Understand the TDD cycle - You will learn to work deliberately in the Red–Green–Refactor loop, so you can build code in small steps, catch defects earlier, and keep your project easier to maintain over time.
Write Python tests - You will practice creating unit tests with unittest and pytest, so you can write your own tests with clear assertions, exception checks, and a structure ready for real project work.
Start with a test - You will go through your first TDD iterations from an empty task to a working solution, helping you define expected behavior before implementation and make coding decisions with more confidence.
Test functions and classes - You will learn how to test simple functions as well as class and method behavior, which will help you design code that is more predictable, modular, and easier to extend safely.
Organize test scenarios - You will use setUp, tearDown, and test parameterization to build repeatable scenarios faster, reduce duplication, and keep larger test suites easier to read and maintain.
Isolate external dependencies - You will learn mocking and stubbing for code that depends on APIs, databases, or files, so you can verify application logic without relying on unstable or slow external systems.
Avoid common pitfalls - You will see when to choose unit tests and when integration tests make more sense, how to avoid overengineering, and how to keep tests useful instead of turning them into overhead.
Apply TDD in a project - While building a simple application, you will practice the full TDD workflow in context, making it much easier to transfer this approach into your own everyday development tasks.
Test-Driven Development is placed in contrast with a traditional code-first approach to show how it changes the development process. The Red-Green-Refactor cycle is explained alongside the main reasons for using TDD and the misconceptions that often distort its purpose.
Python testing starts with the mechanics of building and running simple automated checks. The section compares unittest with pytest, explains the structure of a basic unit test, and works through common assertions such as assertEqual, assertTrue, and assertRaises.
Writing a failing test before implementation sets the pace for the core TDD workflow. Participants practice adding only the minimal code needed to pass, then refactoring after success and repeating the cycle as functionality expands into larger, coherent increments.
From small functions to object behavior, test design is adapted to the structure of Python code. The training shows a bottom-up approach to function testing, methods for validating classes and methods, and the use of setUp and tearDown to prepare and clean test environments.
When code interacts with APIs, databases, or files, isolation techniques become essential for reliable automated tests. This part introduces mocking and stubbing with unittest.mock, test parameterization in pytest, and ways to structure larger suites so they remain manageable over time.
Good TDD depends not only on writing tests early, but on choosing the right level of verification for each change. Attention is given to balancing unit and integration tests, keeping tests readable as documentation, and preventing overengineered or misleading test designs.
Deciding between unit and integration tests is addressed in the context of maintainable Python code and realistic project scope. The section also covers tests as living documentation, common TDD mistakes, and ways to avoid unnecessary complexity in the test layer.
A simple application such as a task manager or mini API becomes the vehicle for applying TDD across a full development flow. Participants move feature by feature, writing tests first, evolving code incrementally, and using the test suite to refactor safely as the project grows.
Python basics - You should be comfortable with basic Python syntax, including functions, conditionals, loops, and working with data, so you can follow examples and write your own code.
Working with classes - You should understand what classes, objects, and methods are in Python, because during the training you will test object behavior and build simple code structures.
Running Python code - You should be able to run a Python script from the terminal or your development environment, so you can execute tests smoothly and review each iteration as you go.
Project basics - It will help if you already know how to read simple code and move around a project structure, because you will work with test files and code developed step by step.
