the slides

2. Visualization
& Storytelling Workshop
Jose Berengueres † Mariam AlMheiri
CIT, UAEU, Al Ain, AD, UAEU, CIT
CHSS, UAEU, Al Ain, AD, UAEU
Dubai Sep 14th 2019 – Careem

3. Agenda
• Good chart
• bad chart
• How to use charts to think++

4. Schedule
13:30 Intro
13:40 (1) Making sense of data
14:10 break
14:20 (2) Communicating w/ charts
14:50 break
15:00 (3) Strategy Mapping

5. Schedule
13:30 Intro
13:40 (1) Making sense of data
14:10 break
14:20 (2) Communicating w/ charts
14:50 break
15:00 (3) Strategy Mapping

6. data
wisdom
information
knowledge
Can you order this?

7. data > information > kn > wisdom
1 What is data?

8. data > information > kn > wisdom
many --------------------> one
many ----------------------> scarce ?
-----------------------------> value arrow ?
1 What is data?

9. data > information > kn > wisdom
many --------------------> one
many ----------------------> scarce ?
-----------------------------> value?
1 What is data?

10. data > information > kn > wisdom
1 What is data?

11. data > HOW ? > wisdom
2 How to arrive to wisdom?

12. data > HOW ? > wisdom
2 How to arrive to wisdom?
Synthesis process: the dialectic combination of thesis and antithesis into a
higher stage of truth

13. 3 Exercise! Match the words
data
information
kn
wisdom
NO!

14. data
information
kn
wisdom
3 Exercise! Match the words

15. data
information
kn
wisdom
?
3 Exercise! Match the words
Reflection:
Wisdom not just a summary, it
is something more. Context?
how to?

16. 4 Exercise! synthesise (the meaning) of the last 10 slides
synthesis: ______________________________

17. 5 Lets draw it

18. SHARE YOUR SOLUTIONS

19. Solution A

20. Solution B
wisdom
info
data

21. A vs B
kn
info
data

22. 6 Exercise! Visualize the following Gender statistic
Of all the 23,859 respondents
of the 2018 kaggle data science survey 4,513
said they were female.

23. Solutions

24. Solutions
pie chart ?
bar chart ?
stack bar ?
vertical vs. horizontal?
isomeasure?
personas?

25. Solutions

26. Solutions

27. Solutions

28. Solutions

29. Solutions

30. Solutions

31. 7 Exercise! Visualize this
A goal of gender parity has been set. How would you visualize it?

32. Solutions

33. Solutions

34. Solutions

35. 8 Stories Naratives Data

36. 8 Stories Naratives Data

37. 8 Stories Naratives Data

38. 8 Stories Naratives Data

39. 8 Stories Naratives Data

41. Flashback!
Synthesis process: the dialectic combination of thesis and antithesis into a
higher stage of truth (is this same as adding context?)

42. 9 Execisie! is this data, information or knowledge?
https://www.kaggle.com/paultimothymooney/2018-kaggle-machine-learning-data-science-survey

43. 10 Execisie! Summarizing vs. mk meaning – #HMW make more meaning?

44. Solutions
https://www.kaggle.com/harriken/storytelling-the-2018-kaggle-survey#cohorts

45. 11 Execisie! Salary distribution of data scientists...
#HMW make more meaning?
https://www.kaggle.com/headsortails/what-we-do-in-the-
kernels-a-kaggle-survey-story/report

46. How to use gravity to convey power?
< -- ? -- >

47. Solutions
https://www.kaggle.com/harriken/storytelling-the-2018-kaggle-survey#cohorts

48. Solutions
https://www.kaggle.com/harriken/storytelling-the-2018-kaggle-survey#cohorts

49. Solutions
https://www.kaggle.com/harriken/storytelling-the-2018-kaggle-survey#cohorts

50. Solutions
https://www.kaggle.com/harriken/storytelling-the-2018-kaggle-survey#cohorts

51. 12 Data scientist by country
#HMW more meaning?

52. hint!

53. Solutions

54. hint!

55. Solutions

58. Recap part 1

59. Schedule
13:30 Intro
13:40 (1) Making sense of data
14:10 break
14:20 (2) Communicating w/ charts
14:50 break
15:00 (3) Strategy Mapping

60. 12 Cultural locales
• color meaning by country
• color awarenes by gender
• color awarenss by profession
• sexism awareness by country
• Right to left languages

61. 13 Storytelling patterns
Patterns of succesful storytelling
• A/B (testing, advertising)
• What is... What could be (duarte)
• Aha moment! (Welch)
• Heroes journey (duarte)

63. 14 Storytelling patterns in charts: symbolic charts
1. Winners
2. Visualizing ALL OR NOTHING relationships
3. Expressing Enormity
4. Empathy & Personas
5. Log vs. Volumetric charts

64. 15 Most popular viz libs HMW make meaning?

65. hint!

66. Solutions

67. 16 Visualizing ALL OR NOTHING relationships.
Exercise! HMW viz this?

68. Solutions

69. 17 Expressing enormity. Exercise! How would you express the follwowing..
• # of chart in the universe 102,023,342,012
• # of those charts which are great 9,993

70. Expressing enormity
Solutions

71. Expressing enormity

72. Expressing enormity

73. 18 Gender distribution in data scientist. HMW empathise this chart?

