This document outlines an agenda for a one-day workshop on data visualization and infographic design. The workshop aims to challenge existing thinking about creating and consuming visualizations, equip attendees with an appreciation of analytical and design choices, provide practice opportunities, and inspire attendees. The agenda covers fundamentals, the design methodology, data representation types, color theory, interactivity, and tools. Exercises are integrated throughout. The trainer, Andy Kirk, has extensive experience delivering visualization training globally.

1. Separating Myth from Truth
in Data Visualisation
Andy Kirk
www.visualisingdata.com
@visualisingdata

2. Visualisation specialist: Editor of visualisingdata.com
design consultant, trainer, lecturer, author, researcher...
DATAVISUALISATION AND INFOGRAPHIC DESIGN
Training Workshop
1-day ‘Introduction’ event
The agenda is constantly refined to accommodate the most contemporary thinking
and references. The final itinerary may be slightly different, and the precise times
may vary, but here is an indicative outline of the typical 1-day agenda.
9:30 WELCOME: Introduction to today’s workshop
9:45 FUNDAMENTALS: What is data visualisation?
10:00 Exercise – Instinctive critical evaluations | Review
10:30 THE DATA VISUALISATION DESIGN METHODOLOGY
10:35 1. Understanding context andestablishing purpose
10:50 Exercise – Visualising ideas within problem contexts | Review
11:05 BREAK
11:15 2. Acquiring, preparing and familiarising with your data
3. Establishing editorial focus with your subject matter
11:35 Exercise – Exploring data and our editorial focus | Review
12:00 4. Conceiving your visualisation design
Key principles of good visualisation design
12:15 LUNCH
13:05 Data representation, including a gallery of chart types and their roles
13:35 Exercise – Identifying data representation choices | Review
14:15 Colour theory and application
Exercise – Forensic design assessments (1)
14:35 BREAK
14:50 Interactivity and animation features
Exercise – Forensic design assessments (2)
15:10 Annotation and composition
Exercise – Forensic design assessments (3) | Review
15:45 5. Construction, tools and technology
16:00 Exercise – Developing a data visualisation concept | Review
16:50 WRAP UP: The ‘8 Hats’ of visualisation design, workshop review
17:00 FINISH
People attending these workshop sessions come from all backgrounds, organisation
types and domain areas.
You might be an analyst, statistician, or researcher looking to enhance your data
capabilities. Perhaps you’re a skilled designer or developer looking to take your
portfolio of work into a more data-driven direction. Maybe you do not personally
get involved in the analysis or visualisation of data but coordinate others who do.
You might be a frequent consumer of visualisation and infographic work looking to
increase the sophistication of how you read, interpret and evaluate the effectiveness
of such designs. These events are intended for all audience types and levels.
The most critical attribute is that you have an inherent curiosity, an instinct for
discovering and sharing insights from data, and an interest in approaching your
data visualisation and infographic work with a fresh perspective.
You should have a willingness to contribute to discussions during exercise activities
and do so in a respectful and constructive manner.
To view a selection of testimonials from previous workshop participants visit
www.visualisingdata.com and click on Training.
The ‘Introduction to Data Visualisation and Infographic Design’ 1-day workshops
aim to provide delegates with an accessible and comprehensive introduction to data
visualisation and infographic design.
The focus for this training is the craft of this discipline, helping delegates to know
what to think, when to think about and how to resolve all the analytical and
design decisions involved in any data-driven challenge. There are four over-riding
objectives for these workshops:
To challenge your existing thinking about creating and consuming visualisation
works, refining the clarity of your convictions about effective visualisation design.
To equip you with an appreciation of all the analytical and design choices available
across the creative workflow: the options that exist and what decisions to take.
Tho provide an opportunity to practice by undertaking focused activities at each
stage of the creative workflow, applying and cementing the learning at each stage.
To inspire you by broadening your visual vocabulary, by exposing you to the latest
techniques and contemporary resources, and by giving you the confidence to
enhance your data visualisation capabilities.
Andy Kirk is a UK-based data visualisation specialist: A design consultant, training
provider, lecturer, author, blog editor, speaker, and researcher.
Since launching his freelance career in late 2011, he has delivered nearly 150 public
and private training events in 14 countries across five continents. Recent clients
include PepsiCo, Standard Chartered, Cisco, Heineken, and CERN. Visit
www.visualisingdata.com and click on Training to see a map and profile of his past
training events.
Andy’s teaching activities extend to a visiting lecturer position at the Maryland
Institute College of Art (MICA) in the US where he teachers a module on the
Information Visualisation (MPS) Masters programme.
The workshop session is structured around a proven design methodology where we
will build up, stage by stage, a detailed understanding of all the different aspects of
decision-making that goes into all data visualisation or infographic design work.
Whilst the event is described as an ‘Introduction’ this does not mean it is pitched
at a ‘basic’ level: Data visualisation and infographic design teaching is really
framed by the time available - and the breadth/depth achievable - rather than any
distinction between content progressing from beginner towards advanced topics.
The content will be delivered through a blend of teaching, discussion, and group
practice. The practical exercises vary in nature from evaluating works, conceiving
ideas, identifying best-fit solutions and exploring data. The most technical of the
exercises will involve looking at data in Excel. There will also be some sketching
tasks but note these are not a test of artistic capability, rather conceptual thinking.
These workshops are designed to be technology neutral, they are not focused on
or based around any specific tool or programme. However, there will be a section
providing an overview of some of the most essential and common visualisation
tools currently in the market.
All materials will be issued digitally on USB memory sticks containing all training
content, exercise files and a range of useful references. Attendees are encouraged to
bring laptops to use as a workspace for the session. The only software requirements
are Excel, a browser and a pdf reader. No other technical prerequisites exist.
TRAINING OBJECTIVES
THE WORKSHOP
WHO SHOULD ATTEND?
TYPICALAGENDA
TRAINER PROFILE
REGISTER NOW! Visit www.visualisingdata.com and click on Training to find an event nearyou

