Webcast Presentation as IEEE Distinguished Lecturer in Computer Science at Union County College October 2016.

1. The Art Of Data
Visualization
An Online Course
by
Dr. Andres Fortino

3. Agenda
• Data visualization is storytelling in a graphical
medium.
• We strive to create a visual (a sign) that delivers
intended message (a signifier) elegantly and achieves
intended purpose (the significant). In other words,
after making a data visual you ask yourself:
Did I create a good sign with a message that is clear and
moves people to action?

4. Agenda
• We will explore a few significant elements to
produce compelling charts by answering these
questions:
– Does the visual send a strong and unmistakable
signal?
– Have the principles of perception been used to
greatest advantage?
– Is it a quality visual that resolves the viewer's desire to
be informed?
– Can you improve your visual by emulating famous
chart makers?

5. SIGNALS
The Art of Data Visualization

6. What’s the point?
• For a data visual to be successful:
Are signs and symbols used properly in the visual?

7. People need to make decisions

8. Give Them a Sign

9. What your signs should not be
Not a solution Wrong
What is wrong with these signs?
Too complex

10. Is it cute or is it clear?

11. Semiotics
• The science of sign making
• It has three parts:
– Signifier – the intended
meaning
– Signified – the symbol
or icon that stands in
for the signifier
– Sign – The combination
that makes up our
perception

13. Cultural Difference in
Symbol Interpretation

14. Semiotics and Data Visuals
• Translation to information visualization
• Is our visual a sign? Of what?
• Is the signifier (meaning) clear?
• Have we used a good significant (symbolic
image to represent our meaning)?

15. Example
• Signifier
– Have we reached our goal yet?
• Significant
– A thermometer icon
• Sign
– A thermometer chart
• Is this the appropriate chart?
• Did we use this chart appropriately here?
• Have we violated or used cultural norms?

16. What is wrong symbolism in this chart?
Results of the 2012 US Presidential Elections
1. Winner’s bar should
be on top
1. The democrats won
the race so the
symbol for each
party is wrong
1. The color for each
party is wrong

17. What’s the point?
• For a data visual to be successful:
Are signs and symbols used properly in the visual?
• Has the visualization transgressed cultural
conventions?
• Were we expecting the symbolism as used in
the chart?

18. PERCEPTION
The Art of Data Visualization

19. Eye of the beholder
“Many an object is not seen, though it falls within the
range of our visual ray, because it does not come within
the range of our intellectual ray, i.e. we are not looking
for it. So, in the largest sense, we find only the world we
look for.”
“The question is not what you look at, but what you see.”
Henry David Thoreau, Journal

20. Get the Picture
• Did you know that 25% of your brain power is
connected to visual stimulus, and 70% of our
sensory receptors are in our eyes?
• No wonder we "get the picture" faster when
presenting information visually.

23. Yarbus - Measuring Eye Movements
while Seeing

24. Where does your eye fall?

25. Where does your eye fall?
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ACME WEBSITE VISITS
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26. Using Yarbus’ work
• Your eye is attracted unconsciously to strong
focal point images
• You should decide what are the most
important things you want your viewers to
focus on and make them stand out when they
first view chart
• Use Gestalt, color theory, the right chart,
remove chart junk and other methods to
assist in focusing the viewer’s eye

27. Why does this happen?
• Our perception is programmed (via our DNA)
try to make sense of shapes to “flight or flee”
in a split second
This is the work of the millions of our rod cells that see in black and
white and can discriminate detail

28. What’s the point?
• For a data visual to be most successful:
Does the eye of the viewer focus on the most important
point you are trying to make?
• Did you check the visual to make sure that as soon as
the viewer sees it their eyes go to the most
important feature to stress?
• If not, what can be done to the visualization to make
it happen?

29. Another the point
• For a data visual to be most successful
The principles of the Gestalt psychology of perception
should be thoughtfully employed when creating a
visualization

31. Can you spot the wolf?

32. Visual Perception and Gestalt
PROXIMITY
Objects close together
are perceived as related.
SIMILARITY
Objects similar in nature
are perceived as related.
FIGURE-BACKGROUND
Objects are perceived as figures
(in focus) or background.
CONTINUITY
Objects moving in the same direction
are perceived as related.
PRÄGNANZ (Simplicity)
Simple patterns are preferred to and
understood more than complex ones.
CONNECTEDENESS
Connected objects
are perceived as related.
CLOSURE
Open structures are
perceived as closed.

