#Dgo2019 Conference workshop A3 - viza
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Visualization & Storytelling Workshop Policy communication with charts - presentation by Jose Berengueres † Mariam AlMheiri CIT, UAEU, Al Ain, AD, UAEU, CIT CHSS, UAEU, Al Ain, AD, UAEU MBRS Dubai Jun 18th 2019 – d.go.2019
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#Dgo2019 Conference workshop A3 - viza
- 1. Visualization & Storytelling Workshop Policy communication with charts Jose Berengueres † Mariam AlMheiri CIT, UAEU, Al Ain, AD, UAEU, CIT CHSS, UAEU, Al Ain, AD, UAEU MBRS Dubai Jun 18th 2019 – d.go.2019 http://dgsoc.org/dgo-2019/
- 2. Agenda • Good chart / bad chart • How to use charts to think++
- 3. 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
- 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. data > information > kn > wisdom 1 What is data?
- 6. data > information > kn > wisdom many --------------------> one many ----------------------> scarce ? -----------------------------> value arrow ? 1 What is data?
- 7. data > information > kn > wisdom many --------------------> one many ----------------------> scarce ? -----------------------------> value? 1 What is data?
- 8. data > information > kn > wisdom 1 What is data?
- 9. data > HOW ? > wisdom 2 How to arrive to wisdom?
- 10. data > HOW ? > wisdom 2 How to arrive to wisdom? Synthesis process: the dialectic combination of thesis and antithesis into a higher stage of truth
- 11. 3 Exercise! Match the words data information kn wisdom NO!
- 12. data information kn wisdom 3 Exercise! Match the words
- 13. data information kn wisdom ? 3 Exercise! Match the words Reflection: Wisdom not just a summary, it is something more. Context? how to?
- 14. 4 Exercise! synthesise (the meaning) of the last 10 slides synthesis: ______________________________
- 15. 5 Lets draw it
- 17. Solution A
- 20. 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.
- 21. Solutions
- 22. Solutions pie chart ? bar chart ? stack bar ? vertical vs. horizontal? isomeasure? personas?
- 23. Solutions
- 24. Solutions
- 25. Solutions
- 26. Solutions
- 27. Solutions
- 28. Solutions
- 29. 7 Exercise! Visualize this A goal of gender parity has been set. How would you visualize it?
- 30. Solutions
- 31. Solutions
- 32. Solutions
- 33. 8 Bezos vs. Musk: Chart war
- 34. Flashback! Synthesis process: the dialectic combination of thesis and antithesis into a higher stage of truth (is this same as adding context?)
- 35. 9 Execisie! is this data, information or knowledge? https://www.kaggle.com/paultimothymooney/2018-kaggle-machine-learning-data-science-survey
- 36. 10 Execisie! Summarizing vs. mk meaning – #HMW make more meaning?
- 38. 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
- 39. How to use gravity to convey power? < -- ? -- >
- 44. 12 Data scientist by country #HMW more meaning?
- 45. hint!
- 46. Solutions
- 47. hint!
- 48. Solutions
- 51. Recap part 1
- 52. 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
- 53. 12 Cultural locales • color meaning by country • color awarenes by gender • color awarenss by profession • sexism awareness by country • Right to left languages
- 54. 13 Storytelling patterns Patterns of succesful storytelling • A/B (testing, advertising) • What is... What could be (duarte) • Aha moment! (Welch) • Heroes journey (duarte)
- 56. 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
- 57. 15 Most popular viz libs HMW make meaning?
- 58. hint!
- 59. Solutions
- 60. 16 Visualizing ALL OR NOTHING relationships. Exercise! HMW viz this?
- 61. Solutions
- 62. 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
- 66. 18 Gender distribution in data scientist. HMW empathise this chart?