74. Solutions

75. 19 Age bias in arrests in USA. Exercise! HMW empathise this chart?
https://www.kaggle.com/harriken/police-dogs-and-grey-hair-will-save-you-from-jail

76. Solutions

77. 19 Gender & Violence in in arrests in USA. Exercise! HMW empathise this
chart?
https://www.kaggle.com/harriken/police-dogs-and-grey-hair-will-save-you-from-jail

78. Solutions

79. 20 Log vs. Volumetric charts. Exercise! HMW viz this?
$ bn
All 83000
Stocks 66000
Physical 31000
Gold 8200
USD 1500
Apple 730
Amzn 400
Crypto 100
Bill 96
Page 41
BTC 41

80. 20 Log vs. Volumetric charts

84. Schedule
13:30 Intro
13:40 (1) Making sense of data
14:10 break
14:20 (2) Communicating w/ charts
14:50 break
15:00 (3) Strategy Mapping

85. Wardley Maps – atool
to map strategy for
visual ppl

86. thank you

90. Story telling?
- What is storytelling?
- Why does it work?
- culture, folklore, and now science
- Why is it addicitve?

91. Exercise
- Try to remember the following sequence
of facts A , B, C
- Make a story
- A killed C with B
- remember test.
(brain is essy to remmebr relations, not data)

92. Narrative
data > stories > NArrative
Facts > methods > beliefs

93. case 1
data > stories > NArrative
Facts > methods > beliefs

94. case 2
data > stories > NArrative
Facts > methods > beliefs

95. case 2 Scatters: mean in your sector vs
you – mean
https://www.mckinsey.com/business-functions/strategy-and-
corporate-finance/our-insights/enduring-ideas-the-7-s-
framework

96. case 2 Scatters: mean in your sector vs
you – mean
https://www.mckinsey.com/business-functions/strategy-and-
corporate-finance/our-insights/enduring-ideas-the-7-s-
framework

97. case 2 Scatters: mean in your sector vs
you – mean
https://www.mckinsey.com/business-functions/strategy-and-
corporate-finance/our-insights/enduring-ideas-the-7-s-
framework

98. case 2 Scatters: mean in your sector vs
you – mean
https://www.mckinsey.com/business-functions/strategy-and-
corporate-finance/our-insights/enduring-ideas-the-7-s-
framework

99. case 2 Scatters: strategic control
https://www.mckinsey.com/business-functions/strategy-and-
corporate-finance/our-insights/enduring-ideas-the-7-s-
framework

100. case 2 Scatters: RFC
https://www.mckinsey.com/business-functions/strategy-and-
corporate-finance/our-insights/enduring-ideas-the-7-s-
framework

101. [1] https://www.aauw.org/research/solving-the-equation/
[2] https://www.theverge.com/2018/11/2/18057716/google-walkout-20-thousand-employees-ceo-sundar-pichai-meeting
[3] https://www.forbes.com/sites/womensmedia/2017/08/03/breaking-down-the-gender-gap-in-data-science/#129d1bb74287
[4] https://www.kaggle.com/paultimothymooney/2018-kaggle-machine-learning-data-science-survey
[5] https://en.wikipedia.org/wiki/Generations_in_the_workforce
[6] Sinton, E (2011). ‘Baby boomers are very privileged human beings’ https://www.telegraph.co.uk/finance/personalfinance/pensions/8840963/Baby-
boomers-are-very-privileged-human-beings.html retrieved October 23, 2013 from www.telegraph.co.uk
[7] Ken Blanchard Companies. (2009). Next Generation of
workers. http://www.kenblanchard.com/img/pub/Blanchard_Next_Generation_of_Workers.pdf Retrieved October 14, 2013, from kenblanchard.com
[8] Adecco Group UK and Ireland. (n.d.). Managing the modern workforce. http://www.adeccogroupuk.co.uk/SiteCollectionDocuments/Adecco-Group-
Workplace-Revolution.pdf Retrieved October 13, 2013, from www.Adeccouk.co.uk
ref. needed
[10] https://en.wikipedia.org/wiki/Affluence_in_the_United_States
[11] https://www.epi.org/blog/top-1-0-percent-reaches-highest-wages-ever-up-157-percent-since-1979/
[12] J. Berengueres, Sketch thinking. 2016
[13] https://en.wikipedia.org/wiki/Marimekko#Marimekko_chart
[14] ref. needed
[15] https://www.kaggle.com/ash316/kaggle-journey-2017-2018
[16] https://en.wikipedia.org/wiki/BRICS
[17] https://www.kaggle.com/harriken/brics-growth
[18] See primary vs. secondary color in https://material.io/design/color/the-color-system.html#color-theme-creation
[19] Dutta, S., Reynoso, R.E., Garanasvili, A., Saxena, K., Lanvin, B., Wunsch-Vincent, S., León, L.R. and Guadagno, F., 2018. THE GLOBAL INNOVATION
INDEX 2018: ENERGIZING THE WORLD WITH INNOVATION. GLOBAL INNOVATION INDEX 2018, p.1.
[20] CSV file global innovation in https://www.globalinnovationindex.org/analysis-indicator
[21] World Bank, https://data.worldbank.org/indicator/SP.POP.TOTL
[22] https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient
[23] https://www.pinterest.com/pin/281615782925970581/?lp=true
[24] https://en.wikipedia.org/wiki/Regression_toward_the_mean
[25] https://www.kaggle.com/harriken/residuals-fig8b-test
[26] https://www.kaggle.com/harriken/police-dogs-and-grey-hair-will-save-you-from-jail
References