leaRning out loud - Analytics>Forward edition

Similar to my talk from EARL Boston 2017, but with some updates since I joined RStudio.
PDF with links and resources available at: http://bit.ly/analytics_fwd

Transcript

1. M A R A A V E R I C K leaRning out loud
2. a n a l y t i c s > f o r w a r d 2 This Talk Will Not Cover
3. A N A L Y T I C S > F O R W A R D a•po•ri•a n·patently insincere professings (e.g. by a public speaker) of an inability to know where to begin or what to say
4. a n a l y t i c s > f o r w a r d 5 Mara Averick TIDYVERSE DEV ADVOCATE, RSTUDIO Not a Real Data Scientist™
5. a n a l y t i c s > f o r w a r d 5 Mara Averick TIDYVERSE DEV ADVOCATE, RSTUDIO
6. A N A L Y T I C S > F O R W A R D data scientist
7. A N A L Y T I C S > F O R W A R D SCIENCE & SOCIETY
8. Anthropology of Science In Beamtimes and Lifetimes: The World of High Energy Physics. (1988). Cambridge, MA: Harvard University Press. “Like many social groups that do not reproduce themselves biologically, the experimental particle physics community renews itself by training novices.” — Sharon Traweek, Pilgrim's Progress: Male Tales Told During a Life in Physics, 1988 8
9. Philosophy of Science 9 “Science may be described as the art of systematic over-simplification.” — Sir Karl Popper, The Logic of Scientific Discovery, 1934
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. . . A N A L Y T I C S > F O R W A R D
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. . . A N A L Y T I C S > F O R W A R D #
15. A N A L Y T I C S > F O R W A R D how did this happen?
16. A N A L Y T I C S > F O R W A R D
17. Ethical, Legal & Social Implications of Nanotechnology Credit: NIST Nanotechnology Interdisciplinary Research Team
18. A N A L Y T I C S > F O R W A R D
19. A N A L Y T I C S > F O R W A R D
20. A N A L Y T I C S > F O R W A R D NIRT × twitter
21. A N A L Y T I C S > F O R W A R D NIRT × twitter
22. A N A L Y T I C S > F O R W A R D LUCY TWEET GOES HERE
23. A N A L Y T I C S > F O R W A R D ex•o•ter•ic adj· understandable by outsiders or the general public
24. A N A L Y T I C S > F O R W A R D “It is impossible to speak in such a way that you cannot be misunderstood.” — Karl Popper
25. artifact A N A L Y T I C S > F O R W A R D
26. artifact A N A L Y T I C S > F O R W A R D $
27. artifact A N A L Y T I C S > F O R W A R D $
28. A N A L Y T I C S > F O R W A R D data scientist
29. A N A L Y T I C S > F O R W A R D data scientist
30. A N A L Y T I C S > F O R W A R D scientist
31. A N A L Y T I C S > F O R W A R D 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
32. 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
33. A N A L Y T I C S > F O R W A R D sci•o•list n· a superficial pretender to knowledge
34. 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. Science-Gossip
35. 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
36. 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.
37. A N A L Y T I C S > F O R W A R D all scientists physicists stamp collectors
38. A N A L Y T I C S > F O R W A R D physilatelists physicists stamp collectors
39. A N A L Y T I C S > F O R W A R D 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
40. 1. Universalism 2. Communism 3. Disinterestedness 4. Organized skepticism “Mertonian” norms of science A N A L Y T I C S > F O R W A R D
41. 1. Universalism 2. Communism 3. Disinterestedness 4. Organized skepticism (“communalism”) “Mertonian” norms of science A N A L Y T I C S > F O R W A R D
42. A N A L Y T I C S > F O R W A R D (Ross 1962) “Is not science a dispassionate recording of facts, uncontaminated by value judgements?”
43. A N A L Y T I C S > F O R W A R D data science-ing library(tidyverse) data <- read_csv(data.csv) model <- fancy_algo(data) model
44. A N A L Y T I C S > F O R W A R D Mom, where do data come from?
45. A N A L Y T I C S > F O R W A R D Mom, where do data come from? SCIENCE
46. A N A L Y T I C S > F O R W A R D science in context
47. A N A L Y T I C S > F O R W A R D science in context
48. What does doing science look like? Credit: Smithsonian Institution Archives
49. A N A L Y T I C S > F O R W A R D
50. socio-technical systems A N A L Y T I C S > F O R W A R D
51. A N A L Y T I C S > F O R W A R D socio-technical systems complex (Norman & Stappers 2015)
52. A N A L Y T I C S > F O R W A R D what about R? Oh, I’m super into it!
53. A N A L Y T I C S > F O R W A R D Oh, I’m super into it!
54. my favourite tools
55. A N A L Y T I C S > F O R W A R D data wrangling
56. A N A L Y T I C S > F O R W A R D data wrangling rectangling % Jenny Bryan
57. A N A L Y T I C S > F O R W A R D data wrangling rectangling idea
58. A N A L Y T I C S > F O R W A R D
59. A N A L Y T I C S > F O R W A R D
60. A N A L Y T I C S > F O R W A R D
61. A N A L Y T I C S > F O R W A R D
62. A N A L Y T I C S > F O R W A R D
63. A N A L Y T I C S > F O R W A R D not very tidy &
64. A N A L Y T I C S > F O R W A R D not very tidy &
65. a n a l y t i c s > f o r w a r d sharing untidy efforts... 50
66. a n a l y t i c s > f o r w a r d sharing untidy efforts... 50
67. a n a l y t i c s > f o r w a r d sharing untidy efforts... 50 100% selfish
68. a n a l y t i c s > f o r w a r d but... 51
69. a n a l y t i c s > f o r w a r d things that are selfish things that are useful to other people but... 52 things that are selfish things that are useful to other people
70. a n a l y t i c s > f o r w a r d things that are selfish things that are useful to other people but... 52 things that are selfish things that are useful to other people FOSS happy place
71. a n a l y t i c s > f o r w a r d outcomes... 53
72. a n a l y t i c s > f o r w a r d outcomes... 53 Elijah Meeks, also valuable…
73. a n a l y t i c s > f o r w a r d outcomes... 54 made with tidytext
74. a n a l y t i c s > f o r w a r d outcomes... 54
75. a n a l y t i c s > f o r w a r d outcomes... 55
76. a n a l y t i c s > f o r w a r d outcomes... 55 experts need n00bs
77. a n a l y t i c s > f o r w a r d ‣ Read (or run) ‣ Relatable ‣ Retrievable ‣ Relevant ‣ Real tweet Rs 56 * completely arbitrary rules I made up you do you '(!
78. A N A L Y T I C S > F O R W A R D data scientist
79. a n a l y t i c s > f o r w a r d classic misdirection? 58
80. you never know... 59
81. you never know... 59
82. a n a l y t i c s > f o r w a r d Thank You
83. a n a l y t i c s > f o r w a r d http://bit.ly/analytics_fwd Thank You
84. 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 o#cer or employee of the United States Government as part of that person’s o#cial duties under the terms of Title 17, Chapter 1, Section 105 of the US Code. See Copyright. A N A L Y T I C S > F O R W A R D
85. * 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> A N A L Y T I C S > F O R W A R D