DataEngConf: Data Science at the New York Times by Chris Wiggins
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This document discusses data science at The New York Times. It references various topics related to data science including predictive analytics, descriptive analytics, prescriptive analytics, data engineering, data science skills, and the importance of people, ideas, and tools/delivery in data science teams. It also references the data science work of Chris Wiggins and how data science has evolved the field of journalism and publishing.
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DataEngConf: Data Science at the New York Times by Chris Wiggins
- 1. data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins references: bit.ly/icerm
- 2. data science: mindset & toolset drew conway, 2010 references: bit.ly/icerm
- 5. data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins references: bit.ly/icerm
- 6. biology: 1892 vs. 1995 references: bit.ly/icerm
- 7. biology: 1892 vs. 1995 references: bit.ly/icerm new toolset, new mindset
- 9. news: 20th century church state references: bit.ly/icerm
- 12. church
- 13. news: 20th century church state references: bit.ly/icerm
- 14. news: 21st century church state engineering references: bit.ly/icerm
- 15. news: 21st century church state engineering: DSE references: bit.ly/icerm
- 16. 1851 1996 newspapering: 1851 vs. 1996 references: bit.ly/icerm
- 17. example: millions of views per hour2015
- 19. references: bit.ly/icerm "...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’’
- 20. references: bit.ly/icerm "...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
- 21. data science: the web references: bit.ly/icerm
- 22. data science: the web is your “online presence” references: bit.ly/icerm
- 23. data science: the web is a microscope references: bit.ly/icerm
- 24. data science: the web is an experimental tool references: bit.ly/icerm
- 25. data science: the web is an optimization tool references: bit.ly/icerm
- 26. 1851 1996 newspapering: 1851 vs. 1996 vs. 2008 2008 references: bit.ly/icerm
- 27. “a startup is a temporary organization in search of a repeatable and scalable business model” —Steve Blank references: bit.ly/icerm
- 28. every publisher is now a startup references: bit.ly/icerm
- 29. every publisher is now a startup
- 31. news: 21st century church state engineering references: bit.ly/icerm
- 32. news: 21st century church state engineering references: bit.ly/icerm
- 34. learnings - predictive analytics - descriptive analytics - prescriptive analytics references: bit.ly/icerm
- 35. learnings - predictive analytics - descriptive analytics - prescriptive analytics cf. modelingsocialdata.org references: bit.ly/icerm
- 36. predictive analytics, e.g., cf. modelingsocialdata.org
- 37. predictive analytics, e.g., “the funnel” cf. modelingsocialdata.org
- 38. interpretable predictive analytics supercoolstuff cf. modelingsocialdata.org
- 39. interpretable predictive analytics supercoolstuff cf. modelingsocialdata.org arxiv.org/abs/q-bio/0701021
- 40. optimization & learning, e.g., “How The New York Times Works “popular mechanics, 2015
- 41. optimization & prediction, e.g., “How The New York Times Works “popular mechanics, 2015 (some models) (somemoneys)
- 42. recommendation as predictive analytics
- 43. recommendation as predictive analytics bit.ly/AlexCTM
- 44. descriptive analytics, e.g, cf. daeilkim.com ; import bnpy
- 50. common requirements in data science:
- 51. common requirements in data science: 1. people 2. ideas 3. things cf. USAF
- 52. things: what does DS team deliver?
- 53. things: what does DS team deliver? - build data prototypes - build APIs - impact roadmaps
- 54. - build data prototypes
- 55. - build data prototypes cf. daeilkim.com
- 56. - build data prototypes cf. daeilkim.com
- 57. - in puppet, w/python2.7 - collaboration w/pers. team - build APIs
- 60. data skills - data engineering - data science - data visualization - data product - data multiliteracies - data embeds cf. “data scientists at work”, ch 1
- 61. data skills - data engineering - data science - data visualization - data product - data multiliteracies - data embeds cf. “data scientists at work”, ch 1
- 62. data skills - data engineering - data science - data visualization - data product - data multiliteracies - data embeds cf. “data scientists at work”, ch 1
- 63. data science: people - new mindset > new toolset
- 65. summary: pay attention to: 1. people 2. ideas 3. things cf. USAF
- 66. thanks to the data science team!
- 67. data science @ The New York Times chris.wiggins@columbia.edu chris.wiggins@nytimes.com @chrishwiggins