Python for Testing Track

Python for Testing 101 (PT101)

Understand the fundamentals of software testing, learn how different testing techniques fit into the development process, and prepare for the PCET™ – Certified Entry-Level Tester with Python certification.

Course Details

Beginner +

4-5 Weeks

English

Free (Core) & Paid ( Pro)

5750+ people already enrolled

Core

4 modules

40+ lessons

hands-on labs,

interactive exercises,

real-life data analysis scenarios, quizzes, and tests,

a Diploma of Course Completion,

a 20% discount code for the PCET™ certification exam.

Free

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Pro

4 modules

40+ lessons

hands-on labs,

interactive exercises,

real-life data analysis scenarios, quizzes, and tests,

a Diploma of Course Completion,

a 20% discount code for the PCET™ certification exam.

$49

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About the course

This course introduces the core concepts of software testing, covering key testing methods, testing levels, and software quality best practices. You’ll learn how testing helps identify defects, improve reliability, and manage software releases effectively. The course explores manual and automated testing, different testing techniques, and how to structure testing efforts for better results.

The course covers topics such as error handling, debugging techniques, software testing levels (unit, integration, system, and acceptance), test documentation, and test coverage. You’ll also learn about industry best practices like the test pyramid, the DRY and KISS principles, and refactoring techniques to improve code quality. Additionally, the course introduces concepts from the ISTQB® methodology, including the 7 testing principles, test case design techniques, and the structured approach to testing in different software development models.

By completing this course, you’ll build a strong foundation in software testing and be prepared for the PCET™ – Certified Entry-Level Tester with Python certification.

This course is structured to help you learn both conceptually and practically. As you move through the modules, you will build knowledge, apply it in hands-on activities, and check your understanding along the way. Here’s what you can expect:

Python for Testing 101 (PT101) is designed for learners who want to build a solid foundation in software testing. It is particularly suited for:

  • Aspiring programmers and beginners in software testing who want to learn the basics of testing and software quality assurance for personal or professional growth.

  • Learners preparing for entry-level roles in software testing or quality assurance, such as software tester, QA engineer, or test analyst.

  • Industry professionals familiar with other programming languages and tools who want to understand software testing principles and how Python can be used for testing purposes.

  • Aspiring programmers and professionals looking to expand their skills in software testing, test automation, security, and software quality assurance as part of their career development.

  • Team leaders, product managers, and project managers who want to understand software testing processes to improve collaboration with development and QA teams.

Prerequisites

This course is designed for learners who:

No prior knowledge of object-oriented programming (OOP) is required. This course is designed as a smooth transition from Python Essentials 1 to software testing, making it accessible for learners with basic Python experience.


Associated Certification

Prior Experience Recommended:

PCEP Badge

Entry

PCET Badge

PCET-30-0x

Certified Entry-Level Tester with Python

Associate

PCAT Badge

PCAT-31-0x

Certified Associate Tester with Python



Course Overwiev

Here’s an overview of the course structure:

Understanding the purpose of testing, key terminology (errors, defects, failures), testing principles, and the role of testing in the software development lifecycle.

Exploring testing types and levels, the test pyramid, unit and integration testing, test doubles, and the overall testing process and documentation.

Working with static vs. dynamic testing, improving code quality using PEP 8 and linters, understanding test coverage, and applying refactoring principles.

Using debugging tools, writing assertions in Python, logging results, and applying white-box, black-box, and experience-based testing techniques.



Go Pro for Hands-on Practice & Course Diploma

The Pro version of the course includes a Diploma of Course Completion and gives you access to 40+ hands-on labs, interactive exercises, and quizzes designed to reinforce your learning and help you apply concepts in real-world scenarios. It also supports your preparation for the PCET™ certification exam by providing practical experience aligned with exam objectives.

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Python code example

Here’s a real snippet from the course so you can see how we learn in practice. Here you can see a functional test, where we simulate a user attempting to log into a system by providing a username and password::

Source Code:

   
test_funcional.py
def test_login():
    # Simulate entering username and password
    username = "test_user"
    password = "test_pass"
    
    # Call the login function and check the result
    result = login(username, password)
    
    # Assert the login was successful
    assert result == "Login Successful", "Login Failed"


The test checks if the login() function correctly returns "Login Successful" when provided with valid credentials.



Labs in Pro course

Learning to code happens by doing, not just reading. In the course, you’ll work through hands-on labs that turn concepts into real Python skills. Below are just a few examples of what you’ll build and solve.

Lab

Build a Balanced Test Pyramid

In this lab, you’ll design a balanced testing strategy by classifying tests into Unit, Integration, and End-to-End levels. You’ll learn how the Test Pyramid helps teams build faster and more reliable software.

Lab

Refactoring Loop Simulator

Step into the role of a junior QA tester at a travel app and help structure its automated testing strategy. You’ll decide where each test belongs in the Test Pyramid to ensure speed, coverage, and stability.

Lab

Using Assertions for Data Processing Validation

Assertions become your safety net while verifying a function that filters and transforms data. You’ll confirm correctness across multiple inputs, including edge cases that often break logic.

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