Introduction to Testing in Python
Learn to test Python code and data pipelines with pytest and unittest
Introduction to Testing in Python is a concise, practical course that teaches how to design and run tests in Python using pytest and unittest. It frames testing as a safeguard for code and data workflows, emphasizing unit testing, integration testing, fixtures, markers, and techniques for validating data pipelines before they reach production.
Through a mix of short conceptual lessons and hands‑on exercises with real datasets, learners practice writing assertions, organizing test suites, and applying fixtures and teardowns to prevent regressions. The course is geared toward practitioners who want measurable skills in test automation and test-driven validation of Python code and pipelines.
At a Glance
Introduction to Testing in Python is a focused DataCamp course taught by Alexander Levin with contributions from other instructors and collaborators.
It introduces Python testing basics and practical workflows using pytest and unittest, with emphasis on fixtures, markers, test organization, and testing data pipelines.
| Level | Intermediate |
| Rating | 4.8 out of 5 |
| Duration | 2+ hours |
| Languages | English |
| Certificate | Statement of Accomplishment |
| Access | Access while your DataCamp subscription is active |
| Course includes |
|
| Price | Subscription-based; included with DataCamp’s monthly or annual plans |
What This Course Teaches
The course frames outcomes as measurable competencies that prepare learners to design, implement, and evaluate tests across Python projects and data pipelines.
By the end, learners should be able to design and run tests with pytest and unittest, apply fixtures and teardowns, organize test suites with markers and CLI tooling, and validate data pipelines before production.
How the Course Is Structured
Introduction to Testing in Python is organized as a short, focused sequence of chapters that mix concise conceptual lessons with guided, hands‑on exercises using real datasets.
The course is presented as 4 modules and runs about 2+ hours in total.
Curriculum overview
01Creating Tests with pytest▾
Introduces what a test is and how to write and run pytest tests, with coverage of CLI usage and test markers for skipping or expected failures.
- First test suite
- pytest.raises
- Invoking pytest from CLI
- Applying test markers
- Failed tests with xfail
- Conditional skipping
02Pytest Fixtures▾
Covers the @pytest.fixture decorator and related tools to prepare test state, chain fixtures, and perform teardowns to avoid side effects.
- Introduction to fixtures
- Getting familiar with fixtures
- Data preparation with fixtures
- Chain fixtures and autouse
- Fixtures teardowns
03Basic Testing Types▾
Explains unit, feature, integration and performance testing, and how to design test cases and run pytest examples from the CLI.
- Unit testing with pytest
- Feature vs unit testing
- Integration testing examples
- Performance testing concepts
- Hands-on practice exercises
04Writing tests with unittest▾
Introduces the unittest framework, its assertion methods, CLI usage, fixtures, and applies these tools to practical examples such as testing data pipelines.
- Meeting the Unittest
- Factorial and prime-check examples
- Unittest CLI and options
- Fixtures in unittest
- Practical data‑pipeline tests
Audience & Requirements
Introduction to Testing in Python targets practitioners who already write Python code and need practical, test-driven methods to make that code more reliable.
It is best suited to Python developers, data engineers, and data scientists who want to apply pytest and unittest to validate functions and data pipelines before production.
- Python developers building applications or libraries who need unit and integration tests.
- Data engineers and data scientists responsible for data pipelines and ETL validation.
- Software engineers looking to adopt pytest and unittest workflows in existing projects.
- Learners upskilling toward roles that require test automation and CI-friendly test suites.
- Completion of introductory Python and software-engineering courses (e.g., Writing Functions in Python; Software Engineering Principles in Python).
- Comfort writing and reading Python functions and simple scripts.
- Basic familiarity with running commands in a terminal or using an IDE to run test commands.
Final Verdict
Introduction to Testing in Python is a practical, compact course that I recommend for intermediate Python developers, data engineers, and data scientists who need to add reliable test practices to their workflow.
Given its strong platform rating, the fact that it issues a formal Statement of Accomplishment, and that it is offered through a subscription model, the course represents particularly good value for learners who already use the platform or plan to take multiple short, skills‑focused courses.
LinkedIn Learning 
Pluralsight 