Writing Unit Tests for Data Science Code

This course teaches best practices for testing Data Science code, focusing on bridging the gap between Software Engineering and Data Science. The learning outcomes include understanding unit testing in Data Science, real-world applications, use cases, key takeaways, and tools that aid in testing. The course covers skills such as mocking calls, mocking data, and utilizing tools like Pytest. The teaching method includes a talk by Dr Nile Wilson, with a syllabus covering various aspects of unit testing in Data Science. The intended audience includes those in MLOps, anyone involved in productionalizing data, and individuals looking to enhance their testing practices in Data Science applications.