GITHUB DATA SCIENCE PORTFOLIO BUILDER

Meet Pretesh, get oriented with the course structure, and see how your labs, assignments and final project build toward one recruiter-ready GitHub portfolio.

• Instructor introduction
• Course structure & outcomes
• Assignments & final project overview

Set up a professional development environment and learn why data scientists and recruiters rely on GitHub, then turn your profile into a page that presents your skills clearly.

• Git fundamentals & version control
• Anatomy of strong data science repositories
• Common portfolio mistakes
• GitHub profile optimization & LinkedIn alignment
• Live lab: Git, VS Code & Python virtual environment setup
• Live lab: Drafting your GitHub profile README

Choose a portfolio-worthy project and scope it like a real business problem, with defined stakeholders, success metrics and a plan you'll follow for the rest of the course.

• Portfolio-worthy project selection
• Business-focused problem framing
• Success metrics
• Project planning
• Live lab: Writing your project charter

Structure your repository the way data teams do and plan your work with GitHub Issues and milestones, so every task in your project has a clear place to live.

• Repository structures (src/, notebooks/, data/, docs/)
• GitHub Issues
• Milestones & project boards
• Live lab: Building your repository structure & milestone board

Learn how teams work on shared code through feature branches and pull requests, then merge your first PR and get comfortable resolving the merge conflicts that come with it.

• Main & feature branches
• Pull requests
• Merge conflicts
• Code review fundamentals
• Live lab: Creating & merging your first pull request

Bring your dataset into the project securely and run hypothesis-driven EDA that explains the "why" behind each chart and ends on clear business opportunities.

• Data sourcing & data security
• .gitignore for datasets & credentials
• Hypothesis-driven exploration
• Visualization best practices
• Business storytelling
• Live lab: Building a narrative-driven EDA notebook

Build your primary baseline model, evaluate it with a train-validation-test split, and log every experiment in a README tracker so your modeling decisions are easy to follow.

• Baseline models
• Evaluation strategies
• Train-validation-test splits
• Experiment tracking
• Live lab: Building & tracking your baseline model

Move from messy notebooks to modular Python scripts that separate data processing, training and evaluation, giving your codebase the structure engineering teams expect.

• Notebook-to-script refactoring
• Modular code with preprocess.py, train.py & evaluate.py
• Live lab: Refactoring your analysis into the src folder

Assignment #1: Git Fundamentals & Project Planning Quiz
A 15-question quiz on Git fundamentals, branching, pull requests, repository architecture and project planning.

Make your project run on anyone's machine by managing paths, parameters and secrets in config files and capturing every dependency your code needs.

• Config files & environment variables
• Paths, parameters & secrets
• requirements.txt & environment.yml
• Setup instructions in README.md
• Live lab: Creating your .env file & reproducible environment

Assignment #2: Peer Repository Reproducibility Audit
Clone a partner's repository, set it up from their README and run their code, then grade its reproducibility, readability, quality and structure with a rubric.

Discover why testing matters in data science and write unit tests, validation checks and assertions that protect your preprocessing, feature engineering and modeling scripts.

• Why testing matters
• Unit tests
• Validation checks & assertions
• Live lab: Building a test suite for your scripts

Replace print statements with production-ready logging and pick up the debugging and error handling habits that keep data science code dependable as a project grows.

• Logging basics
• Debugging workflows
• Error handling basics
• Live lab: Adding production-ready logging

Automate your quality checks with GitHub Actions, writing workflow files that run your tests and validate your code every time you push a change.

• CI/CD with GitHub Actions
• Automated testing
• Workflow files
• Build validation
• Live lab: Setting up GitHub Actions to run tests

Write a README that walks hiring managers and teammates from the business problem through your data, approach, results and recommendations in clear, professional language.

• Anatomy of a strong project README
• Business problem & data
• Methodology & results
• Recommendations
• Live lab: Drafting your README content

Assignment #3: Software Engineering Fundamentals Quiz
A 15-question quiz on unit testing, reproducibility, CI/CD with GitHub Actions, documentation and configuration management.

Turn your README into a visual project landing page with embedded charts and architecture diagrams, then clean up your commit history and folders for a recruiter-ready repo.

• Visual storytelling with images, charts & tables
• Solution architecture diagrams
• README design considerations
• Commit history cleanup
• Final folder structure
• Live lab: Embedding charts & organizing final outputs

Practice reviewing technical repositories like a senior teammate: clone a peer's project, follow their setup and give constructive feedback on readability and reproducibility.

• Reading technical repositories
• Constructive code feedback
• Readability & reproducibility checks
• Workshop: Paired repository reviews with feedback rubrics

Apply your peer's feedback through pull requests, then make your project discoverable and properly licensed with metadata, topic tags and a clear repository description.

• Comments, change requests & approvals
• Implementing peer suggestions
• LICENSE & CONTRIBUTING.md
• Repository metadata & GitHub topics
• Live lab: Adding a license, description & topic tags

Assignment #4: Collaborative Pull Request Implementation
Implement your Assignment #2 feedback in your partner's repository through branches and pull requests, then document the full review and merge process.

Convert your project into career assets, from resume bullet points and STAR stories to a LinkedIn post that sends recruiters straight back to your GitHub portfolio.

• Resume bullet points
• STAR stories
• LinkedIn features
• Live lab: Updating your resume & drafting a project post

Assignment #5: Team Collaboration & Project Discoverability Quiz
A 10-question quiz on team collaboration, GitHub branching, peer reviewing and project discoverability.

Present a 3-minute walkthrough of your repository covering the problem, approach, results and lessons learned, and leave with a plan for maintaining your portfolio.

• Presenting technical work
• Portfolio maintenance & next steps
• Live lab: Final project walkthrough presentation