AI Coding Prep

Tools, environment, and prompting habits to have in place before Week 1

This is the before-the-course prep page: get your environment ready and pick up a few prompting habits that the weekly material will assume from Week 1 onward. None of this is a session — work through it on your own.

If you have already used Copilot/Cursor on a real project and you know what “give it a 1/1000 sample plus a data dictionary” means, you can skip the page entirely.

What you need installed

Required

A working Python or R setup you’ve used before. Stata is fine for the early weeks of the course but the capstone is realistically Python or R.
A ChatGPT account (free tier is fine for Week 1).
A Claude account (free tier is fine).

Strongly recommended

VS Code + the GitHub Copilot extension. Walkthrough: VS Code + Copilot setup.
A GitHub account — free Copilot for students via the GitHub Student Pack.

Optional

Cursor — AI-first editor; some students prefer it to VS Code.
An AI CLI (Claude Code, Codex). You’ll install one of these in Week 4 — see Installing AI CLI Tools when you get there. No need to do it now.

Why AI works well for data-analysis code

It is worth being explicit about why AI is unusually good at this slice of coding, because it tells you where to trust it and where not to.

Repetitive patterns. Load → clean → analyse → visualise is the same shape across most projects.
Well-documented libraries. pandas, dplyr, ggplot2, scikit-learn — extensive coverage in training data.
Specific intent. “Scatter of sales against employment, regression line, ggplot” leaves little room for ambiguity.
Iteration is cheap. Chat is the right interface for “almost — now make the axis log”.

Where AI is reliably not enough on its own: research-design choices, econometric interpretation, domain-specific logic, and any quality control you would do on a colleague’s code.

Prompting habits for code

A short list. The course will reinforce all of these, but the earlier you internalise them the smoother Weeks 1–3 will be.

Be specific about the stack.

“Using pandas and seaborn …” or “Using R and tidyverse …”.
“I prefer polars to pandas unless you need a pandas-only feature.”
“Use fixest::feols for regressions, not lm.”

Show the data shape.

Paste the column list and dtypes, or a 5–10 row sample.
For larger files, upload a 1/1000 random sample rather than the full file.
Even better: hand it a data dictionary first (Week 2 covers how to build one).

Specify the output you want.

“Save as PNG, 1200 × 800, for a paper figure.”
“Return a tidy data frame, one row per (country, year).”
“Markdown with LaTeX equations inline, no code block.”

Define defaults once, reuse them.

In ChatGPT Custom Instructions or a Claude Project: language, library preferences, comment style, OS.
In Cursor/Claude Code: a CLAUDE.md or rules file at the repo root (Week 5).

Two sample prompts to compare

Try both on your own data and notice the difference.

Vague

Here is some sales data, summarise regional variation.

Specific

Using Python, pandas, and plotnine:

1. Load `sales.csv` with columns id, date, sales, region.
2. Filter to 2023.
3. Group by region, compute mean and median sales.
4. Bar chart of mean sales by region, ordered descending, viridis palette.
Return the code only, with brief comments.

The second is boring on purpose — boring is what reproduces.

Tools, in one place

A quick map of what each tool is good for. You don’t need all of these.

Tool	Good for	Notes
ChatGPT (Canvas, Advanced Data Analysis)	Quick exploration, data uploads, chart drafts	“Code Interpreter” runs Python in-browser
Claude Projects + Artifacts	Carrying a codebook / data dictionary across many turns	Used heavily in Week 2
GitHub Copilot	Inline completion in your editor	Works in VS Code, RStudio (via extension), JupyterLab
Cursor	AI-first editor with whole-repo context	Some students prefer it to VS Code + Copilot
Claude Code / Codex (CLI)	Agentic work on a real repo	Introduced in Week 4 — install then

Self-check before Week 1

You’re ready if you can, in 10 minutes:

Open a CSV in your editor of choice and run a small script with Copilot inline suggestions on.
Paste the same task into ChatGPT or Claude with a specific prompt and get runnable code back.
Take that code, drop it in your editor, run it, and iterate on the output.

If any of those three is friction, fix it now rather than mid-Week-2.

