1. The Science of Data Visualization Presented by Nick Beaton

3. Business reality “We’re in data shock” “Every 2 days, we generate more data than from the beginning of time through 2003”

6. What is Visual Analytics?

7. “Visual analytics is the representation and presentation of data that exploits our visual perception abilities in order to amplify cognition.” - Andy Kirk, author of “Data Visualization: a successful design process”

8. Let’s Look at Some Data IIIIIIIV Xyxyxyxy 108.04109.14107.4686.58 86.9588.1486.7785.76 137.58138.741312.7487.71 98.8198.7797.1188.84 118.33119.26117.8188.47 149.96148.1148.8487.04 67.2466.1366.0885.25 44.2643.145.391912.5 1210.84129.13128.1585.56 74.8277.2676.4287.91 55.6854.7455.7386.89

9. Let’s Look at Some Data PropertyValue Mean of x in each case 9 (exact) Variance of x in each case 11 (exact) Mean of y in each case7.50 (to 2 decimal places) Variance of y in each case4.122 or 4.127 (to 3 decimal places) Correlation between x and y in each case 0.816 (to 3 decimal places) Linear regression line in each case y = 3.00 + 0.500x (to 2 and 3 decimal places, respectively) IIIIIIIV Xyxyxyxy 108.04109.14107.4686.58 86.9588.1486.7785.76 137.58138.741312.7487.71 98.8198.7797.1188.84 118.33119.26117.8188.47 149.96148.1148.8487.04 67.2466.1366.0885.25 44.2643.145.391912.5 1210.84129.13128.1585.56 74.8277.2676.4287.91 55.6854.7455.7386.89

10. Let’s Look at Some Data … Visually “Anscombe’s Quartet” Source: Wikipedia

11. Two Basic Types Exploration – find a story the data is telling you Explanation – tell a story to an audience Represents large quantities of data coherently Help the user to discern relationships in the data Does not distort what the data has to say Takes into account your audience’s expectations The Value of Data Visualization

12. Agenda 1.Why Data Visualization? 2.Human Perception and Cognition 3.Visualization Best Practices 4.Demonstrate new techniques for harnessing data

13. Human Perception & Cognition

15. Optimized for what bees see

16. Bottom-up: Finds patterns Top-down: Directs and interprets EyeBrain Human vision is bi-directional

17. Painting by Ilya Efimovich Repin (1884)

18. Where you look depends on your task Painting by Ilya Efimovich Repin (1884)

19. “What are the people’s ages?” Eye-tracking studies by Alfred Yarbus (1965)

20. “What are people wearing?” Eye-tracking studies by Alfred Yarbus (1965)

21. http://www.useit.com/alertbox/reading_pattern.html Eye Gaze heat maps

22. Humans Are Slow at Mental Math 34 X 72 ------------------

23. We’re Faster When We Use the World 34 X 72 ------------------ 68 23 1 80 ------------------ 2448

24. Much Faster 34 X 72 ------------------ 68 23 1 80 ------------------ 2448

25. We’re Faster When We Can “See” Data

28. Preattentive Visual Attributes

29. Preattentive Features and Tasks

30. Proximity: Objects that are closer together or connected are perceived as a group Similarity: Objects that share similar attributes, color or shape, are perceived as a group Enclosure and Continuity: Objects that appear to have a boundary or a continuation around them are perceived as a group Closure: Open structures can easily be perceived as complete Gestalt Principles of Perception

31. From findwally.co.uk

32. Need attention to see a change From Ron Rensink, University of British Columbia http://www.psych.ubc.ca/~rensink/flicker/download/index.html “Change Blindness”

33. Visual Interruptions Make People Slow

35. Derren Brown - Person Swap https://www.youtube.com/watch?v=vBPG_OBgTWgwww.youtube.com/watch?v=vBPG_OBgTWg

36. Visualization Best Practices

37. Best Practices Overview 1.Representing data for humans 2.Color 3.Maps 4.Creating dashboards

38. Types of Data Qualitative (categorical) Arizona, New York, Texas Sarah, John, Maria Coors, Bud Light, Stella Artois Qualitative (ordinal) Gold, silver, bronze Excellent health, good health, poor health Love it, like it, hate it Quantitative Weight (10 lbs, 20 lbs, 5000 lbs) Cost ($50, $100, $0.05) Discount (5%, 10%, 12.8%) Position, Shape, Color Position, Size, Color Intensity, Different Colors / Shapes Position, Length, Size, Color Intensity

39. How Do Humans Like Their Data? Position Color Size Shape More important Less important

40. How Do Humans Like Their Data? Time: on an x-axis Location: on a map Comparing values: bar chart

41. How do humans like their data?

42. How Do Humans Like Their Data? Orient data so people can read it easily Better Good

43. Color Me Impressed Color perception is relative, not absolute

44. Color Me Impressed Provide a consistent background

45. Color Me Impressed Humans can only distinguish ~8 colors This is not helpful.

46. Color Me Impressed Humans can only distinguish ~8 colors This is helpful.

47. Color Me Impressed For quantitative data, color intensity and diverging color palettes work well

48. Mapping to Insight Use maps when location is relevant

49. Mapping to Insight Use filled maps (“cloropleths”) for defined areas and only ONE measure

50. Mapping to Insight Filled maps won’t work for multiple measures

51. Mapping to Insight Don’t use maps just because you can

52. Mapping to Insight Maps don’t have to be geographic

53. Mapping to Insight Maps don’t have to be geographic

54. Dashboards Dashboards bring together multiple views

55. Dashboards Dashboards should pass the 5-second test

56. Dashboarding for the 5-second Test Most important view goes on top or top-left Legends go near their views Avoid using multiple color schemes on a single dashboard Use 5 views or fewer in dashboards Provide interactivity

57. Dashboarding for the 5-second Test Use your words! Titles Axes Key facts and figures Units Remove extra digits in numbers Great tooltips