LeaRning Out Loud, EARL Boston 2017

Slides from talk from talk given at EARL Boston, November 3, 2017

🐦 @dataandme - https://twitter.com/dataandme
📽 full-res - https://bit.ly/mara-earl

Transcript

1. E A R L 2 0 1 7 leaRning out loud
2. Mara Averick
 Data Nerd Plays with data for fun, and, sometimes, profit; almost always using R.
3. Mara Averick
 Data Nerd Plays with data for fun, and, sometimes, profit; almost always using R. Has no fancy letters after name.
4. Mara Averick
 Data Nerd Plays with data for fun, and, sometimes, profit; almost always using R. Has no fancy letters after name. Great at self-portraits. Super nervous.
5. E A R L 2 0 1 7
6. E A R L 2 0 1 7 data scientist
8. E A R L 2 0 1 7 SCIENCE & SOCIETY
9. E A R L 2 0 1 7 “Science may be described as the art of systematic over- simplification.” — Karl Popper
10. “Who let her in?!” • Sometimes I go on Twitter. . .
11. “Who let her in?!” • Sometimes I go on Twitter. . . • I tend to: LEARN OUT LOUD OMG I just learned a thing!! !
12. “Who let her in?!” • Sometimes I go on Twitter. . . • I tend to: LEARN OUT LOUD • This is useful to other people ⁉
13. If you want to make a deep non-technical contribution to the field of data science, I can not think of a better role model than @dataandme. . . E A R L 2 0 1 7
14. If you want to make a deep non-technical contribution to the field of data science, I can not think of a better role model than @dataandme. . . E A R L 2 0 1 7 #
15. E A R L 2 0 1 7 how did this happen?
16. E A R L 2 0 1 7
17. Ethical, Legal & Social Implications of Nanotechnology Credit: NIST Nanotechnology Interdisciplinary Research Team
18. E A R L 2 0 1 7
19. E A R L 2 0 1 7
20. E A R L 2 0 1 7
21. E A R L 2 0 1 7 NIRT × twitter
22. E A R L 2 0 1 7 NIRT × twitter
23. E A R L 2 0 1 7 LUCY TWEET GOES HERE
24. E A R L 2 0 1 7 ex•o•ter•ic adj· understandable by outsiders or the general public
25. E A R L 2 0 1 7 “It is impossible to speak in such a way that you cannot be misunderstood.” — Karl Popper
26. artifact E A R L 2 0 1 7 $
27. artifact E A R L 2 0 1 7 $
28. E A R L 2 0 1 7 data scientist
29. E A R L 2 0 1 7 scientist
30. E A R L 2 0 1 7 Scientist : The Story of a Word “The appellation scientist is considered a title of honour, hotly contended for by economists, engineers, physicians, psychologists, and others.” — Sydney Ross, 1962. Annals of Science
31. william whewell “…by analogy with artist, they might form scientist…there could be no scruple in making free with this termination when we have such words as sciolist, economist, and atheist” W. Whewell. (anonymously) 1834. The Quarterly Review, 51, 58-61. William Whewell. Popular Science
32. E A R L 2 0 1 7 sci•o•list n· a superficial pretender to knowledge
33. thomas h. huxley “To any one who respects the English language, I think ‘Scientist’ must be about as pleasing a word as ‘Electrocution.’ I sincerely trust you will not allow the pages of Science-Gossip to be defiled by it.” Thos. H. Huxley 1894. Letter to J.T. Carrington, editor of Science-Gossip, in Ross 1962.
34. Hardwicke's science-gossip : an illustrated medium of interchange and gossip for students and lovers of nature. London : Robert Hardwicke, 1866- https://www.biodiversitylibrary.org/bibliography/1953
35. Ernest Rutherford Ernest Rutherford at the McGill University in 1905 “…said that scientists were divided into two categories— physicists and stamp collectors” – Daniel Lang, The New Yorker, 1963.
36. E A R L 2 0 1 7 all scientists physicists stamp collectors
37. E A R L 2 0 1 7 physilatelists physicists stamp collectors
38. E A R L 2 0 1 7 pan•chres•ton n· an explanation or theory which can fit all cases, being used in such a variety of ways as to become meaningless
39. (“communalism”) “Mertonian” norms of science 1. Universalism 2. Communism 3. Disinterestedness 4. Organized skepticism E A R L 2 0 1 7
40. E A R L 2 0 1 7 “Is not science a dispassionate recording of facts, uncontaminated by value judgements?” (Ross 1962)
41. E A R L 2 0 1 7 data science-ing library(tidyverse) data <- read_csv(data.csv) model <- fancy_algo(data) model
