Do you want to make pictures using data but don't know where to start? Would you like to learn how data visualization works, and how to tell stories with data? This workshop by David Newbury explores the history of data visualization from the first maps to the latest interactive tools from the New York Times.The workshop will also discuss the hows and whys of storytelling with data. It finshes with a collaborative exploration of data visualization using Sharpies, Post-It notes, and things that begin with "S". No computers will be used in this class, and there are no prerequisites. As a result of this workshop, you'll have a stronger foundation in understanding how to communicate information more-effectively. We’re excited to partner with the Carnegie Library of Pittsburgh on a “Data 101” training series designed to build information literacy, mapping, and data visualization skills for people looking to get started in using data, or more-experienced users looking to brush-up on their skills. The training sessions will be offered monthly at one of the Library’s branches, and will be followed by ample time to practice what you’ve learned. This first class on data visualization was offered on the morning of May 10, 2016 at the East Liberty Branch.

1. Data Visualization
Data 101
May 10th, 2016
Data 101. David Newbury — @workergnome 1

2. David NewburyProfessional nerd artist
@workergnome
www.workergnome.com
Data 101. David Newbury — @workergnome 2

3. What We're Doing Today:
—(Brief) History of Data Visualization
—(Tiny) Theory of Visualization
—(Nerdy) Overview of Concepts
—(Fake) Data Exploration
—(Incomplete) Overview of Tools
Data 101. David Newbury — @workergnome 3

4. What We're not Doing Today:
—Writing Code
—Thinking about Mapping
—Worrying about Data Provenance
Data 101. David Newbury — @workergnome 4

5. Which is biggest?
15012, 8271, 30193, 1189, 9913, 16000, 92481, 49801,
100407, 2910, 3809, 8018, 61528, 18083, 38691, 1800
Data 101. David Newbury — @workergnome 5

6. Which is biggest?
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7. Which is biggest?
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8. Why?
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9. (Brief)
History of
Data Visualization
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10. Tabula Peutingeriana, 5th century CE
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11. Data 101. David Newbury — @workergnome 11

12. Rene Descartes, 1600s
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13. Joseph Priestly, New Chart of History (1769)
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14. William Playfair, (1786 & 1801)
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15. Data 101. David Newbury — @workergnome 15

16. Data 101. David Newbury — @workergnome 16

17. John Snow, London Cholera Map (1854)
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18. Cholera Map
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19. Florence Nightingale, War Deaths (1855)
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20. Charles Minard, March on Moscow (1862)
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21. More recent history.
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22. Data 101. David Newbury — @workergnome 22

23. Edward Tufte
The Visual Display of
Quantitative Information.
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24. Data 101. David Newbury — @workergnome 24

25. New York Times
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26. Data 101. David Newbury — @workergnome 26

27. (tiny)
Theory
of Visualization
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28. Dataviz is constructed reality.
You are telling a story, not (just) stating facts.
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29. data art
as opposed to
data visualization
as opposed to
statistical graphics
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30. Statistical
Graphics
How do I create Statistical
Graphs in SAS 9.1.3 without
Proc Gplot. UCLA: Statistical
Consulting Group.
http://www.ats.ucla.edu/stat/
sas/notes2/
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31. Data Art
Dear Data
Giorgia Lupi & Stefanie
Posavec.
http://www.dear-data.com
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32. Two Uses1). help people grasp things outside their reach
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33. Two Uses1). help people grasp things outside their reach
2.) tell stories
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34. explanatory visualization work
as opposed to
exploratory visualizations
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35. Dataviz is constructed reality.
Do you care how true your story is?
Do you care how accurate your story is?
Are you trying to teach, entertain, or convince?
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36. Data 101. David Newbury — @workergnome 36

37. Data 101. David Newbury — @workergnome 37

38. Data 101. David Newbury — @workergnome 38

39. Data 101. David Newbury — @workergnome 39

40. (Nerdy)
Overview of Concepts
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41. What can you visualise?
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42. Potential Subjects.
subways, sheep, the solar system,
shoes, sleep, skyline,
snow, supermarket, sausages,
school,the sea, spiders,
staircases, syrup, soap,
sawmills, stereos...
Data 101. David Newbury — @workergnome 42

