Am I a data scientist?

Slides from my talk at JSM 2015, in the session: "The Statistics Identity Crisis: Are We Really Data Scientists?" https://www.amstat.org/meetings/jsm/2015/onlineprogram/ActivityDetails.cfm?SessionID=211266

Transcript

1. Am I a data scientist? Alyssa Frazee, Stripe @acfrazee
2. © 2009 Lisa Slavid
3. statistician a data scientist © 2009 Lisa Slavid
4. Where I’m coming from Math undergrad Biostatistics PhD “Machine Learning Engineer” today Recurse Center (née Hacker School) 2010
5. Am I a data scientist?
6. Am I a data scientist? What do I really mean by this question?
7. Am I a data scientist? What do I really mean by this question? Could I get a job offer with a title of “data scientist?”
8. Am I a data scientist? What do I really mean by this question? Am I preparing my students to be able to get job offers with a title of “data scientist?”
9. Am I a data scientist? What do I really mean by this question? Could I get a job offer with a title of “data scientist?” → sometimes implicitly industry → and sometimes specifically tech
10. What’s “data science”?
12. data skills spectrum
13. theoretical statistics software engineering
14. theoretical statistics software engineering data science
15. understanding quantitative data building a product data science
16. output: numerical results output: usable software data science
17. Am I a statistician? points for: • Am in a grad program called [bio]statistics • Know things about martingales and the delta method • Can explain what a p-value is and interpret linear regression coefficients points against: • Haven’t proved a theorem since 2011 • Spend more time writing bash scripts than inventing estimators • No publications in statistics journals
18. Or am I a data scientist? points for: • Can program in more than one language • Actively use git & GitHub • Have written R packages and reproducible reports • Once made a web app and also a D3.js graph points against: • Not working in industry • Have never written a SQL query more complicated than select * from table • Understanding of Hadoop, Spark, and AWS is vague at best • Have never written production code
20. Idea! I will listen to what experts in our field say! Camp #1: Data science is just a rebranding of applied statistics. Camp #2: Statistics and data science are overlapping. Neither is a subset of the other. Camp #3: Statistics is irrelevant to data science.
22. First: do I want to be a data scientist?
23. Second: Does it matter?
24. Am I on the job market? Am I hiring?
25. If you decide it matters: some distinguishing features
26. Intentionality about programming
27. Intentionality about programming Spending time thinking primarily about: • code efficiency • version control • code quality (cleanliness, modularity) • documentation / usability • unit testing • systematic debugging • giving and receiving code review • and other principles of software engineering
28. Interest in schleppy- but-practical projects
29. Interest in schleppy- but-practical projects • figuring out how to get the data you need • combining existing tools/methods in new ways • finding the simplest solution that works in practice
30. Focus on concrete decision-making
31. Focus on concrete decision-making less about inference and parameter estimation, more about what action should be taken
32. Camp #1: Data science is just a rebranding of applied statistics. Camp #2: Statistics and data science are overlapping. Neither is a subset of the other. Camp #3: Statistics is irrelevant to data science.
33. Perspective from the other side Camp #1: Data science is just a rebranding of applied statistics.
34. Perspective from the other side Camp #1: Data science is just a rebranding of applied statistics. Intentionality about programming
35. Perspective from the other side Camp #1: Data science is just a rebranding of applied statistics. The day-to-day work is different!
36. Perspective from the other side Last month I: • wrote Ruby, Scala, Coffeescript, and Python • fought with maven • backfilled some busted tables in our databases • investigated the mystery of why some of our cluster boxes are overworked • learned how to be on call (so I can fix some of Stripe if it breaks at 3am) • helped teach a SQL class • and did some statistics
37. Camp #3: Statistics is irrelevant to data science. Perspective from the other side
38. Camp #3: Statistics is irrelevant to data science. Perspective from the other side
39. Statistics and data science are overlapping. Neither is a subset of the other. Perspective from the other side
40. About that identity crisis: Program intentionally and be a data scientist, if you want!
41. About that identity crisis: Or don’t! Statistics is hugely important and relevant in its own right!
42. • http://andrewgelman.com/2013/11/14/statistics-least- important-part-data-science/ • http://bulletin.imstat.org/2014/09/data-science-how-is-it- different-to-statistics%E2%80%89/ • https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the- 21st-century/ • http://datascopeanalytics.com/blog/what-is-a-data-scientist/ Further reading: