Automated Testing: A Software Engineering Concept Data Scientists Must Know To Succeed | Towards Data Science
Summary
This article explains why automated testing matters for data science code, especially when notebooks and pipelines become difficult to maintain. It shows how to refactor a monolithic pipeline into smaller, testable functions and how to write unit tests with the AAA pattern. It also explains which parts of a codebase deserve testing and which ones do not, such as thin wrappers around trusted third-party libraries. The article ends by recommending a clear project structure and a CI pipeline so tests run automatically on every change.
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