data science in academia and the real world
•
1 gostou•2,449 visualizações
talk given 2014-04-29 to the New York Open Statistical Programming Meetup (http://www.meetup.com/nyhackr/) by Chris Wiggins (columbia/NYT/hackNY)
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Destaque
Destaque (20)
Semelhante a data science in academia and the real world
Semelhante a data science in academia and the real world (20)
Mais de chris wiggins
Mais de chris wiggins (16)
Último
Último (20)
data science in academia and the real world
- 1. what is a computational 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
- 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: web scale
- 10. R+D: nytlabs.com
- 16. example: millions of views per hour
- 17. from “segments” to algorithms insert figure here
- 18. from intuition to prediction insert figure here
- 19. data science: the web
- 20. data science: the web is your “online presence”
- 21. data science: the web is a microscope
- 22. data science: the web is an experimental tool
- 23. data science: the web is an optimization tool
- 24. </header>
- 26. common requirements in data science:
- 27. common requirements in data science: 1. practices 2. skills 3. culture
- 28. common requirements in data science: 1. practices 2. skills 3. culture
- 29. common requirements in data science: 1. practices 2. skills 3. culture
- 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 !
- 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 !
- 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)
- 60. summary:
- 61. summary: pay attention to: 1. practices 2. skills 3. culture
- 62. practices: 1. reframe questions as 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 a computational 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