Before diving into coding, let's get oriented. This module introduces the three programming languages I use throughout the course—Python, Stata, and R—and the software environments where you'll write and run your code.

If you're completely new to programming, this can feel overwhelming. Three languages? Multiple softwares? Don't worry. By the end of this module, you'll understand what each tool is for and which ones to focus on first. You don't need to master everything—you need to know where to start.

What You'll Learn

• The differences between Python, Stata, and R—and when to use each
• How to navigate the software environments (IDEs) for each language
• How to set up your computer for coding, or use cloud-based alternatives

The Three Languages

Each language in this course emerged from a different community with different goals. Understanding their origins helps you choose the right one for each task.

Aspect	Python	Stata	R
Born	1991	1985	1993
Origin	General-purpose scripting	Statistical analysis	Statistical computing
License	Free, open-source	Commercial (paid)	Free, open-source
Strength	ML, versatility, AI tools	Econometrics, panel data	Statistics, visualization
Learning curve	Gentle	Gentle for basics	Steeper initially
AI assistance	Excellent	Good	Good

My Recommendation: Start with Python and Stata

One of the most common questions I get is "Which language should I learn first?" Here's my answer as of the beginning of this course (Jan 2026):

The Software Environments (IDEs)

An IDE (Integrated Development Environment) is the software where you write and run your code. Think of it like a word processor for code—it provides syntax highlighting, error checking, and tools to run your programs.

Each language has a preferred environment, but some IDEs (like VS Code) can handle multiple languages. I've created detailed guides for each:

(Note: part 2 of the course --ProTools ER2-- will cover AI-powered IDEs like "Claude code desktop" and "LM studio". For the moment, however, it is important to learn coding outside of fully-AI-assisted environments. Hold your FOMO! (="Fear Of Missing Out", for boomers))

Quick Start: No Installation Needed

If you want to start coding immediately without installing anything, use these cloud-based options:

Language	Cloud Option	Link
Python	Google Colab	colab.research.google.com
R	Posit Cloud	posit.cloud
Stata	University remote desktop	Check with your IT department

Which Tool for Which Task?

Here's some tips to choosing tools for common research tasks (these suggestions assume you are a PhD candidate in Economics; but even so, take them with a pinch of salt: "most needed" tools vary across subfields. In the end, you need to figure out what's best!):

Task	(Probably) best tool	Why
Replicating an economics paper	Stata	Most replication packages are in Stata (statement valid as of now; expect things to change)
Machine learning / Deep learning	Python	Python has excellent libraries. You can run code in Colab (an online IDE) if you want to use cloud GPUs (however, you'll need to pay ...)
Publication-quality visualizations	R	Great visualization capabilities (eg ggplot2)
Web scraping	Python	Best libraries (BeautifulSoup, Selenium)
Panel data econometrics	Stata or R	Purpose-built for this
Quick exploratory analysis	I use Python, but you'll use what you are most comfortable with. I often end up using an online IDE for those tasks (eg. Colab)	No setup, immediate results
Collaborating with economists	popularity is still: Stata>=R>>Python (probably, my guess).	Any language really; expect python to gain momentum.
Learning with AI assistance	Python	AI tools are most fluent in Python

File Formats Reference

As you work with code and data, you'll encounter many different file types. The key distinction to remember:

Files by Language

Each programming language has its own file formats. Here's what belongs to what:

What's Next?

Now that you understand the landscape, here's what to do:

1. Choose your starting point. If you're new to programming, I recommend starting with Python in Google Colab (zero setup required). However, the learning habits you set up initially may be very persistent. So, I encourage you to explore different environments early on to find what works best for you. I particularly enjoy working on Visual Studio because it provides a robust development environment with excellent debugging capabilities, and you can code in different languages, all in one environment.
2. Read the relevant guide. Use the guide cards above to learn your chosen environment.
3. Move to Module 1. Once you can run code, you're ready to start learning the basics.

Don't feel like you need to read all the IDE guides now. You can always come back to them when you need a specific tool. The most important thing is to start coding. Look at the bottom right of the page: there is a chatbot assistant especially trained to answer questions on the course materials. Use it to ask questions as you go along! A log (ie a transcript) of the conversations will be stored and I will use it to improve the course and expand the course materials in the direction people need the most. So, using the chatbot you'll be contributing to a public good!