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Home > Test and Code > 33: Katharine Jarmul - Testing in Data Science


	Podcast:		Test and Code
	Episode:		33: Katharine Jarmul - Testing in Data Science
	Category:		Technology
	Duration:		00:37:14
	Publish Date:		2017-11-30 11:00:00
	Description:		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. Sponsored By: Python Testing with pytest: Simple, Rapid, Effective, and Scalable The fastest way to learn pytest. From 0 to expert in under 200 pages. Patreon Supporters: Help support the show with as little as $1 per month. Funds help pay for expenses associated with the show. 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.
	Total Play:		0