- 67. Solutions
- 68. 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
- 69. Solutions
- 70. 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
- 71. Solutions
- 72. 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
- 73. 20 Log vs. Volumetric charts
- 77. 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
- 78. Wardley Maps – atool to map strategy for visual ppl
- 79. thank you
- 80. [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
Editor's Notes
- Part 1 - Making sense of data. Time: 30 minutes. Dataset: Kaggle 2018 Data science community survey [5-6]. Topics: How to Summarize large amounts of country level data. How to cluster country data. How to use color for visual clarity. How to style chart for impact. How to combine economic kpis in a dataset. How to use scatter plots for prescriptive analytics. Storytelling with regression and the mean reversion. (10 minutes break) Part 2 - Communicating effectively with charts. Time: 30 minutes. Dataset: Data Science for Good: Center for Policing Equity dataset. Displaying bivariate correlations and differences. (10 minutes break) Part 3 – Strategic policymaking with Wardley maps. Time: 30 minutes. Case study: Designing a curriculum of the future at a university. Using maps for strategic decision making. the dialectic combination of thesis and antithesis into a higher stage of truth
- Part 1 - Making sense of data. Time: 30 minutes. Dataset: Kaggle 2018 Data science community survey [5-6]. Topics: How to Summarize large amounts of country level data. How to cluster country data. How to use color for visual clarity. How to style chart for impact. How to combine economic kpis in a dataset. How to use scatter plots for prescriptive analytics. Storytelling with regression and the mean reversion. (10 minutes break) Part 2 - Communicating effectively with charts. Time: 30 minutes. Dataset: Data Science for Good: Center for Policing Equity dataset. Displaying bivariate correlations and differences. (10 minutes break) Part 3 – Strategic policymaking with Wardley maps. Time: 30 minutes. Case study: Designing a curriculum of the future at a university. Using maps for strategic decision making. the dialectic combination of thesis and antithesis into a higher stage of truth
- Part 1 - Making sense of data. Time: 30 minutes. Dataset: Kaggle 2018 Data science community survey [5-6]. Topics: How to Summarize large amounts of country level data. How to cluster country data. How to use color for visual clarity. How to style chart for impact. How to combine economic kpis in a dataset. How to use scatter plots for prescriptive analytics. Storytelling with regression and the mean reversion. (10 minutes break) Part 2 - Communicating effectively with charts. Time: 30 minutes. Dataset: Data Science for Good: Center for Policing Equity dataset. Displaying bivariate correlations and differences. (10 minutes break) Part 3 – Strategic policymaking with Wardley maps. Time: 30 minutes. Case study: Designing a curriculum of the future at a university. Using maps for strategic decision making. the dialectic combination of thesis and antithesis into a higher stage of truth
- Part 1 - Making sense of data. Time: 30 minutes. Dataset: Kaggle 2018 Data science community survey [5-6]. Topics: How to Summarize large amounts of country level data. How to cluster country data. How to use color for visual clarity. How to style chart for impact. How to combine economic kpis in a dataset. How to use scatter plots for prescriptive analytics. Storytelling with regression and the mean reversion. (10 minutes break) Part 2 - Communicating effectively with charts. Time: 30 minutes. Dataset: Data Science for Good: Center for Policing Equity dataset. Displaying bivariate correlations and differences. (10 minutes break) Part 3 – Strategic policymaking with Wardley maps. Time: 30 minutes. Case study: Designing a curriculum of the future at a university. Using maps for strategic decision making. the dialectic combination of thesis and antithesis into a higher stage of truth
- Part 1 - Making sense of data. Time: 30 minutes. Dataset: Kaggle 2018 Data science community survey [5-6]. Topics: How to Summarize large amounts of country level data. How to cluster country data. How to use color for visual clarity. How to style chart for impact. How to combine economic kpis in a dataset. How to use scatter plots for prescriptive analytics. Storytelling with regression and the mean reversion. (10 minutes break) Part 2 - Communicating effectively with charts. Time: 30 minutes. Dataset: Data Science for Good: Center for Policing Equity dataset. Displaying bivariate correlations and differences. (10 minutes break) Part 3 – Strategic policymaking with Wardley maps. Time: 30 minutes. Case study: Designing a curriculum of the future at a university. Using maps for strategic decision making. the dialectic combination of thesis and antithesis into a higher stage of truth
- the dialectic combination of thesis and antithesis into a higher stage of truth
- the dialectic combination of thesis and antithesis into a higher stage of truth
- the dialectic combination of thesis and antithesis into a higher stage of truth
- olution
- Reflection: Wisdom is not kn. Wisdom is not here. Wisdom not just a summary, it is something more. Context?
