Running examples

This course uses two running examples.

One is the main project in the lessons. The other is the default homework project. They are separate on purpose: the lesson project keeps the core path clear, and the homework project makes you reuse the same Python ideas in a different context.

Course materials: Podcast Aggregator

In the lessons, we build a Podcast Aggregator.

At the beginning, it is a small Python program with hardcoded podcast data. Later, it grows into a larger application with real data, better code organization, analysis, tests, classes, an API, a database, and a dashboard.

The first version looks like this:

Browse, filter, or get stats.

  1. Browse all episodes
  2. Filter by podcast
  3. Show stats
  4. Quit

Why this works well as the main course example:

• it uses record-based data, which matches real Python work
• it naturally needs lists, dicts, loops, filtering, files, APIs, and pandas
• it is not tied to one narrow domain
• it can grow across many modules without changing the core idea

You can find the complete working code for each module - notebooks and shared data - in the companion repo: AI-Shipping-Labs/python-course-code. Each module's lessons also link to the matching notebook so you can run the final program at any point.

Homework: Open Source Project Tracker

In homework, the default running project is an Open Source Project Tracker.

You keep a small collection of open source projects and turn it into a more useful tool over time.

At the beginning, it can store fields like:

• project name
• owner or organization
• language
• topic
• stars

The first version might let you:

• browse all projects
• filter by language or topic
• show a small stats view

Later, this project can grow too:

• module 3: search and string cleanup
• module 4: functions and modules
• module 5: JSON, CSV, and GitHub API data
• module 6: pandas analysis
• module 7: error handling and tests
• module 8: classes and validation
• module 9: API, database, and UI

Why use two running examples

One project would be simpler, but two projects are better for learning.

The lesson project gives you one clear reference path.

The homework project forces you to transfer the same Python ideas into a different domain:

• lesson project: follow the material closely
• homework project: repeat the pattern without copying the same data
• result: better retention and less fake confidence