Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins references: bit.ly/b...
data science @ The New York Times
data science @ The New York Times
“data science” jobs, jobs, jobs
“data science” jobs, jobs, jobs
data science: mindset & toolset drew conway, 2010
modern history: 2009
modern history: 2009
“data science” ancient history: 2001
“data science” ancient history: 2001
data science context
home schooled
B.A. & M.Sc. from Brown
PhD in topology
“By the end of late 1945, I was a statistician rather than a topologist”
invented: “bit”
invented: “software”
invented: “FFT”
“the progenitor of data science.” - @mshron
“The Future of Data Analysis,” 1962 John W. Tukey
introduces: “Exploratory data anlaysis”
Tukey 1965, via John Chambers
TUKEY BEGAT S WHICH BEGAT R
Tukey 1972
Tukey 1975 In 1975, while at Princeton, Tufte was asked to teach a statistics course to a group of journalists who were vi...
TUKEY BEGAT VDQI
Tukey 1977
TUKEY BEGAT EDA
fast forward -> 2001
“The primary agents for change should be university departments themselves.”
data science @ The New York Timeshistories 1. slow burn @Bell: as heretical statistics (see also Breiman) 2. caught fire 2...
biology: 1892 vs. 1995
biology: 1892 vs. 1995 biology changed for good.
biology: 1892 vs. 1995 new toolset, new mindset
genetics: 1837 vs. 2012 ML toolset; data science mindset
genetics: 1837 vs. 2012
genetics: 1837 vs. 2012 ML toolset; data science mindset arxiv.org/abs/1105.5821 ; github.com/rajanil/mkboost
data science: mindset & toolset
1851
news: 20th century church state
church
church
church
news: 20th century church state
news: 21st century church state engineering
1851 1996 newspapering: 1851 vs. 1996
example: millions of views per hour2015
"...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...
data science: the web
data science: the web is your “online presence”
data science: the web is a microscope
data science: the web is an experimental tool
1851 1996 newspapering: 1851 vs. 1996 vs. 2008 2008
“a startup is a temporary organization in search of a repeatable and scalable business model” —Steve Blank
every publisher is now a startup
every publisher is now a startup
news: 21st century church state engineering
news: 21st century church state engineering
learnings
learnings - predictive modeling - descriptive modeling - prescriptive modeling
(actually ML, shhhh…) - (supervised learning) - (unsupervised learning) - (reinforcement learning)
learnings - predictive modeling - descriptive modeling - prescriptive modeling cf. modelingsocialdata.org
predictive modeling, e.g., cf. modelingsocialdata.org
predictive modeling, e.g., “the funnel” cf. modelingsocialdata.org
interpretable predictive modeling supercoolstuff cf. modelingsocialdata.org
interpretable predictive modeling supercoolstuff cf. modelingsocialdata.org arxiv.org/abs/q-bio/0701021
optimization & learning, e.g., “How The New York Times Works “popular mechanics, 2015
optimization & prediction, e.g., “How The New York Times Works “popular mechanics, 2015 (some models) (somemoneys)
recommendation as predictive modeling
recommendation as predictive modeling bit.ly/AlexCTM
descriptive modeling, e.g, cf. daeilkim.com ; import bnpy
modeling your audience bit.ly/Hughes-Kim-Sudderth-AISTATS15
modeling your audience (optimization, ultimately)
also allows insight+targeting as inference modeling your audience
prescriptive modeling
prescriptive modeling cf. modelingsocialdata.org
prescriptive modeling aka “A/B testing”; RCT cf. modelingsocialdata.org
prescriptive modeling, e.g,
prescriptive modeling, e.g,
prescriptive modeling, e.g,
Reporting Learning Test Optimizing Exploredescriptive: predictive: prescriptive:
Reporting Learning Test Optimizing Exploredescriptive: predictive: prescriptive:
common requirements in data science:
common requirements in data science: 1. people 2. ideas 3. things cf. John Boyd, USAF
data science: ideas
data skills data science and… - data engineering - data embeds - data product - data multiliteracies cf. “data scientists ...
data science: ideas - new mindset > new toolset
data science: people
thanks to the data science team!
data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins references: bit.ly/b...
data science @NYT ; inaugural Data Science Initiative Lecture
data science @NYT ; inaugural Data Science Initiative Lecture
Upcoming SlideShare
Loading in …5
×

