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