Uploaded bychris wiggins
data science in academia and the real world
The document discusses the role of a computational biologist at The New York Times and outlines how academia can benefit the company. It emphasizes the importance of practices, skills, and culture in data science, advocating for the reframing of questions as machine learning tasks and fostering communication and skepticism within teams. Additionally, it offers contact information for opportunities and interactions.
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data science in academia and the real world
- 1. what is acomputational biologist doing at the New York Times? ! (and what can academia do for a 163-year old company?) chris.wiggins@columbia.edu chris.wiggins@nytimes.com chris.wiggins@hackNY.org @chrishwiggins
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- 4. biology: 1892 vs.1995 biology changed for good.
- 5. genetics: 1837 vs.2012 from “segments” to algorithms
- 6. genetics: 1837 vs.2012 from intuition to prediction
- 7. data science: webscale
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- 16. example: millions of viewsper hour
- 17. from “segments” toalgorithms insert figure here
- 18. from intuition toprediction insert figure here
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- 20. data science: theweb is your “online presence”
- 21. data science: theweb is a microscope
- 22. data science: theweb is an experimental tool
- 23. data science: theweb is an optimization tool
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- 26. common requirements in datascience:
- 27. common requirements in datascience: 1. practices 2. skills 3. culture
- 28. common requirements in datascience: 1. practices 2. skills 3. culture
- 29. common requirements in datascience: 1. practices 2. skills 3. culture
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- 31. data science: practice -reframe domain questions as machine learning tasks
- 32. data science: practice -better wrong than "nice"
- 33. data science: practice -be relevant !
- 34. data science: practice -be relevant !
- 35. data science: practice -be relevant !
- 36. data science: practice -hypotheses are not data jeopardy !
- 37. data science: practice -befriend experimentalists !
- 38. data science: practice -befriend experimentalists !
- 39. data science: practice -befriend experimentalists !
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- 41. data science: skills -find quantifiables !
- 42. data science: skills -find quantifiables (choose carefully) !
- 43. data science: skills -straw man first !
- 44. data science: skills -straw man first !
- 45. data science: skills -small wins before feature engineering !
- 46. data science: skills -data engineering before data science !
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- 48. data science: culture -be communicative !
- 49. data science: culture -be communicative (promote rhetorical literacy)
- 50. data science: culture -be communicative (promote rhetorical literacy) - related: strive to build models which are both predictive and interpretable
- 51. data science: culture -be skeptical (promote critical literacy)
- 52. data science: culture -be empowering !
- 53. data science: culture -be transparent !
- 54. data science: culture -promote literacy: functional critical rhetorical ! (cf. Selber, Multiliteracies for a Digital Age. 2004)
- 55. data science: culture -promote literacies: 1. functional 2. critical 3. rhetorical ! (cf. Selber, Multiliteracies for a Digital Age. 2004)
- 56. data science: culture -promote literacies: 1. functional 2. critical 3. rhetorical ! (cf. Selber, Multiliteracies for a Digital Age. 2004)
- 57. data science: culture -promote literacies: 1. functional 2. critical 3. rhetorical ! (cf. Selber, Multiliteracies for a Digital Age. 2004)
- 58. data science: culture -promote literacies: 1. functional 2. critical 3. rhetorical ! (cf. Selber, Multiliteracies for a Digital Age. 2004)
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- 61. summary: pay attention to: 1.practices 2. skills 3. culture
- 62. practices: 1. reframe questionsas ML 2. better wrong than "nice" 3. be relevant 4. aim for hypothesis vs data jeapordy 5. befriend experimentalists
- 63. skills: 1. find quantifiables 2. straw man first 3. small wins before feature engineering 4. data engineering before data science !
- 64. culture: 1. be communicative 2.be skeptical 3. be empowering 4. be transparent 5. promote literacies
- 65. find out more! 1.postdoc/student opportunities: chris.wiggins@columbia.edu ! 2. always hiring: chris.wiggins@nytimes.com ! 3. let’s talk: - @chrishwiggins - gist.github.com/chrishwiggins/
- 66. what is acomputational biologist doing at the New York Times? ! (and what can academia do for a 163-year old company?) chris.wiggins@columbia.edu chris.wiggins@nytimes.com chris.wiggins@hackNY.org @chrishwiggins