350+ Exercises - Python Programming Mega Pack - Unit Tests
What you'll learn
- solve over 350 exercises in Python and unit testing
- deal with real programming problems
- work with documentation
- guaranteed instructor support
Requirements
- Completion of all courses in the Python Developer learning path
- Basic knowledge of Python, unit testing and unittest framework
Description
This course is an in-depth, hands-on guide to unit testing in Python. This course is aimed at both beginners who want to solidify their knowledge of Python and experienced developers who want to enhance their testing skills.
With over 350 exercises, this course thoroughly covers the unittest module, one of Python's built-in libraries designed for testing. By working through these exercises, students will gain a practical understanding of unit testing in Python and the importance of test-driven development.
The course starts with the fundamentals of unit testing, including writing simple test cases and understanding the role of assertions. It then progressively moves into more complex areas such as setup and teardown methods, test suites, and mock objects. Throughout the course, students will learn to write robust and effective test cases, crucial for maintaining large codebases and ensuring software quality.
Each exercise is designed to reinforce the concepts learned in the course and provide practical experience. The course also includes solutions to all exercises, allowing students to compare their solutions and gain insights into different approaches.
By the end of this course, students will have a deep understanding of unit testing in Python, enabling them to write comprehensive test cases and enhance software quality in their future projects. This knowledge can lead to improved job prospects and performance in professional settings.
Unit Testing: Ensuring Code Quality One Unit at a Time
Unit testing is a software testing technique that focuses on verifying the functionality of individual components or functions in isolation. By writing and executing small, repeatable tests, developers can detect bugs early, ensure code reliability, and support future code changes with confidence. Unit testing is a fundamental practice in test-driven development and continuous integration workflows.
unittest: Built-in Testing for Reliable Python Code
The unittest framework is Python’s standard library for writing and running unit tests. It provides a structured approach to test automation with features like test case organization, test discovery, setup and teardown methods, and detailed reporting. By enabling developers to validate code functionality and catch regressions early, unittest plays a crucial role in ensuring code quality and maintainability.
Who this course is for:
- Aspiring Python Developers
- Software Engineers and Developers
- QA Engineers and Test Automation Professionals
- Computer Science Students
- Technical Interview Candidates
- Self-Taught Programmers and Career Changers
- Instructors and Educators
- Backend and API Developers
- Continuous Integration / DevOps Professionals
- Anyone Committed to Writing Clean, Testable Code
Instructor
EN
Python Developer/AI Enthusiast/Data Scientist/Stockbroker
Enthusiast of new technologies, particularly in the areas of artificial intelligence, the Python language, big data and cloud solutions. Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization. Master's degree graduate in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics. Since 2015, a licensed Securities Broker with the right to provide investment advisory services (license number 3073). Lecturer at the GPW Foundation, conducting training for investors in the field of technical analysis, behavioral finance, and principles of managing a portfolio of financial instruments.
Founder at e-smartdata
PL
Data Scientist, Securities Broker
Jestem miłośnikiem nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python big data oraz rozwiązań chmurowych. Posiadam stopień absolwenta podyplomowych studiów na kierunku Informatyka, specjalizacja Big Data w Polsko-Japońskiej Akademii Technik Komputerowych oraz magistra z Matematyki Finansowej i Aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego. Od 2015 roku posiadam licencję Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073). Jestem również wykładowcą w Fundacji GPW prowadzącym szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych. Mam doświadczenie w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką. Moje główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe.
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