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data science: past present & future
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins
data science: 2 definitions
applied machine learning
drew conway, 2010
applied machine learning
drew conway, 2010
(closer to DS in academia)
modern history:
2009
modern history:
2009
modern history:
2009
(closer to DS in industry)
data science: pre-2009 history
http://www.stat.ucla.edu/~cocteau/ds3.pdf (2007)
GOOG: “igert in data science”
“data science”
ancient history: 2001
“data science”
ancient history: 2001
data science
context
“the progenitor of data science.” - @mshron
“The Future of Data Analysis,” 1962
John W. Tukey
Tukey 1965, via John Chambers
fast forward -> 1993
fast forward -> 2001
“The primary agents for change should be
university departments themselves.”
NB: placement of stats in math
“Data Analysis… what can be learned from 50 years”
Huber, 2012
NB: placement of stats in math
“Data Analysis… what can be learned from 50 years”
Huber, 2012
“Data Analysis… what can be learned from 50 years”
Huber, 2012
not always obvious, cf. Hoteling 1945
“Data Analysis… what can be learned from 50 years”
Huber, 2012
data science: 2 definitions
1. in academia: machine learning
applied, science/research focus
2. in industry: machine learn...
data science @ The New York Timeshistory: 2 epochs
1. slow burn @Bell: as heretical
statistics (see also Breiman)
2. caugh...
data science @ The New York Times
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins
news: 21st century
church state
data
"...social activities generate large quantities of potentially
valuable data...The data were not generated for the
purpose...
"...social activities generate large quantities of potentially
valuable data...The data were not generated for the
purpose...
(actually ML, shhhh…)
h/t michael littman
(actually ML, shhhh…)
h/t michael littman
Supervised
Learning
Reinforcement
Learning
Unsupervised
Learning
(reports)
descriptive, predictive,
prescriptive modeling
Reporting
Learning
Test
Optimizing
Exploredescriptive:
predictive:
prescriptive:
Reporting
Learning
Test
Optimizing
Exploredescriptive:
predictive:
prescriptive:
data science: ideas
data skills
data science and…
- data analytics
- data engineering
- data embeds
- data product
- data multiliteracies
cf. ...
data science and…
- data analytics
- data engineering
- data embeds
- data product
- data multiliteracies
cf. “data scient...
data science
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins
</talk>
<appendices>
high school!
role of higher ed
in complementary/experiential education?
complementary/experiential education?
see also: http://bit.ly/hackNY15vid
data science: past present & future [American Statistical Association (ASA) Chairs Workshop]
data science: past present & future [American Statistical Association (ASA) Chairs Workshop]
data science: past present & future [American Statistical Association (ASA) Chairs Workshop]
data science: past present & future [American Statistical Association (ASA) Chairs Workshop]
data science: past present & future [American Statistical Association (ASA) Chairs Workshop]
data science: past present & future [American Statistical Association (ASA) Chairs Workshop]
data science: past present & future [American Statistical Association (ASA) Chairs Workshop]
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data science: past present & future [American Statistical Association (ASA) Chairs Workshop]

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statistics and biostatistics chairs workshop held July 12-13 at American Statistical Association (ASA) headquarters in Alexandria, VA.

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data science: past present & future [American Statistical Association (ASA) Chairs Workshop]

  1. 1. data science: past present & future chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins
  2. 2. data science: 2 definitions
  3. 3. applied machine learning drew conway, 2010
  4. 4. applied machine learning drew conway, 2010 (closer to DS in academia)
  5. 5. modern history: 2009
  6. 6. modern history: 2009
  7. 7. modern history: 2009 (closer to DS in industry)
  8. 8. data science: pre-2009 history
  9. 9. http://www.stat.ucla.edu/~cocteau/ds3.pdf (2007) GOOG: “igert in data science”
  10. 10. “data science” ancient history: 2001
  11. 11. “data science” ancient history: 2001
  12. 12. data science context
  13. 13. “the progenitor of data science.” - @mshron
  14. 14. “The Future of Data Analysis,” 1962 John W. Tukey
  15. 15. Tukey 1965, via John Chambers
  16. 16. fast forward -> 1993
  17. 17. fast forward -> 2001
  18. 18. “The primary agents for change should be university departments themselves.”
  19. 19. NB: placement of stats in math “Data Analysis… what can be learned from 50 years” Huber, 2012
  20. 20. NB: placement of stats in math “Data Analysis… what can be learned from 50 years” Huber, 2012
  21. 21. “Data Analysis… what can be learned from 50 years” Huber, 2012
  22. 22. not always obvious, cf. Hoteling 1945 “Data Analysis… what can be learned from 50 years” Huber, 2012
  23. 23. data science: 2 definitions 1. in academia: machine learning applied, science/research focus 2. in industry: machine learning applied, product/software focus
  24. 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. 25. data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins
  26. 26. news: 21st century church state data
  27. 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. 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. 29. (actually ML, shhhh…) h/t michael littman
  30. 30. (actually ML, shhhh…) h/t michael littman Supervised Learning Reinforcement Learning Unsupervised Learning (reports)
  31. 31. descriptive, predictive, prescriptive modeling
  32. 32. Reporting Learning Test Optimizing Exploredescriptive: predictive: prescriptive:
  33. 33. Reporting Learning Test Optimizing Exploredescriptive: predictive: prescriptive:
  34. 34. data science: ideas
  35. 35. data skills data science and… - data analytics - data engineering - data embeds - data product - data multiliteracies cf. “data scientists at work”, ch 1
  36. 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. 37. data science chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins
  38. 38. </talk>
  39. 39. <appendices>
  40. 40. high school!
  41. 41. role of higher ed in complementary/experiential education?
  42. 42. complementary/experiential education? see also: http://bit.ly/hackNY15vid

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