Test & Code

33: Katharine Jarmul - Testing in Data Science

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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 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.
    ...more
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    Test & CodeBy Brian Okken
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