This document discusses cultivating creativity in data work. It argues that data science involves both analysis (Type A) and building (Type B). It emphasizes learning theory, mastering tools/code, and composing narratives to convey insights. Errors are viewed as system failures, not personal faults. Design methods like interviews and prototyping can be applied to data problems through "data design sprints". Empathy and creative delivery are important for success beyond just scientific methods.

1. CULTIVATING CREATIVITY
IN DATA WORK
HILARY PARKER

2. WOMEN IN ANALYTICS CONFERENCE
ABOUT ME
▸ Data Scientist at Stitch Fix
▸ Formerly a Data Analyst at Etsy
▸ #rcatlady
▸ Co-host of “Not So Standard Deviations”
podcast

3. DATA WORK

4. WHAT IS A DATA
SCIENTIST?

6. TYPE A
VS.
TYPE B
https://www.linkedin.com/pulse/data-science-from-b-michael-hochster/

7. TYPE A: ANALYSIS
https://www.linkedin.com/pulse/data-science-from-b-michael-hochster/

8. BE AN ARBITER OF A SET OF FACTS AND
PROVIDE CONTEXT INTO HOW TO UNDERSTAND
THESE FACTS
Jonathan Lisic (NSSD listener)

9. TYPE B: BUILDING
https://www.linkedin.com/pulse/data-science-from-b-michael-hochster/

10. WHAT IS AN ANALYSIS?

11. TRUTH
(WITH A CAPITAL T)

12. THEORY
(HOW DO YOU ACCOUNT FOR UNCERTAINTY?)

13. DELIVERABLE
(MEDIUM FOR THE MESSAGE)

14. NARRATIVE
(HOW DO YOU CONVEY THE TRUTH?)

16. EMOTION

17. MUSIC THEORY

18. INSTRUMENT

19. COMPOSITION

20. :: EMOTION
:: MUSIC THEORY
:: INSTRUMENT
:: COMPOSITION
TRUTH
THEORY
DELIVERABLE
NARRATIVE

21. LEARN THE THEORY

22. THE STUDY OF
UNCERTAINTY

23. https://www.flickr.com/photos/lordsutch/5863912831

24. DISTRIBUTION OF THE MEAN
▸ Central Limit Theorem
▸ Mean / standard error is normally distributed

25. T-DISTRIBUTION
▸ Gosset empirically observed
distributions that were not
normal

27. THEORY
▸ Third person perspective / observational science

28. THEORY
▸ Third person perspective / observational science
▸ Proofs for properties of statistical tests and phenomena

29. THEORY
▸ Third person perspective / observational science
▸ Proofs for properties of statistical tests and phenomena
▸ Empirically explored

30. THEORY
▸ Third person perspective / observational science
▸ Proofs for properties of statistical tests and phenomena
▸ Empirically explored
▸ Provides specific statements that can be used to construct the analysis
narrative.

31. MASTER THE
INSTRUMENT

32. ACHIEVE FLUENCY

33. https://twitter.com/alice_data/status/748200882043396096

34. 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
TEXT
https://statr.me/2013/09/a-conversation-with-hadley-wickham/

35. TEXT
FLUENCY
▸ Fluency in statistical code
▸ Fluency in deliverable creation

36. ERRORS

38. BLAMELESS POSTMORTEM
▸ Rather than viewing errors as “human error” / person making a mistake, view
them as the system failing a person with good intentions

39. BLAMELESS POSTMORTEM
▸ Rather than viewing errors as “human error” / person making a mistake, view
them as the system failing a person with good intentions
▸ Adapt the system so that it does not fail a person with good intentions

40. COMMON ERRORS (THAT HURT FLUENCY)
▸ You re-run the analysis and get different results

41. COMMON ERRORS (THAT HURT FLUENCY)
▸ You re-run the analysis and get different results
▸ Your data becomes corrupted, but you don’t notice

42. COMMON ERRORS (THAT HURT FLUENCY)
▸ You re-run the analysis and get different results
▸ Your data becomes corrupted, but you don’t notice
▸ Requests and questions from your collaborators get lost

43. COMMON ERRORS (THAT HURT FLUENCY)
▸ You re-run the analysis and get different results
▸ Your data becomes corrupted, but you don’t notice
▸ Requests and questions from your collaborators get lost
▸ …

44. DELIVERABLE
▸ Also a third-person / observational science

45. DELIVERABLE
▸ Also a third-person / observational science
▸ Observed fluency (time-to-deliverable)

46. DELIVERABLE
▸ Also a third-person / observational science
▸ Observed fluency (time-to-deliverable)
▸ Observed error rate

47. Data science
as a
Science
(DSaaS) @jtleek

48. COMPOSE THE
NARRATIVE

49. CONVEY THE
TRUTH

50. WHAT IS SUCCESS?

51. A-HA

52. THE A-HA MOMENT
▸ Observable only from the first-person perspective

53. THE A-HA MOMENT
▸ Observable only from the first-person perspective
▸ Third person observers can only rely on accounts

54. 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

55. SCIENTIFIC METHOD AS ARGUMENT

56. SCIENTIFIC METHOD AS ARGUMENT
▸ “Gold standard” for
inferring the Truth

57. SCIENTIFIC METHOD AS ARGUMENT
▸ “Gold standard” for
inferring the Truth
▸ Method of convincing
that is effective for many
(but not all)

58. IS THIS ENOUGH?
(NO)

59. COMMON ADVICE
▸ “Think about your audience”
▸ “Build good partnerships”
▸ “Be a good communicator”
▸ …

60. EMPATHY

61. DESIGN

62. 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, Designerly Ways of Knowing

63. DESIGN METHODS
▸ Interviewing
▸ Creating user profiles
▸ Looking at other existing solutions
▸ Creating prototypes
▸ …

66. DATA DESIGN SPRINTS

67. DATA DESIGN SPRINTS
▸ How do we design a study to understand our customers better?

68. DATA DESIGN SPRINTS
▸ How do we design a study to understand our customers better?
▸ How do we design a plan for developing a new feature for our website?

69. DATA DESIGN SPRINTS
▸ How do we design a study to understand our customers better?
▸ How do we design a plan for developing a new feature for our website?
▸ How do we incorporate new data from users into multiple downstream use-
cases?

72. ▸ Consensus on:
▸ Architecture of problem
▸ First wave of statistical approaches
▸ Next steps (and ownership of these steps)

73. SOME RESOURCES
▸ Designing Your Life
▸ Articulating Design Decisions
▸ Statistics as Principled Argument

74. TRUTH
THEORY
DELIVERABLE
NARRATIVE

75. THANKS!