Tech Setup

Why it matters: GitHub is where coursework lives this term: module assignments, your final project, and, if you build one, your personal portfolio site. It is also a standard professional workflow in data science and policy analysis.

How to set it up:

1. Create a free account at github.com if you do not already have one.
2. Install Git on your machine.
3. Configure Git with your name and email:
   git config --global user.name "Your Name"
git config --global user.email "you@example.com"
4. Accept your invite to the course organization on GitHub, where module templates and your project repository will live.

Why it matters: These are the languages used for analysis: cleaning data, mapping it, modeling it, and evaluating policy. R is the primary course language, with strong ecosystems for spatial data, causal inference, and reproducible reports through Quarto. Python alternatives are welcome for your own exploration.

How to set it up:

• R: install R and RStudio, or use VS Code with the R extension.
• Python: install through Anaconda or Miniconda.
• GitHub Codespaces provides a working environment in the browser, so you may not need a local installation.

Why it matters: Some course tools, particularly spatial-data libraries, depend on system software that can be difficult to install. GitHub Codespaces provides a shared browser-based environment with the required tools already installed.

How to set it up:

• Local VS Code: download Visual Studio Code, then install the R or Python extension from the Extensions tab.
• GitHub Codespaces: open a course repository on GitHub, select Code, then Codespaces, then Create codespace on main.
• Free GitHub accounts include 120 core-hours per month; verified students receive more through the GitHub Student Developer Pack.

Why it matters: AI coding assistants can help debug and explain unfamiliar code, and speed up routine work such as data cleaning. Use them critically: the question, the evidence, and the limits of what the data can support remain yours to reason through.

How to set it up:

• GitHub Copilot: install the GitHub Copilot extension in VS Code and sign in with your GitHub account. It is free for verified students through the GitHub Student Developer Pack.
• Claude: install the Claude Code extension, or the Claude integration for VS Code, and sign in with an Anthropic account.
• Local model: install Ollama, then run ollama run qwen2.5-coder:3b in a terminal to download and start a small coding model. Install the Continue extension in VS Code and connect it to Ollama at http://localhost:11434.
• Local models keep prompts and code on your computer, but run best on machines with at least 8 GB of memory. They are useful for short explanations and debugging; hosted assistants will usually be faster and more capable for larger tasks.
• Any of these options works for the course. Use the assistant you are comfortable with, or try more than one.