- Reflection: Wisdom is not here. Wisdom not just a summary, it is something more (recall sythesis definition). Context?
- not a summary:
- not a summary:
- not a summary:
- not a summary:
- the pyramid represnts power and scarcity at the same time.
- the pyramid represnts power and scarcity at the same time.
- A 2015 survey showed that only 26% of data jobs are held by women [1]. However, lack of feminine perspective creates blind spots such as the #googleWalkout [2] and is bad business - says Forbes [3]. In Fig. 1 (above), we use superhero-themes #batman #wonderwoman to visualize the heavy topic of #gender_equality in #datascience. See a bar chart for a more accurate breakdown [4]. Source: survey question Q1 - What is your gender? Sample size = 23,859 respondents
- source: https://www.kaggle.com/headsortails/what-we-do-in-the-kernels-a-kaggle-survey-story/report
- A 2015 survey showed that only 26% of data jobs are held by women [1]. However, lack of feminine perspective creates blind spots such as the #googleWalkout [2] and is bad business - says Forbes [3]. In Fig. 1 (above), we use superhero-themes #batman #wonderwoman to visualize the heavy topic of #gender_equality in #datascience. See a bar chart for a more accurate breakdown [4]. Source: survey question Q1 - What is your gender? Sample size = 23,859 respondents
- A 2015 survey showed that only 26% of data jobs are held by women [1]. However, lack of feminine perspective creates blind spots such as the #googleWalkout [2] and is bad business - says Forbes [3]. In Fig. 1 (above), we use superhero-themes #batman #wonderwoman to visualize the heavy topic of #gender_equality in #datascience. See a bar chart for a more accurate breakdown [4]. Source: survey question Q1 - What is your gender? Sample size = 23,859 respondents
- A 2015 survey showed that only 26% of data jobs are held by women [1]. However, lack of feminine perspective creates blind spots such as the #googleWalkout [2] and is bad business - says Forbes [3]. In Fig. 1 (above), we use superhero-themes #batman #wonderwoman to visualize the heavy topic of #gender_equality in #datascience. See a bar chart for a more accurate breakdown [4]. Source: survey question Q1 - What is your gender? Sample size = 23,859 respondents
- A 2015 survey showed that only 26% of data jobs are held by women [1]. However, lack of feminine perspective creates blind spots such as the #googleWalkout [2] and is bad business - says Forbes [3]. In Fig. 1 (above), we use superhero-themes #batman #wonderwoman to visualize the heavy topic of #gender_equality in #datascience. See a bar chart for a more accurate breakdown [4]. Source: survey question Q1 - What is your gender? Sample size = 23,859 respondents
- A 2015 survey showed that only 26% of data jobs are held by women [1]. However, lack of feminine perspective creates blind spots such as the #googleWalkout [2] and is bad business - says Forbes [3]. In Fig. 1 (above), we use superhero-themes #batman #wonderwoman to visualize the heavy topic of #gender_equality in #datascience. See a bar chart for a more accurate breakdown [4]. Source: survey question Q1 - What is your gender? Sample size = 23,859 respondents
- A 2015 survey showed that only 26% of data jobs are held by women [1]. However, lack of feminine perspective creates blind spots such as the #googleWalkout [2] and is bad business - says Forbes [3]. In Fig. 1 (above), we use superhero-themes #batman #wonderwoman to visualize the heavy topic of #gender_equality in #datascience. See a bar chart for a more accurate breakdown [4]. Source: survey question Q1 - What is your gender? Sample size = 23,859 respondents
- sensitivite to angular chane
- https://www.kaggle.com/paultimothymooney/2018-kaggle-machine-learning-data-science-survey
- is this wis
- What can we learn from our data scientist uncle? Fig. 2 is user distribution by age. We use a two-color scheme [18] to highlight which age-group won most competitions per user*. However, just a few too many age bins can overwhelm any reader. A way to declutter and structure the bins into usable knowledge is to reduce their numbers and group them in a familiar, relatable form. One way is to group the bins by generations. In this case, we used the Generations in the workforce (the gen X, Y, Z and the Boomers [5]) and we are interested to see which group is the most productive in terms of competitions and cash prizes per user. Because everyone belongs to a generation this chart can become very personable. What can we learn from the wisdom that each generation offers? Generation year brakets and work-ethic attribute The Baby Boomers, born 1946 - 1964 “often branded workaholics” [6] Gen X, born 1967 - 1977 “this generation works to live and carry with them a level of cynicism” [7] Gen Y, “Millennials” born 1980 - 2000 “considered the most educated and self-aware generation in employment” [8] Gen Z, born 2000 -
