Pydata Ann Arbor Eric Ma Testing Data Science Code

By Editorial Team · October 21, 2026 · 4 min read

Pydata Ann Arbor Eric Ma Testing Data Science Code Information Guide

  1. Background on Pydata Ann Arbor Eric Ma Testing Data Science Code
  2. Key Details
  3. Latest News
  4. Expert Insights
  5. Final Thoughts

Background on Pydata Ann Arbor Eric Ma Testing Data Science Code

PyData Ann Arbor: Eric Ma | Testing Data Science Code News
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Key Details

Details Eric Ma - Principled Data Science Workflows | PyData Boston July Virtual Meetup News
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Latest News

Details Eric J Ma Best Testing Practices for Data Science PyCon 2017 Guide
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[63] Software Testing in Open Source and Data Science (Eric Ma)
[63] Software Testing in Open Source and Data Science (Eric Ma)
PyData Ann Arbor: Reshama Shaikh | Community Building: The Good and Messy
PyData Ann Arbor: Reshama Shaikh | Community Building: The Good and Messy
Do Your Homework! Writing tests for Data Science and Stochastic Code - David Waterman
Do Your Homework! Writing tests for Data Science and Stochastic Code - David Waterman
Raoul-Gabriel Urma: Advanced Software Testing for Data Scientists | PyData New York 2019
Raoul-Gabriel Urma: Advanced Software Testing for Data Scientists | PyData New York 2019
Eric J Ma Best Testing Practices for Data Science PyCon 2017
Eric J Ma Best Testing Practices for Data Science PyCon 2017
PyData Boston 2025 Talk: Productive Patterns for Agent-Assisted Programming
PyData Boston 2025 Talk: Productive Patterns for Agent-Assisted Programming
Theodore Meynard - Test your data like you test your code | Pydata London 2022
Theodore Meynard - Test your data like you test your code | Pydata London 2022
Raoul-Gabriel Urma, Kevin Lemagnen: Adv. Software Testing for Data Scientists | PyData London 2019
Raoul-Gabriel Urma, Kevin Lemagnen: Adv. Software Testing for Data Scientists | PyData London 2019
Eric Ma - Beyond Two Groups: Generalized Bayesian A/B[/C/D/E...] Testing - PyCon 2019
Eric Ma - Beyond Two Groups: Generalized Bayesian A/B[/C/D/E...] Testing - PyCon 2019
PyData Ann Arbor: Caitlin Hudon | Building Data Science
PyData Ann Arbor: Caitlin Hudon | Building Data Science
Effective Testing for Machine Learning Projects - Eduardo Blancas | PyData Global 2021
Effective Testing for Machine Learning Projects - Eduardo Blancas | PyData Global 2021

Expert Insights

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Last Updated: October 21, 2026

Final Thoughts

Details Eric Ma-Patterns for Productive Agent-Assisted Programming-PyData Boston 2025 Guide
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Summary

You're already using AI coding assistants for more than autocomplete, but are you using them effectively? This talk presents ... Join our Meetup group: meetup.com/data-umbrella The journey to deploy a model to production starts with

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