What is data science?

1. DATA SCIENCE?
what is
DATA SCIENCE?
- making an impact with data -
Ta Virot Chiraphadhanakul
Data Scientist, Facebook

2. Harvard Business Review (October 2012)

3. whatis data science?

4. VALUABLE INSIGHTS
transforming data into

5. “No one should die because they cannot afford
health care and no one should go broke because
they get sick. If you agree please post this as your
status for the rest of the day”
Information Evolution in Social Networks (Adamic et al. 2014)

6. Social Influence in Social Advertising: Evidence from Field Experiments (Bakshy et al. 2012)

7. A 61-million-person experiment in social influence and political mobilization (Bond et al. 2012)

8. DATA PRODUCTS
transforming data into

12. email classification
spam detection

13. INTERESTING STORIES
and sometimes, transforming data into

14. http://www.google.org/flutrends/

15. Coordinated Migration (Facebook Data Science page)

16. https://jawbone.com/blog/2014-year-review/

17. How families interact on Facebook (Facebook Data Science page)

18. social good
data science for

19. making data accessible to everyone
OPEN DATA

23. https://www.alltuition.com/

24. www.allthebuses.com

25. http://mbtaviz.github.io/

26. http://fragile-success.rpa.org/maps/jobs.html

27. don’t have the data you want? just ask for them!
CROWDSOURCING DATA

28. http://www.openstreetmap.org/

29. http://www.streetbump.org/

30. http://www.waze.com/

31. https://www.23andme.com/

33. hack-a-thons, meet-ups, competitions, fellowships
BUILDING COMMUNITIES

35. https://seanjtaylor.github.io/out-for-justice/

36. http://ivory-infinity-763.appspot.com/

38. http://www.kaggle.com

40. hubway.virot.me bayareabikeshare.virot.me

43. ELEMENTSof data science

44. http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

45. CODING, CODING, CODING

46. STATISTICS

48. data wrangling (aka data janitor work)
BIG DATA, BIG PILE OF JUNK(?)

49. Harvard Business Review (April 2014)

50. http://xkcd.com/605/

51. ice cream consumption vs. murders
CORRELATION ⇏ CAUSATION

52. randomized controlled experiments, a/b testing
CAUSAL INFERENCE

53. MACHINE LEARNING

54. regression, classification, clustering, collaborative
filtering, etc.
MACHINE LEARNING TASKS

55. feature engineering
GARBAGE IN, GARBAGE OUT

56. cross-validation and
penalizing model complexity (regularization)
AVOID OVERFITTING

57. Nature (February 2013)

58. DOMAIN EXPERTISE

59. measure the right things
METRIC, METRIC, METRIC

60. Seven Pitfalls to Avoid when Running Controlled Experiments on the Web (Crook et al. 2009)

61. speed, simplicity, cost of obtaining data, …
BEYOND MODEL ACCURACY

63. VISUALIZATION

64. Graphs in Statistical Analysis (Anscombe 1973)

65. EXPLORATORY DATA ANALYSIS
summarize and visualize important characteristics of a
data set

66. http://bl.ocks.org/mbostock/4063663

69. http://xkcd.com/1138/

70. THANK YOU
Ta Virot Chiraphadhanakul
http://ta.virot.me | @tvirot
THANK YOU