- source:https://www.kaggle.com/headsortails/what-we-do-in-the-kernels-a-kaggle-survey-story/report
- source: https://www.kaggle.com/headsortails/what-we-do-in-the-kernels-a-kaggle-survey-story/report
- Fig. 3 tells a #digitaldivide story. How inclusive are we as a community? Should we pat ourselves on our backs? Again, to create knowledge we need to relate the data to the reader in ways they can connect it to other knowledge they have. Here, one way is to use the income percentiles (see #onepercent). In US, to belong to the 1% elite, one needs to earn more than $422k per year [10]. About 23 respondents declared that they do. In addition, about 6% declared they belong to the 10% percentile, a very inclusive number because 6% is similar to 10%. The 10% percentile income is about $166k in US [11], so if the sample reflects the distribution found in society it means it is at least somehow inclusive. We add a smiley emoji to reassure the reader that yes, this is good. However, those numbers are for US household incomes. When we look globally, the 1% percentile thereshold is $32k per year. This puts 60% of the respondents in the top 1%. 60% is very different from 1% so globally this datapoint does not support inclusiveness because it does not reflect the global distribution. #Ahamoment. One way to create such moments in the story is to A/Bify the story by switching between two points of view. Source - Q9 : What is your current yearly compensation (approximate $USD)?
- Fig. 3 tells a #digitaldivide story. How inclusive are we as a community? Should we pat ourselves on our backs? Again, to create knowledge we need to relate the data to the reader in ways they can connect it to other knowledge they have. Here, one way is to use the income percentiles (see #onepercent). In US, to belong to the 1% elite, one needs to earn more than $422k per year [10]. About 23 respondents declared that they do. In addition, about 6% declared they belong to the 10% percentile, a very inclusive number because 6% is similar to 10%. The 10% percentile income is about $166k in US [11], so if the sample reflects the distribution found in society it means it is at least somehow inclusive. We add a smiley emoji to reassure the reader that yes, this is good. However, those numbers are for US household incomes. When we look globally, the 1% percentile thereshold is $32k per year. This puts 60% of the respondents in the top 1%. 60% is very different from 1% so globally this datapoint does not support inclusiveness because it does not reflect the global distribution. #Ahamoment. One way to create such moments in the story is to A/Bify the story by switching between two points of view. Source - Q9 : What is your current yearly compensation (approximate $USD)?
- Fig. 3 tells a #digitaldivide story. How inclusive are we as a community? Should we pat ourselves on our backs? Again, to create knowledge we need to relate the data to the reader in ways they can connect it to other knowledge they have. Here, one way is to use the income percentiles (see #onepercent). In US, to belong to the 1% elite, one needs to earn more than $422k per year [10]. About 23 respondents declared that they do. In addition, about 6% declared they belong to the 10% percentile, a very inclusive number because 6% is similar to 10%. The 10% percentile income is about $166k in US [11], so if the sample reflects the distribution found in society it means it is at least somehow inclusive. We add a smiley emoji to reassure the reader that yes, this is good. However, those numbers are for US household incomes. When we look globally, the 1% percentile thereshold is $32k per year. This puts 60% of the respondents in the top 1%. 60% is very different from 1% so globally this datapoint does not support inclusiveness because it does not reflect the global distribution. #Ahamoment. One way to create such moments in the story is to A/Bify the story by switching between two points of view. Source - Q9 : What is your current yearly compensation (approximate $USD)?
