🎨 Recreating the Scurvy DAGs with Beautiful Visualizations
This repository contains a data science challenge focused on mastering the DAFT (Directed Acyclic Factor Graphs Toolkit) package to create professional-quality probabilistic graphical models.
The challenge involves recreating three historical Directed Acyclic Graphs (DAGs) that tell the fascinating story of how we lost and rediscovered the cure to scurvy:
- 1747 Understanding: Lemons prevent scurvy (correct but incomplete)
- Misguided Belief: Acid kills bacteria that causes scurvy (wrong)
- 1928 Understanding: Vitamin C prevents scurvy (the real mechanism)
View the complete challenge and interactive content:
👉 https://flyaflya.github.io/daftChallenge/
The GitHub Pages website contains the full interactive challenge with:
- Detailed historical context about scurvy
- Step-by-step DAFT programming tutorials
- Interactive code examples
- Professional styling demonstrations
- Complete grading rubric and submission checklist
- Fork this repository to your GitHub account
- Clone locally using Cursor or VS Code
- Install DAFT:
pip install 'daft-pgm' - Complete the challenge by recreating the three DAGs
- Enable GitHub Pages to publish your solution
- Master the DAFT package for creating probabilistic graphical models
- Learn professional visualization techniques
- Understand causal relationships and data generating processes
- Create publication-quality scientific figures
- Recreate all three scurvy DAGs using DAFT programming
- Apply professional styling and customization
- Demonstrate understanding of causal modeling concepts
- Create visually appealing, publication-ready visualizations
This challenge is designed to push boundaries with AI partnership, allowing you to accomplish more than you thought possible while learning essential data science visualization skills.