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data science @NYT ; inaugural Data Science Initiative Lecture

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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
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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

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