Test & Code - Python Testing & Development

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 Special Guest: Katharine Jarmul.Links:@kjam on Twitter — Data Magic and Computer SorceryKjamistan: Data Sciencedatafuzz’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 — Hypothesi