Unit Testing for Data Science in Python

Course Overview

This course provides an overview of unit testing for data science in Python. Students will learn how to properly structure their test suite, how to execute any subset of tests with ease, and how to flag problematic tests so that their test suite remains green at all times. Additionally, students will learn how to write sanity tests for data science models as well as how to test matplotlib plots. They will also discover how to strike a balance between writing too many and too few tests. Finally, students will be introduced to and put into practise a revolutionary new programming methodology known as Test Driven Development (TDD).

[Applications]
After this course, students can apply the knowledge they have gained to create unit tests for their data science projects in Python. They can use the techniques they have learned to structure their test suite, execute any subset of tests, and flag problematic tests. They can also use the knowledge of writing sanity tests for data science models and testing matplotlib plots to ensure their projects are of the highest quality. Additionally, students can use the Test Driven Development methodology to create their projects in a more efficient and effective manner.

[Career Path]
One job position path that is recommended for learners of this course is a Data Science Test Engineer. A Data Science Test Engineer is responsible for developing and executing automated tests for data science models and applications. They must have a strong understanding of data science principles and be able to write code in Python. They must also be able to create and maintain test suites, and be able to identify and flag problematic tests.

The development trend for this job position is that it is becoming increasingly important as data science models become more complex and data sets become larger. As data science models become more complex, the need for automated tests to ensure accuracy and reliability increases. Data Science Test Engineers are also becoming more important as organizations move towards a DevOps model, where testing is integrated into the development process. As organizations move towards this model, Data Science Test Engineers will be responsible for ensuring that tests are properly integrated into the development process.

[Education Path]
The recommended educational path for learners of this course is to pursue a degree in Data Science. This degree will provide learners with the knowledge and skills to develop and apply data science techniques to solve real-world problems. The degree will cover topics such as data analysis, machine learning, data visualization, and software engineering. Learners will also learn how to use Python to develop data science applications.

The development trend of this degree is to focus on the application of data science techniques to solve real-world problems. This includes the use of machine learning algorithms, data visualization techniques, and software engineering principles. Learners will also be expected to develop their own data science applications using Python. Additionally, learners will be expected to understand the principles of unit testing and test driven development, which are essential for developing reliable data science applications.