Postgraduate Certificate in Pytest for Data-Driven Testing in Python

This course is designed for data scientists, software developers, and quality assurance professionals looking to enhance their testing skills in Python. Participants will gain proficiency in using Pytest for data-driven testing, automating test cases, and improving test coverage. They will learn to write efficient, reusable test scripts and integrate Pytest with various frameworks and tools commonly used in Python development.

Students will leave the course with the ability to apply Pytest in real-world scenarios, ensuring high-quality software development. Practical hands-on exercises and case studies will help participants master the techniques needed for effective data-driven testing.

1. 1. Introduction to Pytest: Learners will understand the basics of Pytest, its installation, and configuration. They will gain the practical skill of writing simple test cases and learn how to use assertions effectively.
2. 2. Assert Statements and Fixtures: This module covers the use of assert statements for writing tests and the concept of fixtures for setting up and tearing down test environments. Learners will be able to create reusable fixtures and write more complex tests.
3. 3. Parametrization and Data-Driven Testing: Learners will study how to use parametrization to run the same test code with different inputs and outputs. They will learn to implement data-driven testing using CSV files, databases, and other data sources.
4. 4. Plugins and Extending Pytest: This module introduces various Pytest plugins and how to extend Pytest functionality. Learners will explore plugins for code coverage, parallel testing, and customizing test output.
5. 5. Test Organization and Structure: Learners will learn best practices for organizing test files and directories, and structuring tests for maintainability. They will gain skills in creating modular and well-organized test suites.
6. 6. Error Handling and Reporting: This module focuses on handling test errors and exceptions, and generating detailed test reports. Learners will be able to handle and report errors effectively to improve testing processes.
7. 7. Continuous Integration with Pytest: Learners will understand how to integrate Pytest with CI/CD pipelines. They will learn to set up CI/CD configurations and ensure automated testing as part of the development process.
8. 8. Advanced Test Strategies: This module covers advanced testing strategies such as mocking, stubbing, and dependency injection. Learners will be able to write more robust and isolated tests.
9. 9. Performance Testing with Pytest: Learners will explore techniques for performance testing using Pytest. They will learn how to measure and optimize the performance of their applications.
10. 10. Best Practices and Case Studies: The final module provides real-world case studies and best practices for implementing Pytest in large-scale projects. Learners will gain insights into best practices for data-driven testing in Python.