33. Category Value
Category A 8.25
Category B 20.10
Category 1 105.20
Category 2 899.36
Category 3
1,003.3
1
Visual Perception and Gestalt
PROXIMITY
Objects close together
are perceived as related.
SIMILARITY
Objects similar in nature
are perceived as related.
FIGURE-BACKGROUND
Objects are perceived as figures
(in focus) or background.
CONTINUITY
Objects moving in the same direction
are perceived as related.
PRÄGNANZ (Simplicity)
Simple patterns are preferred to and
understood more than complex ones.
CONNECTEDENESS
Connected objects
are perceived as related.
CLOSURE
Open structures are
perceived as closed.
Category
Category
Category
Category
Category

34. The Principal Principle- Figure/Ground

36. What’s the point?
• For a data visual to be most successful
The principles of the Gestalt psychology of perception
should be thoughtfully employed when creating a visual
• Does the visual have good figure/ground differences?
• Has grouping been used to best effect?
• Has connectedness been used effectively?
• How about flow?

37. QUALITY
The Art of Data Visualization

38. “The usefulness of a graph can be evaluated
only in the context of the type of data, the
questions the designer wants the readers to
answer, and the nature of the audience.”
Stephen M. Kosslyn,
from Graph Design for the Eye and Mind

39. What’s the point?
• For a data visual to be most successful
The visual must contain a quality to resolve the viewer's
desire to be informed.

40. Christopher Alexander
• Quality Without a Name
• A Pattern Language

41. The Timeless Way
• Although Alexander is speaking about building physical
spaces, homes and towns, this has been applied
universally to many other disciplines, and it applies to
building visuals as well.
“There is one timeless way of building. It is a
thousand years old, and the same today as it has
ever been. The great traditional buildings of the past,
the villages and tents and temples in which man feels
at home, have always been made by people who
were very close to the center of this way.”
Christopher Alexander Timeless Way of Building

42. The Timeless Way
The Process
“A building or a town [or a data visual] will only be
alive to the extent that is governed by the timeless
way.”
“It is a process which brings order out of nothing
but ourselves; it cannot be attained, but it will
happen of its own accord, if we will only let it.”

43. The Timeless Way Precepts:
The Quality
• A visual will have that quality of being alive, and being
very useful to human beings if we use the principles
Alexander outlines.
“To seek the timeless way we must first know the
quality without a name.”
“There is a central quality which is the root
criterion of life and spirit in a man, a town, a
building, or a wilderness. This quality is objective
and precise, but it cannot be named.”

44. The Timeless Way Precepts:
The Gate
• This is the key element: use a pattern language, which are a
collection of techniques that produce quality visuals. Essentially all
the good principles we outline here.
• Patterns resolve forces in tension and, in our case bring knowledge.
• The tension in our case is between knowing and not knowing.
“To reach the quality without a name we must then build a
living pattern language as a gate.”
“This quality in buildings and in towns cannot be made, but
only generated, indirectly, by the ordinary actions of the
people, just as a flower cannot be made, but only
generated from the seed.”

45. Example Architectural Pattern
• In architecture there is a building pattern called a
“door”
• It resolves many forces in tension:
– It marks the controlled separation between
“inside” and “outside”
– Keeping people in (as in jails)
– Keeping people and undesirable elements
out (as in front door of a house, or a screen door)
– While allowing controlled access between spaces
– It can be secured (front door) or not (swing door)
• It comes in many forms
– Barn doors, front doors, screen doors, pet doors

46. Example Visual Pattern
• One pattern in our repertoire of visual forms
language is the “pie chart”
• It resolves many forces in tension
– Knowing what is the contribution of each
part to a whole
– Knowing which parts matters and which are
inconsequential
– Knowing the rough order of the sizes of each datum
• It comes in many forms (pie, doughnut, gauge)
• In the end if it has been applied properly it
informs the viewer, who, after viewing “knows”

47. The Timeless Way Precepts:
The Way
• In our case that means the use of all these patterns
will result in a stream of quality visuals that informs
our viewers
“Once we have built the gate, we can pass through it to the
practice of the timeless way.”
“Now we shall begin to see in detail how the rich and
complex order of a town can grow from thousands of
creative acts. For once we have a common pattern language
in our town, we shall all have the power to make our streets
an buildings live, through our most ordinary acts.”

48. The Timeless Way
• In our case that means that a visual we build with quality
has to inform, it has to dispel ignorance, and provide the
“good of knowing” to the world.
• This is the key element in our analysis of a visual using the
principle: does it inform?
“This is a fundamental view of the world. It says that when
you build a thing you cannot merely build that thing in
isolation, but must repair the world around it, and within it,
so that the larger world at that one place becomes more
coherent, and more whole; and the thing which you make
takes its place in the web of nature, as you make it.”