- Fig. 3 tells a #digitaldivide story. How inclusive are we as a community? Should we pat ourselves on our backs? Again, to create knowledge we need to relate the data to the reader in ways they can connect it to other knowledge they have. Here, one way is to use the income percentiles (see #onepercent). In US, to belong to the 1% elite, one needs to earn more than $422k per year [10]. About 23 respondents declared that they do. In addition, about 6% declared they belong to the 10% percentile, a very inclusive number because 6% is similar to 10%. The 10% percentile income is about $166k in US [11], so if the sample reflects the distribution found in society it means it is at least somehow inclusive. We add a smiley emoji to reassure the reader that yes, this is good. However, those numbers are for US household incomes. When we look globally, the 1% percentile thereshold is $32k per year. This puts 60% of the respondents in the top 1%. 60% is very different from 1% so globally this datapoint does not support inclusiveness because it does not reflect the global distribution. #Ahamoment. One way to create such moments in the story is to A/Bify the story by switching between two points of view. Source - Q9 : What is your current yearly compensation (approximate $USD)?
- is this wis
- not a summary:
- not a summary:
- US-UK-EU gap The US-EU gap is about 50%. However, the UK mean closer to the EU6 mean than to the US mean. Does this mean we discard language barrier as a explanatory factor for the gap? Note: The BRICS, and EU6 mean is mean of country means, not weighted by respondents. Aesthetic considerations This color scheme is called the red on grey, it is my favorite scheme for charts. Unlike, other schemes such as purple on grey, it is gender neutral [23]. However, for it to work the red surface must be kept to a minimum, otherwise it comes across as strident. The blue on grey scheme does not have this limitation (See Figs. 1-5). However, the red on grey has one secret advantage. Usually, using three colors in a chart will clutter it, but because the chromatic distance between red and any shade of grey is so large, we can get away by using black (as a gray 85%) as a third color with a small clutter trade-off. Source - World Bank Population Data 2016, Q11 - Current country of residence [20]
- US-UK-EU gap The US-EU gap is about 50%. However, the UK mean closer to the EU6 mean than to the US mean. Does this mean we discard language barrier as a explanatory factor for the gap? Note: The BRICS, and EU6 mean is mean of country means, not weighted by respondents. Aesthetic considerations This color scheme is called the red on grey, it is my favorite scheme for charts. Unlike, other schemes such as purple on grey, it is gender neutral [23]. However, for it to work the red surface must be kept to a minimum, otherwise it comes across as strident. The blue on grey scheme does not have this limitation (See Figs. 1-5). However, the red on grey has one secret advantage. Usually, using three colors in a chart will clutter it, but because the chromatic distance between red and any shade of grey is so large, we can get away by using black (as a gray 85%) as a third color with a small clutter trade-off. Source - World Bank Population Data 2016, Q11 - Current country of residence [20]
- Part 1 - Making sense of data. Time: 30 minutes. Dataset: Kaggle 2018 Data science community survey [5-6]. Topics: How to Summarize large amounts of country level data. How to cluster country data. How to use color for visual clarity. How to style chart for impact. How to combine economic kpis in a dataset. How to use scatter plots for prescriptive analytics. Storytelling with regression and the mean reversion. (10 minutes break) Part 2 - Communicating effectively with charts. Time: 30 minutes. Dataset: Data Science for Good: Center for Policing Equity dataset. Displaying bivariate correlations and differences. (10 minutes break) Part 3 – Strategic policymaking with Wardley maps. Time: 30 minutes. Case study: Designing a curriculum of the future at a university. Using maps for strategic decision making. the dialectic combination of thesis and antithesis into a higher stage of truth
- Part 1 - Making sense of data. Time: 30 minutes. Dataset: Kaggle 2018 Data science community survey [5-6]. Topics: How to Summarize large amounts of country level data. How to cluster country data. How to use color for visual clarity. How to style chart for impact. How to combine economic kpis in a dataset. How to use scatter plots for prescriptive analytics. Storytelling with regression and the mean reversion. (10 minutes break) Part 2 - Communicating effectively with charts. Time: 30 minutes. Dataset: Data Science for Good: Center for Policing Equity dataset. Displaying bivariate correlations and differences. (10 minutes break) Part 3 – Strategic policymaking with Wardley maps. Time: 30 minutes. Case study: Designing a curriculum of the future at a university. Using maps for strategic decision making. the dialectic combination of thesis and antithesis into a higher stage of truth
