A template repository for the 2026 MS-DS Bootcamp. Use it to practice the full GitHub Flow — issues → branch → commit → pull request → review → merge — on a tiny data science task, including creating and resolving a real merge conflict.
The file you'll work in is shape_attributes.py: it calculates the area, circumference, and diameter of a circle and a sphere of radius r.
One team member creates a private repo from this template (Use this template → Create a new repository), then adds the second team member as a collaborator (Settings → Collaborators).
- Team Member 1 opens Issue #1 — "Define function for area of a sphere" and assigns it to Team Member 2.
- Team Member 1 opens Issue #2 — "Define function for area of a circle" and assigns it to themselves.
Now each of you complete your assigned issue:
- Clone the repo:
git clone <URL> - Create a branch named for your issue:
git checkout -b <branch_name> - In a text editor, add your function at line 11 of
shape_attributes.py(under### Area), then save. - Commit your change:
git commit -am "your message here" - Push your branch:
git push -u origin <branch_name> - Open a pull request. In the description, write
Closes #<your issue number>so the issue closes automatically on merge.
Then:
- Team Member 1 reviews Team Member 2's pull request → Merge → Delete branch → close. Now pull up the Issues — is that issue closed?
- Team Member 2 reviews Team Member 1's pull request → UH OH, a conflict! You both edited the same line. Team Member 2 resolves it right on GitHub using the web editor (keep both functions, delete the
<<<</====/>>>>markers) → Merge → Delete branch → close.
That's the whole loop. main now has both functions, both PRs are merged, and both issues are closed.
Run the exact same flow again for the Circumference functions — add them under ### Circumference instead of ### Area. Same steps, different section.
- Vanderbilt data classification — which AI/compute you can use for which data sensitivity level: https://www.vanderbilt.edu/cybersecurity/guidelines/data-classification/
- GitHub Flow overview: https://docs.github.com/en/get-started/using-github/github-flow
- Connecting to GitHub with SSH (keys + setup): https://docs.github.com/en/authentication/connecting-to-github-with-ssh
- GitHub CLI (
gh): https://cli.github.com - Claude Code: https://code.claude.com/docs
- LM Studio — run open-source LLMs locally: https://lmstudio.ai
- Hugging Face — models: https://huggingface.co