Data Analysis Track
This track covers data analysis workflows using Python or R with Claude Code assistance. You'll learn to clean data, create visualizations, and build analysis pipelines.
What Makes This Different
This isn't "learn pandas methods" or "memorize ggplot syntax." You'll work on real data problems with Claude as your coding partner.
Pro Tip
Claude writes the code. You understand the data. That's the workflow. You describe what you need, Claude generates it, and you learn by doing.
Python vs R: Pick Your Tool
Note
Can't decide? Start with Python. It's more versatile, and the skills transfer to R easily. You can always learn R later for specialized statistical work.
What You'll Learn
By the End, You'll Be Able To:
- Set up reproducible projects — No more "it worked on my machine"
- Clean messy data — Handle missing values, inconsistent formats, duplicates
- Transform data — Reshape, aggregate, join datasets
- Create visualizations — Charts that actually communicate insights
- Build models — Regression, classification, clustering basics
- Generate reports — Share findings with stakeholders
The Workflow (Both Tracks)
Every data project follows the same pattern:
How Claude Fits In
At each step, Claude helps you:
| Step | What Claude Does |
|---|---|
| Get data | Write import code, handle file formats |
| Clean | Generate cleaning code, explain why |
| Explore | Suggest analyses, write aggregations |
| Visualize | Create charts, customize styling |
| Model | Build models, explain results |
| Document | Write reports, explain findings |
Sample Projects
These are the kinds of problems you'll solve:
Python Track Examples
- Sales Analysis: Clean 12 months of messy exports, find seasonal patterns
- Customer Segmentation: Cluster customers by behavior, identify high-value groups
- Churn Prediction: Build a model to predict which users will leave
R Track Examples
- Survey Analysis: Clean Likert-scale data, run statistical tests
- Time Series: Forecast next quarter's revenue
- Research Report: Publication-ready figures and tables
Time Commitment
| Track | Duration | Prerequisites |
|---|---|---|
| Python | 4-6 hours | Start Here |
| R | 4-6 hours | Start Here |
Pro Tip
Work in chunks. Each section is 30-60 minutes. You don't need to finish in one sitting.
What You'll Build
By the end, you'll have:
- A complete analysis project you can show employers
- Reusable scripts for common tasks (cleaning, plotting, etc.)
- Git history showing your analytical process
- Confidence to tackle new data problems