Coding Resources for Social Sciences
In this page, I collect useful resources for coding for researchers in social sciences. A mention goes to Maximilian Kasy that inspired me to build this page.
A quick legend:
- π book
- π webpage
- π charts
- π₯ videos
Econometrics and Statistics
- πBruce Hansenβs Econometrics: By far the best freely available and regularly updated resource for Econometrics
Machine Learning
- πThe Elements of Statistical Learning: General introduction to machine learning
- πGaussian Processes for Machine Learning: Extremely useful tools for nonparametric Bayesian modeling
- πDeep Learning: The theory and implementation of neural nets
- πUnderstanding Machine Learning: From Theory to Algorithms: An introduction to statistical learning theory in the tradition of Vapnik
- πReinforcement Learning - An Introduction: Adaptive learning for Markov decision problems
- πAlgorithms: Introduction to the theory of algorithms
- πTensorflow Playground: Visualisation tool for neural networks
- πArtificial Intelligence: Online lectures on AI
- πThe Ethical Algorithm: How to impose normative constraints on ML and other algorithms
Python
- πRealPython: Collection of Python tutorials, from introductory to advanced. Also contains learning paths for specific topics
- πQuantEcon Python Tutorials and economic applications in Python, especially for macroeconomics
- πCheat Sheets: Collection of cheat sheets for python
- πStructuring a Python project: Advanced tutorial on how to structure a Python program
- πIDE Guide: Comparison of IDEs for Python. Suggested: PyCharm
- πConfiguring remote interpreters via SSH: How to use Python remotely via SSH via PyCharm
- πVisualization in Python: How to make nice graphs in Python, with a dedicated jupyter notebook
- πPython Graph Gallery: Graph examples in Python
Matlab
- πUser defined classes in Matlab: How to work with classes in Matlab
- πJulyter Notebooks: How to run a jupyter notebook with Matlab kernel
- πGraph Tips in Matlab and link2: Suggestions on how to make pretty graphs in Matlab
Julia
- πJulia Manual: Julia unfortunately lacks a big community and tutorials, but it has a very good manual
- πQuantEcon Julia Tutorials and economic applications in Julia, especially for macroeconomics
- πIDE Guide: Guide for IDEs for Julia. Suggested: Juno for Atom.
R
-
πAn Introduction to R: Complete introduction to base R
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πR for Data Science Introduction to data analysis using R, focused on the tidyverse packages
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πAdvanced R: In depth discussion of programming in R
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πHands-On Machine Learning with R: Fitting ML models in R
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πBayesian statistics using Stan and link
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πRStudio Cheat Sheets for various extensions, including data processing, visualization, writing web apps, β¦
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πR Graph Gallery: Graph examples in R
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A collection of elaborate graphs with code in R
Others
- πGithub Advanced: Advanced guide for version control with Github
- π₯The Missing Semester of Your CS Education Video lectures and notes on tools for computer scientists (version control, debugging, β¦)
- πPGF plots in Latex: Gallery and examples to make plots directly in Latex
- πWork remotely from server: How to setup SSH for remote computing