Data Analysis Track

Master data analysis workflows using Python or R with Claude as your coding partner.

What You'll Learn

• Set up reproducible data projects
• Clean and transform data efficiently
• Create visualizations and models
• Version control your analyses with Git
• Use Claude to write, debug, and explain code

Prerequisites

• Completed Start Here setup
• Basic familiarity with either Python or R (or willingness to learn!)

Choose Your Language

Track Outline

Both tracks follow the same structure:

1. Project Setup

• Create a new analysis project
• Set up virtual environment (Python) or renv (R)
• Configure VS Code for your language
• Use CLAUDE.md to guide your session

2. Data Cleaning

• Import data from CSV/Excel
• Handle missing values
• Transform and reshape data
• Use Claude to generate cleaning code

3. Exploratory Analysis

• Summary statistics
• Grouping and aggregation
• Data visualization basics
• Interactive exploration with Claude

4. Modeling & Insights

• Build predictive models
• Evaluate model performance
• Create compelling visualizations
• Document findings

5. Reproducible Output

• Generate reports (Jupyter/RMarkdown)
• Save scripts and notebooks
• Commit to Git with meaningful messages
• Share your analysis

Sample Projects

Python Track

• Sales data analysis with pandas
• Customer segmentation with scikit-learn
• Interactive dashboard with Streamlit

R Track

• Survey data analysis with tidyverse
• Time series forecasting
• Statistical report with RMarkdown

Time Commitment

• Python Track: ~4-6 hours
• R Track: ~4-6 hours (Coming in V1.5)

What You'll Build

By the end of this track, you'll have:

• A complete, reproducible data analysis project
• Scripts/notebooks you can reuse
• Confidence using Claude for data work
• Git history showing your analytical process

Ready to Start?

Choose your language above and dive in!