3. The representation and presentation of data to facilitate understanding
What is data visualisation?

4. In data visualisation we often hear about the always and the
nevers but, invariably, it is far more nuanced and requires
more sophisticated convictions.
Which things are the always and which are the mostlys?
Which things are the nevers and which are the rarelys?
“Separating Myth from Truth in Data Visualisation”

5. CHARTS AND CHART
DESIGN

6. ______ charts are evil

7. “Pie charts are evil...”

8. Robert Kosara: “In defense of pie charts”
https://eagereyes.org/criticism/in-defense-of-pie-charts

9. Maximum 2-3 parts of a whole, start at the vertical

10. http://www.bricksetforum.com/discussion/21529/colors-how-theyve-changed
Value of pie charts for exploring data (vs. communicating)?

11. “Word clouds are evil...”

12. Google search: “Marketing Infographics”
“Infographics are evil...”

13. http://www.bmj.com/content/350/bmj.g7620/infographic |
http://lasombra.blogs.com/la_sombra_del_asno/2013/10/urban-oasis.html
Good infographics aren’t!

14. BAD PIE CHARTS ARE BAD CHARTS
BAD BAR CHARTS ARE BAD CHARTS
BAD WORD CLOUDS ARE BAD CHARTS
BAD RADAR CHARTS ARE BAD CHARTS
Repeat after me...

15. No chart is evil: They play different roles & all have limitations

16. 3D

17. http://blog.visual.ly/2ds-company-3ds-a-crowd/
Distorting decoration: Don’t create 3D out of 2D data

18. http://hmi.ucsd.edu/howmuchinfo.php
Distorting decoration: Can’t consume in 3D

19. Image from https://twitter.com/aquigley/status/739752961920933888
When is 3D legitimate?: When consuming in 3D...

20. http://www.nytimes.com/interactive/2015/03/19/upshot/3d-yield-curve-economic-growth.html?smid=pl-share&_r=1&abt=0002&abg=0
When is 3D legitimate?: When representing 3D data...

