As Data Science becomes more of a staple in large organizations, the need for proper testing of code slowly becomes more integrated into the skillset of a Data Scientist.
Imagine you have been creating a pipeline for predicting customer churn in your organization. A few months after deploying your solution, there are new variables that might improve its performance.

Unfortunately, after adding those variables, the code suddenly stops working! You are not familiar with the error message and you are having trouble finding your mistake.

This is where testing, and specifically unit testing, comes in!

Writing tests for specific modules improves the stability of your code and makes mistakes easier to spot. Especially when working on large projects, having proper tests is essentially a basic need.

No data solution is complete without some form of testing

This article will focus on a small, but very important and arguably the foundation of testing, namely unit tests. Below, I will discuss in detail why testing is necessary, what unit tests are, and how to integrate them into your Data Science projects.

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