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ICOTS 2018

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My keynote at the International Conference on Teaching Statistics. I discuss the roles of art, science and design in the practice of data science.

Published in: Data & Analytics

ICOTS 2018

  1. 1. CULTIVATING CREATIVITY IN DATA WORK HILARY PARKER
  2. 2. CULTIVATING CREATIVITY IN DATA WORK
  3. 3. You’ve told me everything NOT to do, but how will I know what to do? Anonymous Roger Peng student
  4. 4. http://r4ds.had.co.nz/explore-intro.html
  5. 5. “Data Science is an art”
  6. 6. https://leanpub.com/artofdatascience “Data analysis is hard, and part of the problem is that few people can explain how to do it. It’s not that there aren’t any people doing data analysis on a regular basis. It’s that the people who are really good at it have yet to enlighten us about the thought process that goes on in their heads.”
  7. 7. https://www.slideshare.net/ElsevierConnect/zen-and-the-art-of-data-science-maintenance/1
  8. 8. https://www.slideshare.net/ElsevierConnect/zen-and-the-art-of-data-science-maintenance/1
  9. 9. “The demand for this “right” brain thinking is increasing and in era of increased automation, the need for the “art” of data science will be the increasing cry of business.”
  10. 10. If data science is an art, why don’t we teach it like art?
  11. 11. MUSIC THEORY :: INSTRUMENT :: COMPOSITION :: THEORY PROGRAMMING LANGUAGE NARRATIVE
  12. 12. My experience of the field
  13. 13. Theory
  14. 14. Theory
  15. 15. Programming Language
  16. 16. We may find that the two bottlenecks are what you want to do, and how you tell the computer to do that. A lot of my existing work…has been more about how to make it easier to express what you want. Hadley Wickham https://statr.me/2013/09/a-conversation-with-hadley-wickham/
  17. 17. Teach students tools that allow for fluency
  18. 18. Programming Language
  19. 19. Narrative
  20. 20. Narrative
  21. 21. My experience in tech
  22. 22. Product Development
  23. 23. Design sprints for data science
  24. 24. Design as a Discipline
  25. 25. Design ability is, in fact, one of the three fundamental dimensions of human intelligence. Design, science, and art form an ‘and’ not an ‘or’ relationship to create the incredible human cognitive ability.” Nigel Cross
  26. 26. Design ability is often treated as “mythical” and a “mysterious talent”
  27. 27. https://leanpub.com/artofdatascience “Data analysis is hard, and part of the problem is that few people can explain how to do it. It’s not that there aren’t any people doing data analysis on a regular basis. It’s that the people who are really good at it have yet to enlighten us about the thought process that goes on in their heads.”
  28. 28. Design process is solution-focused, versus problem-focused
  29. 29. You’ve told me everything NOT to do, but how will I know what to do? Anonymous Roger Peng student
  30. 30. Design is constructive thinking -- you need to start building solutions to understand the problem fully
  31. 31. Design process employs both the left-brain and the right-brain.
  32. 32. “The demand for this “right” brain thinking is increasing and in era of increased automation, the need for the “art” of data science will be the increasing cry of business.”
  33. 33. Though the field did have a “scientific design” movement in the 60s, it has mostly moved on from cookbook methods
  34. 34. Gosset to Pearson in 1905 From “Guinnessometrics: The Economic Foundation of “Student’s” t” by Stephen T. Ziliak
  35. 35. Design is a form of non-verbal rhetoric, with sketching as the language
  36. 36. One thing that is clear is that sketches enable designers to handle different levels of abstraction simultaneously… Clearly this is something important in the design process. We see that designers think about the overall concept and at the same time think about detailed aspects of the implementation of that concept. Nigel Cross
  37. 37. http://r4ds.had.co.nz/explore-intro.html
  38. 38. Teaching Design
  39. 39. Teach design as a fundamental process that is independent of theory Borrow on design school curricula Employ design methods such as design sprints
  40. 40. The Role of Art?
  41. 41. What is success in Data Science / Analysis?
  42. 42. A-HA
  43. 43. A data analysis is successful if the audience to which it is presented accepts the results. Roger Peng https://simplystatistics.org/2018/04/17/what-is-a-successful-data-analysis/
  44. 44. THE A-HA MOMENT ▸ Observable only from the first-person perspective
  45. 45. THE A-HA MOMENT ▸ Observable only from the first-person perspective ▸ Third person observers can only rely on accounts
  46. 46. THE A-HA MOMENT ▸ Observable only from the first-person perspective ▸ Third person observers can only rely on accounts ▸ People are unreliable about communicating their experiences
  47. 47. COMMON ADVICE ▸ “Think about your audience” ▸ “Build good partnerships” ▸ “Be a good communicator” ▸ …
  48. 48. Empathy
  49. 49. Empathy“the capacity to understand or feel what another person is experiencing from within their frame of reference, i.e., the capacity to place oneself in another's position.” https://en.wikipedia.org/wiki/Empathy
  50. 50. Design sprints (and other design processes) People are most willing to share when they don’t feel judged Listening to and understanding stakeholders is a key part of creating solutions for them
  51. 51. Design sprints (and other design processes) Most have rules for “playing nice” ▸ Non-judgmental observation ▸ Ideas originate from stakeholder interviews ▸ Require listening to and validating other people’s opinions
  52. 52. Cultivating empathy