- US-UK-EU gap The US-EU gap is about 50%. However, the UK mean closer to the EU6 mean than to the US mean. Does this mean we discard language barrier as a explanatory factor for the gap? Note: The BRICS, and EU6 mean is mean of country means, not weighted by respondents. Aesthetic considerations This color scheme is called the red on grey, it is my favorite scheme for charts. Unlike, other schemes such as purple on grey, it is gender neutral [23]. However, for it to work the red surface must be kept to a minimum, otherwise it comes across as strident. The blue on grey scheme does not have this limitation (See Figs. 1-5). However, the red on grey has one secret advantage. Usually, using three colors in a chart will clutter it, but because the chromatic distance between red and any shade of grey is so large, we can get away by using black (as a gray 85%) as a third color with a small clutter trade-off. Source - World Bank Population Data 2016, Q11 - Current country of residence [20]
- US-UK-EU gap The US-EU gap is about 50%. However, the UK mean closer to the EU6 mean than to the US mean. Does this mean we discard language barrier as a explanatory factor for the gap? Note: The BRICS, and EU6 mean is mean of country means, not weighted by respondents. Aesthetic considerations This color scheme is called the red on grey, it is my favorite scheme for charts. Unlike, other schemes such as purple on grey, it is gender neutral [23]. However, for it to work the red surface must be kept to a minimum, otherwise it comes across as strident. The blue on grey scheme does not have this limitation (See Figs. 1-5). However, the red on grey has one secret advantage. Usually, using three colors in a chart will clutter it, but because the chromatic distance between red and any shade of grey is so large, we can get away by using black (as a gray 85%) as a third color with a small clutter trade-off. Source - World Bank Population Data 2016, Q11 - Current country of residence [20]
- all good stories...
- US-UK-EU gap The US-EU gap is about 50%. However, the UK mean closer to the EU6 mean than to the US mean. Does this mean we discard language barrier as a explanatory factor for the gap? Note: The BRICS, and EU6 mean is mean of country means, not weighted by respondents. Aesthetic considerations This color scheme is called the red on grey, it is my favorite scheme for charts. Unlike, other schemes such as purple on grey, it is gender neutral [23]. However, for it to work the red surface must be kept to a minimum, otherwise it comes across as strident. The blue on grey scheme does not have this limitation (See Figs. 1-5). However, the red on grey has one secret advantage. Usually, using three colors in a chart will clutter it, but because the chromatic distance between red and any shade of grey is so large, we can get away by using black (as a gray 85%) as a third color with a small clutter trade-off. Source - World Bank Population Data 2016, Q11 - Current country of residence [20]
- not a summary:
- planets
- https://www.kaggle.com/harriken/police-dogs-and-grey-hair-will-save-you-from-jail
- https://www.kaggle.com/harriken/police-dogs-and-grey-hair-will-save-you-from-jail
- Part 1 - Making sense of data. Time: 30 minutes. Dataset: Kaggle 2018 Data science community survey [5-6]. Topics: How to Summarize large amounts of country level data. How to cluster country data. How to use color for visual clarity. How to style chart for impact. How to combine economic kpis in a dataset. How to use scatter plots for prescriptive analytics. Storytelling with regression and the mean reversion. (10 minutes break) Part 2 - Communicating effectively with charts. Time: 30 minutes. Dataset: Data Science for Good: Center for Policing Equity dataset. Displaying bivariate correlations and differences. (10 minutes break) Part 3 – Strategic policymaking with Wardley maps. Time: 30 minutes. Case study: Designing a curriculum of the future at a university. Using maps for strategic decision making. the dialectic combination of thesis and antithesis into a higher stage of truth
- Part 1 - Making sense of data. Time: 30 minutes. Dataset: Kaggle 2018 Data science community survey [5-6]. Topics: How to Summarize large amounts of country level data. How to cluster country data. How to use color for visual clarity. How to style chart for impact. How to combine economic kpis in a dataset. How to use scatter plots for prescriptive analytics. Storytelling with regression and the mean reversion. (10 minutes break) Part 2 - Communicating effectively with charts. Time: 30 minutes. Dataset: Data Science for Good: Center for Policing Equity dataset. Displaying bivariate correlations and differences. (10 minutes break) Part 3 – Strategic policymaking with Wardley maps. Time: 30 minutes. Case study: Designing a curriculum of the future at a university. Using maps for strategic decision making. the dialectic combination of thesis and antithesis into a higher stage of truth