42. E A R L 2 0 1 7 Mom, where do data come from? SCIENCE
43. E A R L 2 0 1 7 science in context
44. E A R L 2 0 1 7 science in context
45. What does doing science look like? Credit: Smithsonian Institution Archives
46. What does doing science look like?
47. What does doing science look like?
48. What does doing science look like?
49. What does doing science look like? credit: Robin Higgins
50. E A R L 2 0 1 7
51. E A R L 2 0 1 7 socio-technical systems
52. E A R L 2 0 1 7 socio-technical systems complex (Norman & Stappers 2015)
53. E A R L 2 0 1 7 what about R? Oh, I’m super into it!
54. E A R L 2 0 1 7 Oh, I’m super into it!
55. my favorite tools
56. E A R L 2 0 1 7 data wrangling
57. E A R L 2 0 1 7 data wrangling rectangling % Jenny Bryan
58. E A R L 2 0 1 7 data wrangling rectangling idea
59. E A R L 2 0 1 7
60. E A R L 2 0 1 7
61. E A R L 2 0 1 7
62. E A R L 2 0 1 7
63. E A R L 2 0 1 7
64. E A R L 2 0 1 7 not very tidy &
65. E A R L 2 0 1 7 not very tidy &
66. E A R L 2 0 1 7 OMG I just learned a thing!! !
67. E A R L 2 0 1 7 data scientist http://bit.ly/mara-earl
68. works cited • Park, Dong Huk, Lisa Anne Hendricks, Zeynep Akata, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach. 2016. “Attentive Explanations: Justifying Decisions and Pointing to the Evidence.” arXiv, <https://arxiv.org/abs/1612.04757> • Ross, Sydney. 1962. “Scientist: The Story of a Word.” Annals of Science 18 (2): 65–85. doi:10.1080/00033796200202722 • Merton, Robert K. (1973) [1942]. “The Normative Structure of Science.” In The Sociology of Science: Theoretical and Empirical Investigations, 267–78. University of Chicago Press (1979). ' • Wammes, Jeffrey D., Melissa E. Meade, and Myra A. Fernandes. 2016. “The Drawing Effect: Evidence for Reliable and Robust Memory Benefits in Free Recall.” Quarterly Journal of Experimental Psychology 69 (9): 1752–76. • Traweek, Sharon. 1988. Beamtimes and Lifetimes: The World of High Energy Physics. Cambridge, MA: Harvard University Press. • Latour, Bruno, and Steve Woolgar. 1979. Laboratory Life: The Construction of Scientific Facts. Beverly Hills: Sage Publications. • Norman, Donald A. 2002. The Design of Everyday Things. Reprint. New York, NY, USA: Basic Books, Inc. • Norman, Donald A., and Pieter Jan Stappers. 2015. “DesignX: Complex Sociotechnical Systems.” She Ji: The Journal of Design, Economics, and Innovation 1 (2): 83–106. doi:10.1016/j.sheji.2016.01.002 • NIST image: This work is in the public domain in the United States because it is a work prepared by an officer or employee of the United States Government as part of that person’s official duties under the terms of Title 17, Chapter 1, Section 105 of the US Code. See Copyright. E A R L 2 0 1 7
69. ( complex? • Markoski, Branko. 2012. “Using Neural Networks in Preparing and Analysis of Basketball Scouting.” Data Mining Applications in Engineering and Medicine, 109–32. doi:10.5772/48178. • Bruce, Scott. 2015. “Evaluating the Importance of Statistical Diversity in the NBA Using Player Tracking Data.” Journal of Sports Analytics. <https://arxiv.org/pdf/1511.04351.pdf> • Fewell, Jennifer H, Dieter Armbruster, John Ingraham, Alexander Petersen, and James S Waters. 2012. “Basketball Teams as Strategic Networks.” PLoS ONE 7 (11). doi:10.1371/journal.pone.0047445. • de Saá Guerra, Yves, Juan Manuel Martín Gonzalez, Samuel Sarmiento Montesdeoca, David Rodriguez Ruiz, Nieves Arjonilla López, and Juan Manuel García-Manso. 2013. “Basketball Scoring in NBA Games: An Example of Complexity.” Journal of Systems Science and Complexity 26 (AUGUST): 94–103. doi:10.1007/s11424-013-2282-3. • Clemente, Filipe Manuel, Fernando Martins, Dimitris Kalamaras, Rui Mendes, Fernando Manuel, and Lourenço Martins. 2015. “Network Analysis in Basketball: Inspecting the Prominent Players Using Centrality Metrics.” Journal of Physical Education and Sport 15 (2): 212–17. doi:10.7752/jpes.2015.02033. • Kohli, Ikjyot Singh. 2015. “On Optimal Offensive Strategies in Basketball.” arXiv, 1–8. <https://arxiv.org/abs/1506.06687> E A R L 2 0 1 7