data science @NYT ; inaugural Data Science Initiative Lecture

179,997 views

Published on

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

Published in: Data & Analytics, Engineering
no profile picture user
  • There is a useful site for you that will help you to write a perfect and valuable essay and so on. Check out, please ⇒ www.HelpWriting.net ⇐
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • You can hardly find a student who enjoys writing a college papers. Among all the other tasks they get assigned in college, writing essays is one of the most difficult assignments. Fortunately for students, there are many offers nowadays which help to make this process easier. The best service which can help you is ⇒ www.HelpWriting.net ⇐
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • You can ask here for a help. They helped me a lot an i`m highly satisfied with quality of work done. I can promise you 100% un-plagiarized text and good experts there. Use with pleasure! ⇒ www.HelpWriting.net ⇐
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Don't forget another good way of simplifying your writing is using external resources (such as ⇒ www.HelpWriting.net ⇐ ). This will definitely make your life more easier
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • ⇒ www.WritePaper.info ⇐ This service will write as best as they can. So you do not need to waste the time on rewritings.
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
Show More
Show More

data science @NYT ; inaugural Data Science Initiative Lecture

  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
  48. "...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’’
  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’’ - J Chambers, Bell Labs,1993
  50. data science: the web
  51. data science: the web is your “online presence”
  52. data science: the web is a microscope
  53. data science: the web is an experimental tool
  54. 1851 1996 newspapering: 1851 vs. 1996 vs. 2008 2008
  55. “a startup is a temporary organization in search of a repeatable and scalable business model” —Steve Blank
  56. every publisher is now a startup
  57. every publisher is now a startup
  58. news: 21st century church state engineering
  59. news: 21st century church state engineering
  60. learnings
  61. learnings - predictive modeling - descriptive modeling - prescriptive modeling
  62. (actually ML, shhhh…) - (supervised learning) - (unsupervised learning) - (reinforcement learning)
  63. learnings - predictive modeling - descriptive modeling - prescriptive modeling cf. modelingsocialdata.org
  64. predictive modeling, e.g., cf. modelingsocialdata.org
  65. predictive modeling, e.g., “the funnel” cf. modelingsocialdata.org
  66. interpretable predictive modeling supercoolstuff cf. modelingsocialdata.org
  67. interpretable predictive modeling supercoolstuff cf. modelingsocialdata.org arxiv.org/abs/q-bio/0701021
  68. optimization & learning, e.g., “How The New York Times Works “popular mechanics, 2015
  69. optimization & prediction, e.g., “How The New York Times Works “popular mechanics, 2015 (some models) (somemoneys)
  70. recommendation as predictive modeling
  71. recommendation as predictive modeling bit.ly/AlexCTM
  72. descriptive modeling, e.g, cf. daeilkim.com ; import bnpy
  73. modeling your audience bit.ly/Hughes-Kim-Sudderth-AISTATS15
  74. modeling your audience (optimization, ultimately)
  75. also allows insight+targeting as inference modeling your audience
  76. prescriptive modeling
  77. prescriptive modeling cf. modelingsocialdata.org
  78. prescriptive modeling aka “A/B testing”; RCT cf. modelingsocialdata.org
  79. prescriptive modeling, e.g,
  80. prescriptive modeling, e.g,
  81. prescriptive modeling, e.g,
  82. Reporting Learning Test Optimizing Exploredescriptive: predictive: prescriptive:
  83. Reporting Learning Test Optimizing Exploredescriptive: predictive: prescriptive:
  84. common requirements in data science:
  85. common requirements in data science: 1. people 2. ideas 3. things cf. John Boyd, USAF
  86. data science: ideas
  87. data skills data science and… - data engineering - data embeds - data product - data multiliteracies cf. “data scientists at work”, ch 1
  88. data science: ideas - new mindset > new toolset
  89. data science: people
  90. thanks to the data science team!
  91. data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins references: bit.ly/brown-refs

×