Click the green "Use this template" button at the top of the page, then choose "Create a new repository".

This will create your own copy of this project, which you can modify freely — no need to fork!

• Docker and Docker Compose installed (Docker files remain unchanged)
• VS Code with Dev Containers extension (optional)

# Make start.sh executable (if needed) chmod +x start.sh # Build and run the container ./start.sh

Alternatively:

docker-compose up --build -d

docker-compose ps

Confirm the container is up and port 8888 is mapped.

In the VS Code terminal (inside the container), run:

python scripts/generate_beta_binomial_data.py

You should see the expected output (for example, confirmation message).

• Open notebooks/1_Intro_Beta_Binomial.ipynb in VS Code.

• In VS Code, install the following extensions: Docker, Dev Containers, Python, Jupyter.

• VS Code will prompt you to select a kernel. Choose the Python kernel that is specified in the Dockerfile. The libraries that have been listed in the requirements.txt and installed in the docker will be run automatically.

• Now you can run and edit cells directly in VS Code.

docker-compose down

. ├── docs/ # Educational docs │ ├── Beta-Binomial-Classification.md │ └── README.md ├── data/ # Example and synthetic grouped counts │ ├── beta_binomial_examples.csv │ ├── beta_binomial_synthetic.csv │ └── README.md ├── figures/ # Generated visuals (mastery map) │ └── README.md ├── notebooks/ # Hands-on learning notebooks │ ├── 1_Intro_Beta_Binomial.ipynb │ ├── 2_Beta_Binomial_Classifier.ipynb │ ├── 3_Visualization_Master_Map.ipynb │ └── README.md ├── scripts/ # Minimal, readable scripts │ ├── generate_beta_binomial_data.py │ ├── beta_binomial_classifier.py │ ├── plot_mastery_map.py │ └── README.md ├── requirements.txt ├── Dockerfile # untouched ├── docker-compose.yml # untouched └── start.sh # untouched 

1. Read docs/Beta-Binomial-Classification.md for the concepts
2. Open notebooks/1_Intro_Beta_Binomial.ipynb to see posterior updates
3. Run notebooks/2_Beta_Binomial_Classifier.ipynb to classify data
4. Visualize mastery with notebooks/3_Visualization_Master_Map.ipynb

• Posterior updates for Beta–Binomial (conjugacy)
• How to set priors and interpret uncertainty
• Mapping to mastery labels via credible bounds
• Visualizing attempts×successes mastery regions

• Media files in docs/ are placeholders and will be replaced later
• Docker setup is preserved as in the template