43. Potential Subjects.
subways, sheep, the solar system,
shoes, sleep, skyline,
snow, supermarket, sausages,
school,the sea, spiders,
staircases, syrup, soap,
sawmills, stereos...
...and other things that begin with S.
Data 101. David Newbury — @workergnome 43

44. Dimension and Scope
are about choosing
what to focus on.
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45. Scope
Out of the infinite stories about any subject,
which parts are you going to choose?
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46. Possible Scopes
All trains in a day
All the rides that I've been on this year
My train this morning
All of the stops in the city
Each line
Every train stop in the past 50 years
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47. Dimension
Which bits of information about a subject
are you going to focus on?
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48. Possible Dimensions
number of cars
duration of ride
date of a ride
different lines
number of stops
cost per ride
number of stops per day
time between stops
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49. What does your
data look like?
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50. Types of Data
Dates
Numbers
Geo Coordinate
Strings
Categories
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51. Types of Data
number of cars - Numeric
duration of ride - Numeric
date of a ride - Date
different lines - Category
number of stops - Numeric
cost per ride - Category
number of stops per day - Numeric
time between stops - Numeric
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52. Two (related ides):
Categories & measures
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53. Categories are Discrete Things
Measures are for Counting
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54. number of cars - Measure
duration of ride - Measure
date of a ride - Measure
different lines - Categories
number of stops - Measure
cost per ride - Categories
number of stops per day - Measure
time between stops - Measure
cleanliness - Categories
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55. A hidden dimension:
David, Daniel, Dawn, Danique
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56. A hidden dimension:
David (1), Daniel (2), Dawn (3), Danique (4)
Position of the item in the group.
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57. (Fake)
Data Exploration
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58. TRY IT.
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59. Choose one.
subways, sheep, the solar system,
shoes, sleep, skyline,
snow, supermarket, sausages,
school,the sea, spiders,
staircases, syrup, soap,
sawmills, stereos...
...and other things that begin with S.
Data 101. David Newbury — @workergnome 59

60. Now
What?Data 101. David Newbury — @workergnome 60

61. We need to map our data
from a domain
to a range.
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62. Domain
number of cars - 1...8
duration of ride - 30 sec...2 hours
date of a ride - - 24ft...200ft
different lines - Red line, Blue line, Green line, Silver
Line, Yellow Line
number of stops - **2..20
cost per ride - "$2.50, $1.75, $3.00, $0.00"
number of stops per day - ??...???
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63. Range
Domain is the possible input values
Range is the possible output values
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64. Data
3, 7, 10, 6, 2
Position of the item in the group.
Domain
[0-10]
[1-5]
Range
X: 400px
Y: 800px
Mapping
X: item position
Y: numeric value
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65. Data
3, 7, 10, 6, 2
Position of the item in the group.
Area
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66. Data
3, 7, 10, 6, 2
Position of the item in the group.
Color
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67. Data
3, 7, 10, 6, 2
Position of the item in the group.
Multiples
Dimensions
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68. Data
val1: 3, 7, 10, 6, 2
val2: 5, 8, 1, 8, 3
val3: Cat, Dog, Cat, Cat, Dog
Position of the item in the group.
Mapping
X: item position
Y: val1
Size: val2
Color: val3
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69. Dimensions beyond X and Y.
Color
Size
Shape
Labels
Patterns
Icons
Anything Else You Can Imagine
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70. TRY IT.
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71. Finishing
Touches
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72. Measures get Axis
Categories get Headers
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73. Labels
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74. Axis
Category Axis
Number Axis
Date Axis
Log axis
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75. Legends
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76. TRY IT.
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77. Review
Dimensions
Scope
Domain
Range
Categories
Measures
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78. (Incomplete)
Overview of Tools
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79. Data 101. David Newbury — @workergnome 79

80. Data 101. David Newbury — @workergnome 80

81. Data 101. David Newbury — @workergnome 81

82. Data 101. David Newbury — @workergnome 82

83. Data 101. David Newbury — @workergnome 83

84. Data 101. David Newbury — @workergnome 84

85. Thank You.
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