Workshops
Three quick 30-minute workshops to get started with AI-assisted data analysis using Cursor. Choose your starting point or go through them in order for the full experience.
Before you begin, see Setup to download the necessary software and data.
Only use Cursor with files that can be made public. All files in a Cursor workspace may be indexed and shared with AI tools, even if you don’t enter them into the chat. Never use Cursor with personal or confidential data.
More detail: UBC AI guidance.
1. Fundamentals — 30 min
Write better prompts using the prompt formula. Learn why specificity matters.
Learning objectives
By the end of this workshop, you will know:
- How LLMs work and why tokens matter
- The prompt formula: context + task + constraints + format
- How to write focused, specific prompts that work
- Why conversation history affects your results
Time Breakdown:
| Time | Activity |
|---|---|
| 0–5 min | Key Idea: Tokens & Context |
| 5–12 min | The Prompt Formula |
| 12–25 min | Quick Try-Out in Cursor Chat |
| 25–30 min | Key Takeaways & Next Steps |
2. Data Analysis & Visualization — 30 min
Create charts with pandas and matplotlib. Build data visualization skills.
Learning objectives
By the end of this workshop, you will know:
- How to describe visualizations to AI
- When to use bar plots, scatter plots, and box plots
- Generate code by prompting Cursor (pandas and matplotlib)
- Build charts by specifying what you want, not syntax
Time Breakdown:
| Time | Activity |
|---|---|
| 0–2 min | Setup (pandas, matplotlib, get data) |
| 2–10 min | Chart 1: Bar Plot Tutorial |
| 10–18 min | Chart 2: Scatter Plot Tutorial |
| 18–28 min | Your Turn (pick & build one chart) |
| 28–30 min | Key Takeaways |
3. Building with AI — 30 min
Build analysis workflow in Python: load → clean → summarize → plot (pandas + matplotlib).
Learning objectives
By the end of this workshop, you will know:
- How to use Cursor Chat for real data analysis in Python
- Build a workflow from data loading to visualization with pandas and matplotlib
- Debug errors using AI conversation
- Iterate and improve code through prompts
- Consider ethical implications of using AI in coding and data analysis
- Identify practical, real-world applications for AI-assisted workflows
Time Breakdown:
| Time | Activity |
|---|---|
| 0–5 min | Step 1: Load & Inspect Data |
| 5–10 min | Step 2: Clean Data |
| 10–15 min | Step 3: Summary Statistics |
| 15–20 min | Step 4: Create Visualization |
| 20–23 min | If something goes wrong (debugging) |
| 23–26 min | Ethics and responsible use |
| 26–28 min | Real-world applications |
| 28–30 min | Next steps, key takeaways |
Quick Start Workshops
- Pick Workshop 1: Fundamentals and spend 30 minutes on it
- Come back for Workshop 2: Data Analysis & Visualization when you’re ready
- Finish with Workshop 3: Building with AI
Each one builds on the previous, but you can jump around if you want.
Table of contents
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