Software Engineers, the New Data Scientists

scikit-learn day / PyData Paris / 15.06.16

Transcript

1. Christophe Bourguignat @chris_bour / @zelrosHQ scikit-learn day / PyData Paris / 15.06.16
3. MODERN EXAMPLE (1936)
4. SOLUTION 1 : USE EXCEL (easy)
5. SOLUTION 2 : CODE A SIMPLE BUT ROBUST ALGORITHM (medium)
6. SOLUTION 3 : DIVE INTO THEORY AND IMPLEMENT (hard) Gilles Louppe
7. Hopefully Scikit-Learn Does Exist (and this changes everything)
8. Machine Learning Without Learning the Machinery
9. 100 000
10. “I put one of my courses online and it reached an audience of 100 000 students”
12. “To reach a comparable sized audience I would have had to teach at Stanford for 250 years”
13. Andrew Ng
14. https://www.google.fr/trends/explore#q=kaggle Kaggle Creation
15. Scikit-learn Moocs Kaggle Time for a Venn Diagram !
16. Machine Learning Has Become a Meritocracy
17. WHO IS THE MASS POPULATION BEST PLACED TO LEVERAGE THIS MERITOCRACY ?
18. WHO IS THE MASS POPULATION BEST PLACED TO LEVERAGE THIS MERITOCRACY ?
20. Gartner, 2016
21. Many Scikit-Learn contributors are themselves software engineers Analysis of 40 scikit-learn contributors Linkedin Profiles
22. Who will be the New New Data Scientist ?
23. Citizen Data Scientist
25. TOP 5%
26. We will all be kind of data scientists (in a way or an other)
28. We are hiring Data Scientists Software Engineers job@zelros.com Wanting to become
29. @chris_bour / @zelrosHQ