & GitHub Guides - github
- Git & Version Control FAQ - git-tower
- Happy Git and GitHub for the useR - Jenny Bryan
- Git for Scientists - Miles McBain
- Flight rules for git
- Collaborative Models in GitHub / Collaborating on GitHub / GitHub for Collaboration On Open Projects / Development workflow / GitHub Standard Fork & Pull Request Workflow / Git mergetool tutorial
- The Not So Short Introduction to LATEX - Tobias Oetiker
- A simple guide to LaTeX - Step by Step
- Overleaf guides to LaTeX
- Tips + Tricks with Beamer for Economists - Paul Goldsmith-Pinkham
- Template-based introductory guide to LaTeX for Economics
- The Markdown Guide
- Markdown Reference - typora (some Japanese introduction on typora)
- Schwabish, J. A. (2014). An economist's guide to visualizing data. Journal of Economic Perspectives, 28(1), 209-34.
- Some Data visualizations in Python
- Python Plotting for Exploratory Data Analysis
- from Data to Viz - The Python Graph Gallery / The R Graph Gallery
- Kieran Healy - Data Visualization - A practical introduction
- ggplot2: Elegant Graphics for Data Analysis
- (a real) Econ RA Guide
- Aruoba, S. B., & Fernández-Villaverde, J. (2018). A comparison of programming languages in economics.
- Awesome Scientific Writing
- awesome-causality-algorithms
- MATLAB–Python–Julia cheatsheet
- Merely Useful (python and r lecture)
- Analysis of Human Capital - Labor Economics / Econometrics of Human Capital
- Joao B. Duarte - Advanced Macro
- Bryan S. Graham - Econometrics
- Alfred Galichon - Advanced Topics in Microeconometrics
- Alfred Galichon - 'math+econ+code' series
- SciencePo - (UG) Econometrics
- Dietz Vollrath - (UG) Growth
- Ömer Özak - Economic Growth and Comparative Development / Macroeconomics II
- Jonathan Dingel - International Macroeconomics and Trade
- David Ubilava - (UG) Agricultural Markets
- mostly-harmless-replication in Stata, R, Python and Julia
- Ivan Rudik - Environmental and Resource Economics
- Scott Cunningham - causal-inference-class
- Advice for current and aspiring academic economists - Jennifer Doleac
- Writing, Presentation, and Refereeing Advice - Amanda Y. Agan
- Resource - Ryan B Edwards
- Tips 4 Economists - Masayuki Kudamatsu
- Research resources that I recommend - Jonathan Dingel
- Data Science and Economics
- Advice resource - Jonathan Dingel
- Mentoring Reading Materials - AEA-CSWEP
- Resources - Mizuhito Suzuki