The document discusses the evolution and definitions of data science, highlighting its distinctions in academia and industry since 2009. It emphasizes the increasing relevance of data analysis and machine learning, referencing historical contributions and contemporary developments at organizations like The New York Times. Additionally, it outlines the interconnected skill sets necessary for data science, including data analytics, data engineering, and related fields.

1. data science: past present & future
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins

2. data science: 2 definitions

3. applied machine learning
drew conway, 2010

4. applied machine learning
drew conway, 2010
(closer to DS in academia)

5. modern history:
2009

6. modern history:
2009

7. modern history:
2009
(closer to DS in industry)

8. data science: pre-2009 history

9. http://www.stat.ucla.edu/~cocteau/ds3.pdf (2007)
GOOG: “igert in data science”

10. “data science”
ancient history: 2001

11. “data science”
ancient history: 2001

12. data science
context

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

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

15. Tukey 1965, via John Chambers

16. fast forward -> 1993

17. fast forward -> 2001

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

19. NB: placement of stats in math
“Data Analysis… what can be learned from 50 years”
Huber, 2012

20. NB: placement of stats in math
“Data Analysis… what can be learned from 50 years”
Huber, 2012

21. “Data Analysis… what can be learned from 50 years”
Huber, 2012

22. not always obvious, cf. Hoteling 1945
“Data Analysis… what can be learned from 50 years”
Huber, 2012

23. data science: 2 definitions
1. in academia: machine learning
applied, science/research focus
2. in industry: machine learning
applied, product/software focus

24. data science @ The New York Timeshistory: 2 epochs
1. slow burn @Bell: as heretical
statistics (see also Breiman)
2. caught fire 2009-now: as job
description

25. data science @ The New York Times
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins

26. news: 21st century
church state
data

27. "...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’’

28. "...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, “GLS”

29. (actually ML, shhhh…)
h/t michael littman

30. (actually ML, shhhh…)
h/t michael littman
Supervised
Learning
Reinforcement
Learning
Unsupervised
Learning
(reports)

31. descriptive, predictive,
prescriptive modeling

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

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

34. data science: ideas

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

36. data science and…
- data analytics
- data engineering
- data embeds
- data product
- data multiliteracies
cf. “data scientists at work”, ch 1
data science and…
- data analytics
- data engineering
- data embeds
- data product
- data multiliteracies
data skills
nota bene!
these are 3 separate skill sets;
academia often conflates the 3.
In industry these are*
- 3 related functions
- 3 collaborating teams
* or at least @ NYT, 2016

37. data science
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins

38. </talk>

39. <appendices>

46. high school!

48. role of higher ed
in complementary/experiential education?

49. complementary/experiential education?
see also: http://bit.ly/hackNY15vid