--- title: "AI Coding Prep" subtitle: "Tools, environment, and prompting habits to have in place before Week 1" --- This is the **before-the-course** prep page: get your environment ready and pick up a few prompting habits that the weekly material will assume from Week 1 onward. None of this is a session — work through it on your own. If you have already used Copilot/Cursor on a real project and you know what "give it a 1/1000 sample plus a data dictionary" means, you can skip the page entirely. ------------------------------------------------------------------------ ## What you need installed **Required** - A working **Python or R** setup you've used before. Stata is fine for the early weeks of the course but the capstone is realistically Python or R. - A **ChatGPT** account (free tier is fine for Week 1). - A **Claude** account (free tier is fine). **Strongly recommended** - **VS Code** + the GitHub Copilot extension. Walkthrough: [VS Code + Copilot setup](vscode-setup.qmd). - A **GitHub** account — free Copilot for students via the [GitHub Student Pack](https://education.github.com/pack). **Optional** - [Cursor](https://www.cursor.com/) — AI-first editor; some students prefer it to VS Code. - An AI CLI (Claude Code, Codex). You'll install one of these in Week 4 — see [Installing AI CLI Tools](install-cli.qmd) when you get there. No need to do it now. ------------------------------------------------------------------------ ## Why AI works well for data-analysis code It is worth being explicit about *why* AI is unusually good at this slice of coding, because it tells you where to trust it and where not to. - **Repetitive patterns.** Load → clean → analyse → visualise is the same shape across most projects. - **Well-documented libraries.** pandas, dplyr, ggplot2, scikit-learn — extensive coverage in training data. - **Specific intent.** "Scatter of `sales` against `employment`, regression line, ggplot" leaves little room for ambiguity. - **Iteration is cheap.** Chat is the right interface for "almost — now make the axis log". Where AI is reliably *not* enough on its own: research-design choices, econometric interpretation, domain-specific logic, and any quality control you would do on a colleague's code. ------------------------------------------------------------------------ ## Prompting habits for code A short list. The course will reinforce all of these, but the earlier you internalise them the smoother Weeks 1–3 will be. **Be specific about the stack.** - "Using pandas and seaborn …" or "Using R and tidyverse …". - "I prefer polars to pandas unless you need a pandas-only feature." - "Use `fixest::feols` for regressions, not `lm`." **Show the data shape.** - Paste the column list and dtypes, or a 5–10 row sample. - For larger files, upload a **1/1000 random sample** rather than the full file. - Even better: hand it a **data dictionary** first (Week 2 covers how to build one). **Specify the output you want.** - "Save as PNG, 1200 × 800, for a paper figure." - "Return a tidy data frame, one row per (country, year)." - "Markdown with LaTeX equations inline, no code block." **Define defaults once, reuse them.** - In ChatGPT Custom Instructions or a Claude Project: language, library preferences, comment style, OS. - In Cursor/Claude Code: a `CLAUDE.md` or rules file at the repo root (Week 5). ------------------------------------------------------------------------ ## Two sample prompts to compare Try both on your own data and notice the difference. **Vague** ``` Here is some sales data, summarise regional variation. ``` **Specific** ``` Using Python, pandas, and plotnine: 1. Load `sales.csv` with columns id, date, sales, region. 2. Filter to 2023. 3. Group by region, compute mean and median sales. 4. Bar chart of mean sales by region, ordered descending, viridis palette. Return the code only, with brief comments. ``` The second is boring on purpose — boring is what reproduces. ------------------------------------------------------------------------ ## Tools, in one place A quick map of what each tool is good for. You don't need all of these. | Tool | Good for | Notes | |---|---|---| | **ChatGPT** (Canvas, Advanced Data Analysis) | Quick exploration, data uploads, chart drafts | "Code Interpreter" runs Python in-browser | | **Claude Projects + Artifacts** | Carrying a codebook / data dictionary across many turns | Used heavily in Week 2 | | **GitHub Copilot** | Inline completion in your editor | Works in VS Code, RStudio (via extension), JupyterLab | | **Cursor** | AI-first editor with whole-repo context | Some students prefer it to VS Code + Copilot | | **Claude Code / Codex** (CLI) | Agentic work on a real repo | Introduced in Week 4 — install then | ------------------------------------------------------------------------ ## Self-check before Week 1 You're ready if you can, in 10 minutes: 1. Open a CSV in your editor of choice and run a small script with Copilot inline suggestions on. 2. Paste the same task into ChatGPT or Claude with a specific prompt and get runnable code back. 3. Take that code, drop it in your editor, run it, and iterate on the output. If any of those three is friction, fix it now rather than mid-Week-2.