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Lean Data Science


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by Noelle Sio Saldana
Principal Data Scientist at Pivotal

The success of a Data Science project is not simply the model fit or the accuracy of its predictions; it is whether those models are being leveraged to make smarter business decisions. Over the past few years, Pivotal’s Data Scientists have experimented with software development methods practiced and taught by their Pivotal Labs counterparts in engineering, design and product management. By reframing Data Science as building software and products instead of research, we found that we reaped similar benefits: shorter and more productive iterations, and clients who actually used the models that we built and skills we taught long after we left.

In this talk, we discuss how we have successfully (and maybe not as successfully) borrowed principles from practices like Lean and Agile to Data Science. Topics include:

Minimum Viable Product Models
Build-Measure-Learn instead of a silver bullet
Pair programming
Scrums and retrospectives
Practicing empathy instead of elitism

Published in: Technology
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Lean Data Science

  1. 1. LEAN DATA SCIENCE Noelle Sio Saldana @noellesio February 2017
  2. 2. DATA SCIENCE IS AWESOME Data Science is awesome!
  3. 3. DATA SCIENCE CAN MAKE ___ BETTER Data Science is awesome!
  4. 4. ...BUT IT’S A LOT OF WORK
  5. 5. ISN’T THERE A BETTER WAY TO DO THIS? (We are Data Scientists, after all.)
  6. 6. Is Data Science more like R&D or software?
  7. 7. R&D “I want to test this hypothesis” Success is measured by model fit
  8. 8. SOFTWARE “I want to change the way people act” Success is measured by impact
  9. 9. Borrowing Innovative Practices
  10. 10.
  11. 11. Just ship it already Minimum Viable Models
  12. 12. Do you have an ‘Alpha Nerd’ problem?
  13. 13. Pair Programming works for Data Scientists, too.
  14. 14. 15 Let’s Communicate More (Or why we have scrums and retrospectives)
  15. 15. Empathy over elitism
  16. 16. Some takeaways 1) Problems can have simpler solutions 2) More communication is a good thing 3) We’re all in this together
  17. 17. THANK YOU