data history / data science @ NYT
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The document discusses the history and evolution of data science. It traces the field back to pioneers like John Tukey in the 1960s who introduced exploratory data analysis. It describes how fields like biology and genetics have been transformed by new machine learning tools and data science approaches. The document also outlines different types of data science work like supervised learning, unsupervised learning, and reinforcement learning. It emphasizes that successful data science requires a focus on people, ideas, and deliverables.
data history / data science @ NYT
- 1. data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins references: bit.ly/icerm
- 2. data science @ The New York Times references: bit.ly/icerm
- 3. data science @ The New York Times references: bit.ly/icerm
- 4. data science @ The New York Times references: bit.ly/icerm
- 5. data science @ The New York Times references: bit.ly/icerm
- 6. “data science” jobs, jobs, jobs references: bit.ly/icerm
- 7. “data science” jobs, jobs, jobs references: bit.ly/icerm
- 8. “data science” jobs, jobs, jobs references: bit.ly/icerm
- 9. data science: mindset & toolset drew conway, 2010 references: bit.ly/icerm
- 11. “data science” blogs, blogs, blogs references: bit.ly/icerm
- 12. “data science” blogs, blogs, blogs The first time I heard "data science" was in 2007 while reading a proposal that my adviser had passed along, outlining an academic program similar to what we think of as data science. The first time I heard "data science" was in 2007 while reading a proposal that my adviser had passed along, outlining an academic program similar to what we think of as data science. references: bit.ly/icerm
- 13. “data science” blogs, blogs, blogs references: bit.ly/icerm
- 14. “data science” ancient history: 2001 references: bit.ly/icerm
- 15. “data science” ancient history: 2001 references: bit.ly/icerm
- 18. PhD in topology references: bit.ly/icerm
- 19. “By the end of late 1945, I was a statistician rather than a topologist” references: bit.ly/icerm
- 23. “the progenitor of data science.” - @mshron references: bit.ly/icerm
- 24. “The Future of Data Analysis,” 1962 John W. Tukey references: bit.ly/icerm
- 25. introduces: “Exploratory data anlaysis” references: bit.ly/icerm
- 26. Tukey 1965, via John Chambers references: bit.ly/icerm
- 27. TUKEY BEGAT S WHICH BEGAT R references: bit.ly/icerm
- 30. Jerome H. Friedman references: bit.ly/icerm
- 31. 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 references: bit.ly/icerm
- 32. TUKEY BEGAT VDQI references: bit.ly/icerm
- 34. TUKEY BEGAT EDA references: bit.ly/icerm
- 35. fast forward -> 2001 references: bit.ly/icerm
- 36. “The primary agents for change should be university departments themselves.” references: bit.ly/icerm
- 37. data science @ The New York Timeshistories 1. in academia -> Bell: as heretical statistics (see also Breiman) 2. in industry: as job description historical rant: bit.ly/data-rant
- 38. data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins references: bit.ly/icerm
- 39. biology: 1892 vs. 1995 biology changed for good. references: bit.ly/icerm
- 40. genetics: 1837 vs. 2012 ML toolset; data science mindset references: bit.ly/icerm
- 41. genetics: 1837 vs. 2012 references: bit.ly/icerm
- 42. genetics: 1837 vs. 2012 ML toolset; data science mindset arxiv.org/abs/1105.5821 ; github.com/rajanil/mkboost
- 43. data science: mindset & toolset references: bit.ly/icerm
- 45. news: 20th century church state references: bit.ly/icerm
- 48. church
- 49. news: 20th century church state references: bit.ly/icerm
- 50. news: 21st century church state engineering references: bit.ly/icerm
- 51. 1851 1996 newspapering: 1851 vs. 1996 references: bit.ly/icerm
- 52. example: millions of views per hour2015
- 54. data science: the web references: bit.ly/icerm
- 55. data science: the web is your “online presence” references: bit.ly/icerm
- 56. data science: the web is a microscope references: bit.ly/icerm
- 57. data science: the web is an experimental tool references: bit.ly/icerm
- 58. data science: the web is an optimization tool references: bit.ly/icerm
- 59. 1851 1996 newspapering: 1851 vs. 1996 vs. 2008 2008 references: bit.ly/icerm
- 60. “a startup is a temporary organization in search of a repeatable and scalable business model” —Steve Blank references: bit.ly/icerm
- 61. every publisher is now a startup references: bit.ly/icerm
- 63. news: 21st century church state engineering references: bit.ly/icerm
- 64. news: 21st century church state engineering references: bit.ly/icerm
- 66. learnings - supervised learning - unsupervised learning - reinforcement learning references: bit.ly/icerm
- 67. learnings - supervised learning - unsupervised learning - reinforcement learning cf. modelingsocialdata.org references: bit.ly/icerm
- 69. from “are you a bayesian or a frequentist” —michael jordan L = NX i=1 ' (yif(xi; )) + || ||
- 70. supervised learning, e.g., cf. modelingsocialdata.org
- 71. supervised learning, e.g., “the funnel” cf. modelingsocialdata.org
- 72. interpretable supervised learning supercoolstuff cf. modelingsocialdata.org
- 73. interpretable supervised learning supercoolstuff cf. modelingsocialdata.org arxiv.org/abs/q-bio/0701021
- 74. optimization & learning, e.g., “How The New York Times Works “popular mechanics, 2015
- 75. optimization & prediction, e.g., “How The New York Times Works “popular mechanics, 2015 (some models) (somemoneys)
- 76. recommendation as supervised learning
- 77. recommendation as predictive modeling bit.ly/AlexCTM
- 78. unsupervised learning, e.g, cf. daeilkim.com ; import bnpy
- 80. modeling your audience (optimization, ultimately)
- 81. also allows recommendation as inference modeling your audience
- 86. common requirements in data science:
- 87. common requirements in data science: 1. people 2. ideas 3. things cf. USAF
- 88. things: what does DS team deliver?
- 89. things: what does DS team deliver? - build data prototypes - build APIs - impact roadmaps
- 90. - build data prototypes
- 91. - build data prototypes cf. daeilkim.com
- 92. - build data prototypes cf. daeilkim.com
- 93. - in puppet, w/python2.7 - collaboration w/pers. team - build APIs
- 96. data skills - data engineering - data science - data visualization - data product - data multiliteracies - data embeds cf. “data scientists at work”, ch 1
- 97. data skills - data engineering - data science - data visualization - data product - data multiliteracies - data embeds cf. “data scientists at work”, ch 1
- 98. data science: people - new mindset > new toolset
- 100. summary: pay attention to: 1. people 2. ideas 3. things cf. USAF
- 101. thanks to the data science team!
- 102. data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins