Testing and Data Science

• Problems that could occur in data science aren’t always easily detectable; you might have values being encoded incorrectly, features being used inappropriately, unexpected data breaking assumptions
• To catch these errors, you have to check for the quality and accuracy of your analysis in addition to the quality of your code. Proper testing is necessary to avoid unexpected surprises and have confidence in your results.
• TEST DRIVEN DEVELOPMENT: a development process where you write tests for tasks before you even write the code to implement those tasks.
• UNIT TEST: a type of test that covers a “unit” of code, usually a single function, independently from the rest of the program.

Resources:

• Four Ways Data Science Goes Wrong and How Test Driven Data Analysis Can Help: Blog Post
• Ned Batchelder: Getting Started Testing: Slide Deck and Presentation Video