49. The Timeless Way
of Living Visuals
• To be “alive” a visual must provide a service.
• The service is the resolution of a tension the
viewer brings to the chart. They want to know.
• If the chart informs, the tension is relieved, the
viewer now “knows”.
• If the viewers is still puzzled over the information
after viewing, the chart does not have the living
quality of informing and releasing the tension of
ignorance.
• Did our chart inform? Did it relieve tension? Then
we can say it is a quality chart

50. What’s the point?
• For a data visual to be most successful
The visual must inform - it should dispel ignorance.
• When critiquing yours or peer’s visuals ask:
– Is the viewer informed?
– Do the forces in tension find resolution in the chart?
– Does the tension the viewer brings to the chart relived by
the information and how it is presented?

51. GIANTS
The Art of Data Visualization

52. John Snow and the
London Cholera Map

53. John Snow and the
London Cholera Map

54. Hans Rosling and Gapminder

55. Charles Minard and
The March on Moscow

56. Florence Nightingale and
The Crimean War

57. CONCLUSION
The Art of Data Visualization

59. “Start with a strong focus, do as much
research as you can, organize, summarize,
and then deliver your conclusions in a
structured and visually appealing manner.”
Alberto Cairo,
The Functional Art

60. Spring 2017 Course Details
• Who:
– Dr. Andres Fortino, principal faculty
• Where:
– NYU School of Professional Studies
– Center for Advanced Digital Applications
• When:
– Five weeks, March 27-May 6, 2017
• What:
– Online self-paced, weekly modules (MOOC-style)
– Non-credit graduate-level seminar, graded
• Sign up:
– https://www.sps.nyu.edu/professional-pathways/courses/DATA1-
CE9002-the-art-of-data-visualization.html
– Cost: $675

61. Course Description
• Data visualization is storytelling in a graphical medium.
• The format of this course is inspired by the workshops used
extensively to train budding writers, in which you gain
knowledge by doing and redoing, by offering and receiving
critique, and above all, by learning from each another.
• Present your project while other students offer critique and
suggestions for improvement.
• The course offers immersion into the creative process, the
discipline of sketching and revising, and the practical use of
tools.
• Develop a discriminating eye for good visualizations.
Readings on aspects of the craft are assigned throughout
the term.

62. Course Learning Outcomes
• Give constructive critique on other people’s data
visualization
• Listen and respond to critique from others on one’s
own data visualization
• Evaluate alternative visualization of the same data
• Refine and improve drafts of data visualization projects
• Interpret data visualization with an integrated lens
combining the perspectives of statistical graphics,
graphic design, and information visualization
• Create at least one piece of work that can be included
in one’s portfolio

64. Class Format - Online
• The instruction for the course is conducted online
asynchronously.
• That means there are not set “class meetings”. The material
for the class is posted on line as modules to be done weekly,
with short video presentations (10-20 min) by the instructor
and assignments to be completed each week.
• Since in this class students learn by giving and receiving
criticism class participate in the class forums each week is
required. Students are required to post at least one criticism
of two of their colleagues assignments to receive credit for
the class.
• Faculty is constantly available to give feedback and guidance
• All classwork as well as assignment submissions are done
through the NYU Classes Learning Management System.
• Studio format with small classes

65. Dr. Andres Fortino Bio
• Dr. Andres Fortino is a consultant in the innovation industry and is currently the
principal with Fortino Global Education.
• He teaches big data analytics and Innovation and Entrepreneurship at NYU School
of Professional Studies. He leads seminars in data visualization and business
analytics at the American Management Association.
• He served as Dean of Academic Affairs at DeVry College of New York and as
Associate Provost for Corporate Graduate Programs at the Polytechnic Institute of
New York University. He holds bachelors and masters degrees in electrical
engineering from City College of New York and a PhD in electrical engineering from
CUNY. Before joining NYU-Poly he served as Dean of the Marist College School of
Management.
• He is a Senior Member of IEEE and a Distinguished Speaker in Computer Science
with the IEEE. He is a Fulbright Specialist in Technology Management. His major
interests are innovation, creativity and data analytics and data visualization as well
as technology innovation management.

66. Student Comments from The Art of Data
Visualization Class Spring 2016
• The Professor did a great job of internalizing his sources and teaching key elements
in his modules. There were many good source materials referenced/made
available.
• I enjoyed the way the course was structured and how each module included
relevant and easy digestible information
• I enjoyed the PPT videos a great deal. I think the topics will stick with me and I will
be much more thoughtful when I create charts throughout my career.
• Assignments were great practice as were the opportunity to give and receive
feedback.
• I would have liked to learn a bit more about certain tools the instructor finds
valuable. I work mostly in excel and PowerPoint for my job but am interested in
learning quick ways to improve my visualizations with other tools and resources
• Great professor. I was very happy with the teaching style and the course overall.
• I found the feedback process to be very helpful-both giving and receiving. Unless it
is a very large class, I think everyone should have to provide feedback on
everyone's assignment.