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We will be using Python as our main programming language, Jupyter for our interactive coding and analyses, and QGIS for basic GIS exploration. You should consider also installing and learning R, RStudio, and Quarto.

In order to use some of the material available on this website and to share your material with others you should create a GitHub account for yourself. This will be useful to you in the future to keep track of changes when you are writing papers. I also recommend creating a Bitbucket account, which has similar functionality, but allows you to have unlimited private repositories for personal use (you won't need this if you have GitHub's educational benefits).

Additionally, I suggest you read Gentzkow & Shapiro's Code and Data for the Social Sciences: A Practitioner’s Guide to familiarize yourself with good practices in coding and statistical analysis. We will cover additional topics in class.

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Guides to Software and Tools

Follow the guides below to have all the software and resources we will use.

• How to create a GitHub account
• How to create a Deepnote account
• Computation with Python, R, Julia, etc. using Anaconda
• Computation using Docker
• Analyzing and Mapping Geographic Information System Data with QGIS
• Creating and Backing-up Stata Environment
• Creating and Editing Documents, Websites, etc.
• Suggestions for MacOS users
• Using the Supercomputer/cluster

Computational Framework

To use the notebooks below, you can:

• (Easy - Online no install required) Click on and join the project's workspace. You will be able to run the code on the web. If you want to edit the code for your own use, you can clone the workspace (you will need to register, which is free - especially if you have an educational email account)
• (Easy - Online no install required) Click on
• (A bit more complex - needs installation and runs on your computer)
  • Use Docker
  • Use Anaconda.

Notebooks & Slides

Basics

1. Introduction to Jupyter and Markdown (Notebook) (html) (slides) (Deepnote)
2. Introduction to Python (Notebook) (html) (slides) (Deepnote)
3. Introduction to Python II - Statements (Notebook) (html) (slides) (Deepnote)
4. Introduction to Python III - Functions and Packages (Notebook) (html) (slides) (Deepnote)

Data Analysis

1. Introduction to Data Analysis with Pandas (Notebook) (html) (slides) (Deepnote)
2. More Economic Data Analysis with Pandas (Notebook) (html) (slides) (Deepnote)
3. Working with World Development Indicators (Notebook) (html) (slides) (Deepnote)
4. Working with Penn World Table (Notebook) (html) (slides) (Deepnote)
5. Working with GapMinder (Notebook) (html) (slides) (Deepnote)

GIS

1. GIS with QGIS (Notebook) (html) (Deepnote)
2. GIS with Python - Geometries (Notebook) (html) (Deepnote)
3. GIS with Python - Rasters (Notebook) (html) (Deepnote)
4. GIS with Python - Data Munging (Notebook) (html) (Deepnote)

Basic Economic Modelling

1. Introduction to CGE (Notebook) (html) (Deepnote)
2. Dynamic Programming in Python (Notebook) (html) (Deepnote)
3. Faster Dynamic Programming with Numba (Notebook) (html) (Deepnote)

Stata and R

1. Stata Jupyter Notebooks (Stata kernel) (Notebook) (html)
2. Stata in a Python Jupyter Notebook (ipystata) (Notebook) (html)

Older Material

1. Introduction (html) (ipynb)

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Try it out online

If you want to try out some of the tools and play around without installing or if installation fails, use this Jupyter Notebook binder, the JupyterLab binder, or one of the links below.