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Lean Data Science
The document discusses the challenges and methodologies in data science, emphasizing the importance of efficiency and collaboration. It contrasts data science with R&D and software development, highlighting the need for simpler solutions and effective communication. Key takeaways include the value of a minimum viable model and fostering empathy within teams.
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Lean Data Science
- 1. LEAN DATA SCIENCE NoelleSio Saldana @noellesio February 2017
- 2. DATA SCIENCE IS AWESOME DataScience is awesome!
- 3. DATA SCIENCE CAN MAKE___ BETTER Data Science is awesome!
- 4. ...BUT IT’S ALOT OF WORK
- 5. ISN’T THERE ABETTER WAY TO DO THIS? (We are Data Scientists, after all.)
- 6. Is Data Sciencemore like R&D or software?
- 7. R&D “I want totest this hypothesis” Success is measured by model fit
- 8. SOFTWARE “I want tochange the way people act” Success is measured by impact
- 9.
- 11.
- 12. Just ship it already MinimumViable Models
- 13. Do you havean ‘Alpha Nerd’ problem?
- 14. Pair Programming worksfor Data Scientists, too.
- 15. 15 Let’s Communicate More (Or whywe have scrums and retrospectives)
- 16.
- 17. Some takeaways 1) Problemscan have simpler solutions 2) More communication is a good thing 3) We’re all in this together
- 18.