Why Git Matters in Data Science

Unlike pure software projects, data science workflows deal with:

  • Code (scripts, notebooks)
  • Data (datasets, feature files, models)
  • Results (graphs, metrics, reports)

Without proper versioning, experiments get lost, collaborators overwrite each other’s work, and reproducibility becomes nearly impossible.

Git solves this by:

  • Version control: Track every experiment step.
  • Collaboration: Branches & pull requests help teams work without conflicts.
  • Experimentation: Keep multiple approaches without losing progress.
  • Reproducibility: Roll back to a specific commit to replicate results.

🔹 Best Practices for DS Git Workflows

1. Organize Repo Structure

project/
├── data/              # raw or small sample data (avoid large files)
├── notebooks/         # exploratory notebooks
├── src/               # production-ready scripts
├── models/            # saved models (use git-lfs)
├── reports/           # markdown, pdfs, dashboards
└── requirements.txt   # dependencies

👉 Keep raw datasets out of Git (use DVC or S3). Use sample datasets for debugging.

2. Branching Strategy

  • main → production-ready code
  • develop → ongoing experiments
  • feature/experiment-name → for new ideas (e.g., feature/xgboost-tuning)

This way, experiments don’t break production pipelines.

3. Tracking Experiments

  • Commit notebooks with clear messages:
  • ✅ feat: try logistic regression with standard scaling
  • ❌ update notebook
  • Save metrics & configs with the code:
  • YAML/JSON files to store hyperparameters
  • Results logged in MLflow or Weights & Biases

4. Git + DVC (Data Version Control)

Git tracks code, but datasets/models are too large.

DVC helps by:

  • Storing data pointers inside Git
  • Keeping actual files in S3/Google Drive/Azure
  • Reproducing experiments with dvc repro

🔹 Example Workflow

# clone repo
git clone git@github.com:username/project.git

# create new experiment branch
git checkout -b feature/xgboost-tuning

# edit notebook, commit results
git add notebooks/xgb_experiment.ipynb
git commit -m "experiment: tuned XGBoost with depth=6"

# push to remote
git push origin feature/xgboost-tuning

Then open a Pull Request → team reviews → merge into develop.