  53. 53. My experience with Zen Buddhism
  54. 54. San Francisco Zen Center
  55. 55. Zazen Central practice of Zen Buddhism
  56. 56. Zazen Central practice of Zen Buddhism “Sitting meditation”
  57. 57. Zazen Central practice of Zen Buddhism “Sitting meditation” Cultivating the ability to observe yourself non-judgmentally, and with curiosity
  58. 58. Zazen Central practice of Zen Buddhism “Sitting meditation” Cultivating the ability to observe yourself non-judgmentally, and with curiosity Acceptance practice
  59. 59. Zazen Cultivate this acceptance with yourself, and it will increase your capacity to observe, accept, and be connected to others.
  60. 60. Empathy / “Art” in Data Science Accepting the audience where they are right now -- their educational context, their biases, their motivations
  61. 61. Empathy / “Art” in Data Science Accepting the audience where they are right now -- their educational context, their biases, their motivations Accepting yourself where you are right now -- your biases, your preferences, your blind spots
  62. 62. Empathy / “Art” in Data Science Accepting the audience where they are right now -- their educational context, their biases, their motivations Accepting yourself where you are right now -- your biases, your preferences, your blind spots Employing modes of communication other than scientific communication
  63. 63. In conclusion:
  64. 64. Data science includes all three forms of human cognition: science, art, and design
  65. 65. Data science includes all three forms of human cognition: science, art, and design Design research can be leveraged for teaching students the “how” of data science
  66. 66. Data science includes all three forms of human cognition: science, art, and design Design research can be leveraged for teaching students the “how” of data science The “art” of data science is the skilled use of empathy, which can be cultivated
  67. 67. THANKS!
  68. 68. Appendix: Resources
  69. 69. Designerly Ways of Knowing / Design Thinking (Nigel Cross) Nigel Cross is one of the primary academics working on “Design as a Discipline”. He is also a superb writer, and writes about the field in a very accessible way. I read Designerly Ways of Knowing, however I am told that Design Thinking is very similar content. Additionally, each chapter of Designerly Ways of Knowing is an article, so it is possible to get access to those without purchasing the book.
  70. 70. Sprint (Jake Knapp) This book outlines the design sprint process, and has several case studies of sprints working (and not working) at various startups. It’s an enjoyable quick read that introduces one structured approach to design thinking.
  71. 71. Designing Your Life (Bill Burnett and Dave Evans) This book is also taught as a very popular class from the Stanford d.school. I found it extremely helpful for establishing a “design mindset” in a relatable way. Additionally, I think there is added benefit to helping students view their academic / work life in a non-judgmental way. I highly recommend introducing this to students, even though it is not related to statistics per se.
  72. 72. Articulating Design Decisions (Tom Greever) This book would be most helpful for students who want to enter the tech industry, but would also be helpful for everyone. It outlines the different contexts and motivations that people in various roles within the tech industry might have (e.g. CEOs are very results-driven rather than problem-driven). The applicability of this book to data science underscores how conceptually similar the fields of design and data science are.
  73. 73. The Art of Data Science (Roger Peng and Elizabeth Matsui) This book discusses the “how” of doing data science, and includes several examples. Roger and Elizabeth are great writers, and it is a fun and accessible read!
  74. 74. Statistics as Principled Argument (Robert Abelson) This book approaches statistics in a “design thinking” way. I don’t have as many comments as I haven’t dug into it, but I am intrigued by people in the field approaching statistics as rhetoric / argument!
  75. 75. The Field Guide to Understanding Human Error (Sidney Dekker) I didn’t talk about Blameless Postmortems in this talk, but have covered them in previous presentations (one, two) and in the paper “Opinionated Analysis Development”. Blameless Postmortems present another structured, non-judgmental paradigm shift for designing processes (versus products). I find them extremely useful for discussing statistical tools such as programming language choices, and think they would be another very valuable thing to teach to introductory students.
  76. 76. Stitch Fix Data Science Some folks on the Analytics & Algorithms team at Stitch Fix created an interactive visualization of the various ways that we use data science at the company. Some teachers have found it helpful to use Stitch Fix as an example of applied statistics in a non-traditional field, and I have to say I’m quite supportive of this! =)
  77. 77. 10% Happier (Dan Harris) This is a very accessible introduction to meditation from a news anchor who came to it in a very skeptical way. I found it be extremely relatable and a great introduction to meditation and the various communities (including the Zen Buddhist community) who practice meditation.
  78. 78. San Francisco Zen Center I live and practice with my partner at the San Francisco Zen Center. They have a number of online programs (including an “online zendo” -- practicing meditation in a group on a Zoom video chat!). This is a great resource if you are interested in Soto Zen Buddhist community and practice.

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