33: Katharine Jarmul - Testing in Data Science

About this Episode

A discussion with Katharine Jarmul, aka kjam, about some of the challenges of data science with respect to testing.

Some of the topics we discuss:

• experimentation vs testing
• testing pipelines and pipeline changes
• automating data validation
• property based testing
• schema validation and detecting schema changes
• using unit test techniques to test data pipeline stages
• testing nodes and transitions in DAGs
• testing expected and unexpected data
• missing data and non-signals
• corrupting a dataset with noise
• fuzz testing for both data pipelines and web APIs
• datafuzz
• hypothesis
• testing internal interfaces
• documenting and sharing domain expertise to build good reasonableness
• intermediary data and stages
• neural networks
• speaking at conferences

Episode Links

• @kjam on Twitter — Data Magic and Computer Sorcery
• Kjamistan: Data Science
• datafuzz’s Python library — The goal of datafuzz is to give you the ability to test your data science code and models with BAD data.
• Hypothesis Python library — Hypothesis is a Python library for finding edge cases in your code you wouldn’t have thought to look for.