Software Engineering with Python

Description

Anyone can write a Python script that works once. Software that a team can read, change and trust a year later is a different craft, and it is the one that employers actually pay for. This course teaches that craft on your own machine, with the same tools that professional teams use: a linter and formatter, a type checker, a test runner, a debugger, packaging, Git and an automated pipeline.

You already write working Python. Here you learn to make it clear, checked and changeable: names, functions and documentation that explain themselves, types that catch mistakes before the code runs, classes and dataclasses that keep rules in one place, interfaces that let you replace a file with a fake in a test, and tests that can really fail.

What changes in your work

• Code: you write readable, typed and documented code, and you improve messy code in small, safe steps that a test suite keeps honest.
• Design: you model problems with classes, dataclasses, composition and protocols, and you keep the rules of a system in one place instead of scattered across functions.
• Quality: you test with pytest, fixtures, parametrization and mocks, you measure coverage and then check whether your tests can fail, and you debug with tracebacks, the debugger and useful logs.
• Teamwork: you manage environments and dependencies, structure and package a project, work with Git branches and merges, review changes, and let a pipeline run the checks on every push.

How it is taught

Each lesson starts from a realistic problem in a small bookshop, explains the idea with its precise definition, a diagram and a worked example, and then proves it by running real tools: the linter, the type checker, pytest, coverage, Git and the packaging tools run inside the lessons, and their real output is shown. Every lesson closes with exercises with hints and a short summary.

You work in your own editor and terminal in a virtual environment. Nothing needs an account or the internet: continuous integration is shown as a workflow file and run locally, step by step, so you know exactly what a server would do.

The project

You build the Inventory Library and Command-Line Tool for Maple Street Books: books, stock movements, rules that never let the stock go negative, JSON and CSV storage, and a command-line tool with clear errors and exit codes. You write the requirements and the design first, build in layers, test with fixtures and coverage, check your tests by breaking the code on purpose, package the project, review it, tag a release and write a README that is tested. A template repository with a test suite tells you which milestone you have finished, and a complete solution is there to compare with afterwards.

What is included

Lessons on clean code, types, refactoring, object-oriented design, testing, debugging, packaging, Git, review and continuous integration, sample inventory data and a set of exercise files, a guided project with a template repository and a solution repository with tests and a workflow file, and a short quiz at the end of every module drawn from a larger question bank.

Who it is for

If you can write Python but your code still feels like a pile of scripts, this course is the step that turns you into someone a team wants to work with. It suits self-taught programmers, analysts and engineers who want professional habits, and it is the natural bridge to building real services and backends. Do not wait until a big project forces you to learn these skills the hard way: enroll now, build the library, and finish with a released package and a tested, documented repository that you can show to anyone.