VS Code for Academics & Students

What This Is

This guide covers the practical setup for using VS Code as a single environment for writing, data analysis, slides, editing, and AI assistance, replacing a dozen separate apps.

For the why, and what a day of academic work looks like inside one editor, read the companion blog post: 95% of My Work Happens in VS Code.

You do not need to be a programmer. If you can write an email, you can use VS Code.

Why VS Code

VS Code is free, runs on Mac, Windows, and Linux, and has extensions for almost anything. More importantly, it’s where AI assistants are most capable: they read and write text, and everything here is text. One environment, one place to work, with an AI assistant alongside you the whole time.

Getting Started

1. Download VS Code. It’s free. Install it.
2. Sign up for GitHub and GitHub Education with your university email. You get GitHub Copilot for free.
3. Install GitHub Copilot from the VS Code extensions marketplace. This is your AI assistant.
4. Pick one task you currently do in another app (writing a document, analysing data, building a presentation) and try doing it in VS Code instead.
5. Ask the AI for help constantly. “How do I create a Markdown file?” “How do I run a Jupyter notebook?” “How do I make this text bold?” There are no stupid questions when you’re talking to an AI.

That’s the whole on-ramp. Everything else you pick up as you need it.

Extensions Worth Installing

Open the extensions marketplace (the squares icon in the sidebar) and add what fits your workflow:

• GitHub Copilot. Your AI assistant (free for academics and students).
• Markdown All in One. Writing, preview, and table tools for Markdown.
• Jupyter. Run notebooks directly in the editor.
• Python. Language support, linting, and environment management.
• Rainbow CSV. Readable CSV viewing.
• Live Server. Live HTML preview (useful for coding slides).
• Overleaf / LaTeX Workshop. For LaTeX documents.

Whatever your workflow involves, there’s probably an extension that brings it into VS Code.

What You Can Do In It

Writing. Draft in Markdown (papers, notes, documentation) with the AI assistant as a copy editor right next to the text. Markdown files are plain text, so they’re portable, diff-able, and version-controllable in a way Word files aren’t.

Data analysis. Describe your dataset and what you want, and the AI writes the code; you review and run it. Be specific:

I have a repeated-measures dataset in data/experiment1.csv with columns: participant_id, condition (A, B, C), reaction_time, accuracy, and session. Three within-subject conditions, some participants have missing sessions, and reaction_time has a right skew. Write me a linear mixed-effects model in Python using statsmodels, with participant as a random intercept, condition as a fixed effect, and Bonferroni-corrected post-hoc pairwise comparisons. Include a check for normality of residuals.

It’s particularly good at catching statistical mistakes you might have glossed over.

Slides. Build lecture and conference decks as HTML. See the HTML slides guide.

Editing and review. Paste a draft and ask for structural feedback, tighter phrasing, or a consistency check across a document.

A Note on Privacy

For sensitive material such as student data or unpublished work, you can run a local model on your own machine instead of a cloud assistant, so nothing leaves your computer. See the Local LLM Setup Guide and the post on why local models matter.

Quick Start Checklist

1. Install VS Code.
2. Sign up for GitHub Education with your university email and enable Copilot.
3. Install the GitHub Copilot, Python, and Jupyter extensions.
4. Move one real task into VS Code this week.
5. Ask the AI for help with every step. That’s how you learn it fastest.

You can be working in VS Code within ten minutes. Start with one real task and keep the AI assistant beside you.