inaugural Data Science Initiative Lecture @ Brown University 2015-12-04 https://www.eventbrite.com/e/data-science-at-the-new-york-times-tickets-19490272931

1. data science @ The New York Times
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins
references: bit.ly/brown-refs

2. data science @ The New York Times

3. data science @ The New York Times

4. “data science”
jobs, jobs, jobs

5. “data science”
jobs, jobs, jobs

6. data science: mindset & toolset
drew conway, 2010

7. modern history:
2009

8. modern history:
2009

9. “data science”
ancient history: 2001

10. “data science”
ancient history: 2001

11. data science
context

12. home schooled

13. B.A. & M.Sc. from Brown

14. PhD in topology

15. “By the end of late 1945, I was a
statistician rather than a topologist”

16. invented: “bit”

17. invented: “software”

18. invented: “FFT”

19. “the progenitor of data science.” - @mshron

20. “The Future of Data Analysis,” 1962
John W. Tukey

21. introduces:
“Exploratory data anlaysis”

22. Tukey 1965, via John Chambers

23. TUKEY BEGAT S WHICH BEGAT R

24. Tukey 1972

25. Tukey 1975
In 1975, while at Princeton, Tufte was asked to teach a
statistics course to a group of journalists who were visiting
the school to study economics. He developed a set of
readings and lectures on statistical graphics, which he
further developed in joint seminars he subsequently taught
with renowned statistician John Tukey (a pioneer in the field
of information design). These course materials became the
foundation for his first book on information design, The
Visual Display of Quantitative Information

26. TUKEY BEGAT VDQI

27. Tukey 1977

28. TUKEY BEGAT EDA

29. fast forward -> 2001

30. “The primary agents for change should be
university departments themselves.”

31. data science @ The New York Timeshistories
1. slow burn @Bell: as heretical
statistics (see also Breiman)
2. caught fire 2009-now: as job
description
historical rant: bit.ly/data-rant

32. biology: 1892 vs. 1995

33. biology: 1892 vs. 1995
biology changed for good.

34. biology: 1892 vs. 1995
new toolset, new mindset

35. genetics: 1837 vs. 2012
ML toolset; data science mindset

36. genetics: 1837 vs. 2012

37. genetics: 1837 vs. 2012
ML toolset; data science mindset
arxiv.org/abs/1105.5821 ; github.com/rajanil/mkboost

38. data science: mindset & toolset

39. 1851

40. news: 20th century
church state

41. church

42. church

43. church

44. news: 20th century
church state

45. news: 21st century
church state
engineering

46. 1851 1996
newspapering: 1851 vs. 1996

47. example:
millions of views per hour2015

49. "...social activities generate large quantities of potentially
valuable data...The data were not generated for the
purpose of learning; however, the potential for learning
is great’’

50. "...social activities generate large quantities of potentially
valuable data...The data were not generated for the
purpose of learning; however, the potential for learning
is great’’ - J Chambers, Bell Labs,1993

51. data science: the web

52. data science: the web
is your “online presence”

53. data science: the web
is a microscope

54. data science: the web
is an experimental tool

55. 1851 1996
newspapering: 1851 vs. 1996 vs. 2008
2008

56. “a startup is a temporary organization in search of a
repeatable and scalable business model” —Steve Blank

57. every publisher is now a startup

58. every publisher is now a startup

60. news: 21st century
church state
engineering

61. news: 21st century
church state
engineering

62. learnings

63. learnings
- predictive modeling
- descriptive modeling
- prescriptive modeling

64. (actually ML, shhhh…)
- (supervised learning)
- (unsupervised learning)
- (reinforcement learning)

65. learnings
- predictive modeling
- descriptive modeling
- prescriptive modeling
cf. modelingsocialdata.org

66. predictive modeling, e.g.,
cf. modelingsocialdata.org

67. predictive modeling, e.g.,
“the funnel”
cf. modelingsocialdata.org

68. interpretable predictive modeling
supercoolstuff
cf. modelingsocialdata.org

69. interpretable predictive modeling
supercoolstuff
cf. modelingsocialdata.org
arxiv.org/abs/q-bio/0701021

70. optimization & learning, e.g.,
“How The New York Times Works “popular mechanics, 2015

71. optimization & prediction, e.g.,
“How The New York Times Works “popular mechanics, 2015
(some models)
(somemoneys)

72. recommendation as predictive modeling

73. recommendation as predictive modeling
bit.ly/AlexCTM

74. descriptive modeling, e.g,
cf. daeilkim.com ; import bnpy

75. modeling your audience
bit.ly/Hughes-Kim-Sudderth-AISTATS15

76. modeling your audience
(optimization, ultimately)

77. also allows insight+targeting as inference
modeling your audience

78. prescriptive modeling

79. prescriptive modeling
cf. modelingsocialdata.org

80. prescriptive modeling
aka “A/B testing”;
RCT
cf. modelingsocialdata.org

81. prescriptive modeling, e.g,

82. prescriptive modeling, e.g,

83. prescriptive modeling, e.g,

84. Reporting
Learning
Test
Optimizing
Exploredescriptive:
predictive:
prescriptive:

85. Reporting
Learning
Test
Optimizing
Exploredescriptive:
predictive:
prescriptive:

86. common requirements in
data science:

87. common requirements in
data science:
1. people
2. ideas
3. things
cf. John Boyd, USAF

88. data science: ideas

89. data skills
data science and…
- data engineering
- data embeds
- data product
- data multiliteracies
cf. “data scientists at work”, ch 1

90. data science: ideas
- new mindset > new toolset

91. data science: people

92. thanks to the data science team!

93. data science @ The New York Times
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins
references: bit.ly/brown-refs