Unit Testing for Data Science in Python

Learn how to write unit tests for your Data Science projects in Python using pytest.
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4 Hours17 Videos55 Exercises10,395 Learners
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Course Description

Every data science project needs unit testing. It comes with huge benefits - saving a lot of development and maintenance time, improving documentation, increasing end-user trust and reducing downtime of productive systems. As a result, unit testing has become a must-have skill in the industry, used by almost every company. This course teaches unit testing in Python using the most popular testing framework pytest. By the end of this course, you will have written a complete test suite for a data science project. In the process, you will learn to write unit tests for data preprocessors, models and visualizations, interpret test results and fix any buggy code. You will also learn advanced concepts like TDD, test organization, fixtures and mocking so that you can test your own data science projects properly.

  1. 1

    Unit testing basics

    In this chapter, you will get introduced to the pytest package and use it to write simple unit tests. You'll run the tests, interpret the test result reports and fix bugs. Throughout the chapter, we will use examples exclusively from the data preprocessing module of a linear regression project, making sure you learn unit testing in the context of data science.
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  2. 2

    Intermediate unit testing

    In this chapter, you will write more advanced unit tests. Starting from testing complicated data types like NumPy arrays to testing exception handling, you'll do it all. Once you have mastered the science of testing, we will also focus on the arts. For example, we will learn how to find the balance between writing too many tests and too few tests. In the last lesson, you will get introduced to a radically new programming methodology called Test Driven Development (TDD) and put it to practice. This might actually change the way you code forever!
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  3. 3

    Test Organization and Execution

    In any data science project, you quickly reach a point when it becomes impossible to organize and manage unit tests. In this chapter, we will learn about how to structure your test suite well, how to effortlessly execute any subset of tests and how to mark problematic tests so that your test suite always stays green. The last lesson will even enable you to add the trust-inspiring build status and code coverage badges to your own project. Complete this chapter and become a unit testing wizard!
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  4. 4

    Testing Models, Plots and Much More

    In this chapter, You will pick up advanced unit testing skills like setup, teardown and mocking. You will also learn how to write sanity tests for your data science models and how to test matplotlib plots. By the end of this chapter, you will be ready to test real world data science projects!
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In the following tracks
Data EngineerPython ProgrammerPython Programming
Hadrien LacroixHillary Green-Lerman
Intermediate Python
Dibya Chakravorty Headshot

Dibya Chakravorty

Senior Python Developer, TECH-5

Dibya is currently developing a test automation framework for a leading German car manufacturer. He thinks that high-quality, well-tested code is far more valuable than code that only seems to work.

His other passion is Deep Reinforcement Learning, because it is a step towards Strong AI. Check out his free Reinforcement Learning course to get a comprehensive introduction to Deep RL.

Dibya has a deep love for Python. He co-organizes the Python meetup group in Munich. He also maintains a website that helps learners find the best Python books in any topic.

Want to connect with him? Here is his LinkedIn profile.

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