Learn how to test real-world Python applications with pytest, from unit tests to full CI/CD automation

What you'll learn

• How to use the pytest testing framework as a tool for making code maintainable.
  
• How to apply testing in real-world projects, from complex legacy code to new projects with a test-driven foundation.
  
• Design a scalable testing system integrated into existing CI/CD pipelines, or set up initial pipelines for automated test execution.
  
• Execute a practical path from 0% to 100% test coverage.
  
• Implement tests for real-world applications without overthinking or getting stuck on where to start.
  

Requirements

• Basic Python skills are required - if you can implement functions and classes, you're good to go!
  
• NO prior experience with pytest or any testing framework is required at all
  

Description

Python is one of the most popular programming languages in the world - it's versatile, powerful, and used in everything from web applications to data pipelines and machine learning systems.

But there's one thing all serious Python projects need to stay healthy: good tests.

If you care about writing high-quality code, maintaining stability, and keeping your codebase easy to change over time, you need a solid testing practice.

That's where pytest comes in.

pytest is one of the most widely used Python testing frameworks - it is fast, flexible, and designed to make testing real-world code straightforward instead of painful.

This course is about pytest and practical Python testing - turning testing from a "nice to have" into a core tool for maintainable, stable code.

You'll learn how to use pytest in real-world Python projects - both when you're facing messy, complex legacy code that feels risky to touch, and when you are starting a new project and want a clean, test-driven foundation from day one. Instead of focusing on toy examples, we will look at patterns, techniques, and workflows that you can apply directly at work or in your own projects.

No advanced background is required:

Basic Python knowledge is enough to get value out of this course. Whether you are a software engineer, machine learning or data science practitioner, data engineer, backend/frontend developer, or startup founder, this course will help you write cleaner, more maintainable, and better-tested Python code.

We will start at the very beginning - writing and running your first tests with pytest - and build up to a scalable, professional testing setup that fits naturally into modern development workflows and CI/CD pipelines.

In detail, this course covers:

Installing & running pytest

• Setting up pytest in new and existing Python projects
  
• Understanding test discovery, naming conventions, and basic structure
  

Writing your first tests properly

• Using assertions effectively
  
• Testing functions, classes, and modules
  
• Avoiding typical mistakes when starting with testing
  

Working with pytest fixtures

• Creating reusable fixtures for data, configuration, and environment setup
  
• Understanding fixture scopes (function, class, module, session) and when to use which
  
• Sharing fixtures across tests and modules
  

Parametrization & powerful test patterns

• Writing one test that covers multiple inputs, edge cases, and scenarios
  
• Structuring and naming tests for clarity and long-term maintainability
  
• Using parametrization to quickly increase coverage without duplicating code
  

Organizing a scalable test suite

• Structuring tests for small scripts and large applications
  
• Grouping, naming, and tagging tests so teams can work efficiently
  
• Using markers to manage slow, flaky, integration, or smoke tests
  

Testing real-world Python applications

• Testing business logic, services, and data-processing code
  
• Strategies for testing code that works with files, APIs, databases, or external services
  
• Reducing flakiness and isolating side effects
  

Mocking, patching & test doubles

• When to mock - and when not to
  
• Using monkeypatch and pytest-mock to replace or isolate dependencies
  
• Writing code that's easier to test without unnecessary complexity
  

From 0% to 100% test coverage (practically)

• Using coverage tools (like pytest-cov) to see what's actually tested
  
• Prioritizing what to test first in large, untested codebases
  
• Designing a realistic path from little or no coverage to high, meaningful coverage
  

Working effectively with legacy code

• Adding tests around existing, fragile code without breaking it
  
• Building a "safety net" before making changes or refactors
  
• Gradually decoupling and improving tightly coupled code using tests as a guide
  

Test-first and test-driven approaches

• When TDD is helpful - and when it's overkill
  
• Using tests to drive the design of new features
  
• Using tests to gain confidence and move faster, not slower
  

Integrating pytest into CI/CD pipelines

• Configuring workflows to run tests automatically on every push, pull request, or release
  
• Speeding up the test suite by parallelizing tests with pytest-xdist
  
• Handling slow or flaky tests in CI and shaping a testing strategy that matches your team's release cadence and risk tolerance
  

Realistic examples & complete workflows

• Building up tests for realistic Python modules and mini-projects
  
• Seeing how to go from no tests at all to a useful, automated test suite
  
• Applying the same approach to your own personal projects or company codebase
  

Throughout the course, you won't just learn how pytest works - you'll learn how to think about testing as a practical tool:

• How to start when your project has 0% test coverage
  
• How to begin testing complex or legacy code that feels intimidating
  
• How to design a testing setup that scales with your team and project
  
• How to integrate tests into CI/CD so they run automatically and reliably
  

By the end of this course, you'll have a clear, hands-on understanding of pytest and Python testing. You'll be able to:

• Use pytest as a tool to keep your codebase stable and maintainable
  
• Implement tests for real-world projects without overthinking where to begin
  
• Design and maintain a scalable testing system integrated into CI/CD
  
• Follow a practical path from 0% to high test coverage that supports refactoring and new feature delivery
  

After completing the course, you won't just "know pytest" - you'll be able to apply it confidently in your daily work, across teams, and in any Python project where clean, reliable, and maintainable code matters.

Who this course is for:

• Junior-, Mid-, or Senior-level Developers working with Python who want to deepen their knowledge and skills to advance their careers and apply these improvements within their organizations.
  
• Development Teams and Tech Leads at companies of any size will also benefit, as the course covers pytest - a mature and scalable testing framework that can be integrated into existing codebases to support legacy code refactoring and accelerate new features development.
  
• Anyone who wants to level up their Python skills and learn advanced concepts like testing.
  
• It is valuable for a wide range of developers, including software engineers, machine learning and data science practitioners, data analysts, data engineers, and both front-end and back-end web developers.
  
• The course is also well-suited for individuals working on any personal projects or building early-stage startups who aim to write clean, maintainable code.
  

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