Capstone Projects — Python Course

What you'll build

Knowledge becomes skill only when you build. These six projects grow with you — from a first command-line tool to a production-ready, packaged application — and together they exercise everything in this course. Ship them to a public repo and you have a portfolio, not just a certificate.

Treat each project the professional way: start from a small working version, commit often, write a few tests, add a README, and only then reach for the stretch goals. Done and shipped beats perfect and unfinished.

Key idea

You don't have to build all six. Pick the track that matches your goals — data, web, or tooling — and go deep. One polished, well-documented project impresses more than five half-finished ones.

1 · Beginner CLI application

A command-line tool you'd actually use — a to-do list, a unit converter, a password generator, or a expense tracker that saves to a JSON file.

Build it: parse arguments with argparse, add/list/remove items, persist to a JSON or CSV file.
Skills: functions, control flow, files & I/O, exceptions, the standard library.
Stretch: add a --help-rich subcommand interface and a handful of pytest tests.

2 · Object-oriented application

A small simulation or game that models a domain with classes — a bank with accounts and transactions, a library catalog, or a text adventure.

Build it: design 3–5 classes with clear responsibilities, use inheritance or composition, add __repr__ and properties.
Skills: OOP fundamentals, encapsulation, dunder methods, dataclasses, type hints.
Stretch: enforce invariants with validation and model fixed states with an Enum.

3 · Data analysis project

Take a real dataset (a public CSV, an export from an app you use) and answer a question with it — trends, comparisons, or a surprising correlation — presented as a notebook with charts.

Build it: load with pandas, clean the messy bits, group and aggregate, and visualize with Matplotlib.
Skills: NumPy, pandas, Matplotlib, comprehensions, functions, Jupyter.
Stretch: write up your findings in the notebook so a non-programmer can follow the story.

4 · Automation tool / web scraper

Automate a chore you do by hand — organizing downloads, renaming photos by date, or collecting prices and headlines from a site into a spreadsheet.

Build it: use pathlib/shutil for files or requests+BeautifulSoup for the web; output CSV or JSON.
Skills: automation, requests, scraping, generators, error handling.
Stretch: make it idempotent, add logging, and schedule it to run automatically.

Watch out

If your project scrapes, do it responsibly (Module 38): prefer an official API, honor robots.txtand terms of service, rate-limit your requests, and respect copyright and privacy.

5 · REST API / web application

A backend that stores and serves data — a URL shortener, a notes API, or a small task manager with real endpoints.

Build it: design REST resources, implement CRUD with FastAPI, persist to a database with SQLite/SQLAlchemy, validate input with type hints.
Skills: web development, HTTP/REST, databases, type hints, testing.
Stretch: add authentication, pagination, and automated API tests; explore the auto-generated /docs.

6 · Production-style packaged app

Take any earlier project and make it professional: fully packaged, tested, documented, and deployable — the kind of repo you'd proudly link on a résumé.

Build it: src layout + pyproject.toml, a console entry point, a test suite with coverage, type checking, and a Dockerfile.
Skills: packaging, testing, clean code, Git, CI/CD, concurrency or async where it fits.
Stretch: add a CI workflow that lints, type-checks, and tests on every push, and publish to (Test)PyPI.

Build your portfolio

Each finished project belongs in its own public Git repository with a clear README (what it does, how to run it, a screenshot or example), a requirements/pyproject file, and a permissive license. That collection — not any single tutorial — is what shows the world you can build.

Key takeaways

Start small and working, commit often, then iterate toward the stretch goals — shipped beats perfect.
Every project gets its own repo with a clear README, run instructions, and a license.
Write a few tests and add type hints — reviewers notice code that's built to last.
Pick the track that matches your goals (data, web, or tooling) and go deep on one polished project.
Depth and documentation impress more than a pile of half-finished demos.

Key idea

That's the whole journey — from your first print("Hello") to typed, tested, concurrent, packaged, deployed Python. You have the foundations to keep learning any corner of the ecosystem on your own. Now go build something you're proud of. 🐍

Congratulations — you've completed The Complete Python Course.