Git for Data Scientists · Lesson 1 of 1

Git for Data Scientists

Use version control to collaborate on code, notebooks, experiments, and project documentation.

Lesson

1. Introduction

A data-science project changes as assumptions, data preparation, and evaluation evolve. Git records source history so teammates can review those changes and reproduce an earlier analysis.

2. Core concepts

Working tree changes are staged before a commit. Branches isolate work, and merges combine histories; conflicts require understanding both edits. Remote repositories share code, not necessarily data, and sensitive artifacts should remain outside Git.

3. Topics covered in this module

Git and GitHub concepts, repository setup, status, diff, and commits
Branch workflow, merges, conflicts, pull requests, and reviews
Remotes, clone, fetch, pull, push, stash, and GitHub Desktop
Notebook diff limitations, .gitignore, secrets, and data versioning choices

4. Syntax and how to read it

git switch -c improve-feature-checks
git status
git diff
git add src/analysis.py
git commit -m "Validate feature input"

The sequence creates an isolated branch, inspects the work, stages one file, and records a commit. Add only the files that belong to the change.

5. Practical workflow

Create a branch for a coherent task, inspect changes, stage only intended files, and commit with an informative message. Share through a reviewed pull request and ensure the environment instructions are current.

6. Common mistakes

Committing credentials, private datasets, or large outputs.
Assuming a file removed in a new commit has disappeared from repository history.

7. Best practices

Use small commits and review staged diffs before pushing.
Document data access and environment setup without checking in restricted data.

8. Practice task

A secret was committed and then removed in a later commit. Is it safe to assume the secret is gone?

9. One possible solution

No. It may remain in repository history and remote copies. Revoke/rotate it immediately and follow the organization’s history-removal and incident process.

10. Summary

Use version control to collaborate on code, notebooks, experiments, and project documentation. Use small commits and review staged diffs before pushing. Document data access and environment setup without checking in restricted data.

11. What comes next

The next module, Using Google Colab for Data Science, builds on this foundation and applies it to a new part of the analytics workflow.

Visual guide

A reliable Git for Data Scientists workflow

Step 01 Frame
Define the question, input scope, and intended output.
Step 02 Work
Apply the method and validate assumptions as you go.
Step 03 Check
Compare results with known cases and communicate limitations.

Code examples

Ignore generated project files

TEXT

.env
.venv/
.ipynb_checkpoints/
models/*.joblib

Expected output

Keeps secrets, environments, notebook checkpoints, and generated model artifacts out of Git.

Inspect what will be committed

BASH

git status --short\ngit diff --cached