Once Upon a Data: Crunching Chaotic Numbers into True Stories

Slides of a workshop given to non-data practitioners about dealing with data and understanding common traps.

Note: I came to learn later that The Berkely admission example is not the best to use in this context. Going back, I'd pick a better example.

Transcript

1. Crunching Chaotic Numbers into Compelling Stories TRUE ONCE UPON A D T OMAYMA SAID
4. People & Questions A COMPLEX Relationship ? ?
5. Fireflies or Balls of Gases? Ever wondered what those sparkly dots are up there? Scene Link
6. The Cult of Certainty The Beginner’s Mind “I don’t wonder I KNOW” “Have you ever WONDERED?”
7. Go to School Grow Up Humans Default Setup: INQUIRY MODE
8. Go to School Grow Up Humans Default Setup: INQUIRY MODE We go to SCHOOL We GROW UP BUT
9. I position myself relentlessly as an IDIOT at IDEO Paul Bennett Chief Creative Officer “ ”
10. Business Question Business Decision Taking Decisions The Classical Way
11. Business Question Data Question Data Answer Business Decision Taking Decisions Data in the loop
12. As I talk with companies about digital transformation, by far THE BIGGEST CHALLENGES they face are CULTURAL, not technical “ ” Doug Cutting Chief Architect
13. Data IN Insights/ Solution OUT Machine Learning
14. NOT in Real Life! Data IN Insights/ Solution OUT Machine Learning
15. Algorithms
16. It takes a BIG man to admit his data is small “ ” Joe Cheng CTO Big Data
17. “What is the PROBLEM?”
18. Data Traps and Trickery
19. What Do Movie Ratings Hide?
20. What Do Movie Ratings Hide?
21. What Do Movie Ratings Hide?
22. What Do Movie Ratings Hide?
23. What Do Movie Ratings Hide?
24. What Do Movie Ratings Hide?
25. What Do Movie Ratings Hide?
26. What Do Movie Ratings Hide?
28. 1 Single Measure/Score Data Traps and Trickery
29. Do Storks Deliver Babies?
30. Do Storks Deliver Babies? Correlation +ve -ve
31. Do Storks Deliver Babies? Correlation
32. Do Storks Deliver Babies? Recreated from data and code by Evelina Gabasova
33. Do Storks Deliver Babies? Statistically Significant Recreated from data and code by Evelina Gabasova Correlation Coefficient = 0.62 P-value = 0.0079
34. Do Storks Deliver Babies? STORKS BABIES
35. Do Storks Deliver Babies? STORKS BABIES “Let’s INCREASE the storks”
36. Do Storks Deliver Babies? STORKS BABIES Confounder?
37. Do Storks Deliver Babies? Country Area? Recreated from data and code by Evelina Gabasova
38. Do Storks Deliver Babies? Population? Recreated from data and code by Evelina Gabasova
39. Correlation Between X & Y 0.816 Anscombe's Quartet
40. ” Never trust summary statistics alone; always visualize your data “ Alberto Cairo The Functional Art Source:: The Functional Art Blog
41. 2 Shallow Correlations Data Traps and Trickery
42. Was There a Gender Bias In Graduate Admission at ?
43. Was There a Gender Bias in Graduate Admission at Berkeley? was sued for gender bias 1973 vudlab.com/simpsons
44. Was There a Gender Bias in Graduate Admission at Berkeley? vudlab.com/simpsons What about segmenting by DEPARTMENT?
45. Recreated based on Rafael Irizaary educational gifs Reversing Trend
46. 3 Simpson’s Paradox Data Traps and Trickery
47. How Wealthy Would You Be ON AVERAGE ? What if Zuckerberg Walked into This Room? gadgetmatch.com
48. How Wealthy Would You Be ON AVERAGE ? What if Zuckerberg Walked into This Room? gadgetmatch.com 100 $ 1000 $ 70 000 000 000 $ Median 600 $ Mean ~ 6 Billion $
53. 4 Misuse of The Average Data Traps and Trickery
55. 5 Invalid Extrapolation Data Traps and Trickery
56. Data Traps and Trickery 1- Single Measure/Score 2- Shallow Correlations 3- Simpson's’ Paradox 4- Misuse of the Average 5- Invalid Extrapolation And more…
57. Ask better questions Dealing with the complexity of real-life problems
58. Test more and be skeptical Dealing with the complexity of real-life problems
59. Have humility about our work Dealing with the complexity of real-life problems
60. Avoid falling in love with our results Dealing with the complexity of real-life problems
61. Consider the ethical implications Dealing with the complexity of real-life problems
62. Crunching Chaotic Numbers into Compelling Stories TRUE ONCE UPON A D T OMAYMA SAID