21. http://www.nytimes.com/interactive/2015/03/19/upshot/3d-yield-curve-economic-growth.html?smid=pl-share&_r=1&abt=0002&abg=0
When is 3D legitimate?: ...and when offering a series of 2D views

22. Truncated axes

23. Truncated axes: Bar charts

24. https://twitter.com/nro/status/676516015078039556
Truncated axes: Line charts

25. https://twitter.com/nro/status/676516015078039556
Truncated axes: Line charts

26. Truncated axes: Area charts

27. Geometric calculations

28. $0.8M out of $7.5M = 10.7%
Length of presented bar progress = 24.6%
Size miscalculations

29. If the value of A is twice as big as the value of B...

30. Data:Ink ratio & “Chart Junk”

31. Data:Ink Ratio: Too brutal, even for the era of print-only
http://www.perceptualedge.com/articles/visual_business_intelligence/sometimes_we_must_raise_our_voices.pdf

32. http://seeingdata.demo.cleverfranke.com/census/
Data:Ink Ratio: “Ink” creates unity, contrast, organisation

33. Data:Ink Ratio: “Ink” creates unity, contrast, organisation

34. https://paragraft.wordpress.com/2008/06/03/the-chart-junk-of-steve-jobs/
Chart Junk: Incongruent or obstructive

35. https://www.statista.com/chart/4101/where-is-pensioner-poverty-the-most-prevalent/ | https://twitter.com/olafcramme/status/523397542172950528 |
http://exceluser.com/blog/1152/oh-no-chart-junk-from-the-wall-street-journal.html
Chart Junk: Incongruent or obstructive

36. Chart junk? Congruent and unobstructive
http://www.wsj.com/articles/SB12147335600370333763904581058081668712042 |
http://www.bloomberg.com/news/features/2015-03-03/junk-food-s-last-stand-the-pizza-lobby-is-not-backing-down

37. Rainbow colour schemes for
showing quantitative values

38. http://eagereyes.org/basics/rainbow-color-map
Multi-hue colour scheme: Magnitude & order?

39. http://www.nzz.ch/aktuell/inland-sommerserie-schweizer-karten-interaktiv/vergleich-strom-versorger-1.18120907
Convergent colour scheme: Magnitude & order

40. Divergent colour scheme: Magnitude & order
http://www.randalolson.com/2015/09/05/visualizing-indego-bike-share-usage-patterns-in-philadelphia-part-2/

41. Red & green
(should they ever be seen?)

42. Impact of colour blindness (deuteranopia)

43. #a6611a
#7b3294
#d01c8b
#018571
#008837
#4dac26
#d7191c #2c7bb6
To mean ’GOOD’ To mean ’BAD’
Alternative colours to replace the default green and red

44. Maybe nobody in your audience is colour-blind?

45. CLAIMS OF
UNIVERSAL TRUTH

46. “Data visualisation is
all about storytelling”

47. Stories can be seen through temporal charts
http://www.nytimes.com/interactive/2016/us/elections/polls.html?_r=0

48. Stories can be seen through temporal charts

49. Stories can emerge from temporal presentations

50. Stories can emerge from temporal presentations

51. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0123507
Stories can emerge from temporal presentations

52. https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen?language=en
Stories can emerge through verbal annotation (chart as a prop)

53. Difference between storytelling and storyforming
The stories we form in our mind through interpretation

54. Difference between storytelling and storyforming
The stories we form in our mind through interpretation

55. “Data visualisation is about
perceptual accuracy”

56. Ranking of elementary perceptual tasks
From Cleveland & McGill, ‘Graphical Perception: Theory, experimentation and application to the development of graphical methods’”, 1984

57. B=5
A=10
Length
Area
A=10 B=5
Ranking of elementary perceptual tasks
Discernibility of attributes: Quantitative data

58. “The ordering does not result in a precise prescription for
displaying data but rather is a framework within which to work.”
Cleveland & McGill
Ranking of elementary perceptual tasks
From Cleveland & McGill, ‘Graphical Perception: Theory, experimentation and application to the development of graphical methods’”, 1984

59. What’s the right level of discernibility?

60. What is the shape of the data you are trying to encode?

61. “Data visualisation is
about simplicity”

62. http://io9.com/5912155/watch-hans-rosling-use-rocks-to-deliver-the-shortest-ted-talk-ever
Complex subject, made simple

63. http://www.fastcodesign.com/1672691/imdbs-top-50-movies-arranged-by-genre#1
Simple subject, made unclear

64. Simple subject, shown as clearly as possible

65. https://twitter.com/theboysmithy/status/705323516711804928
Complex subject, shown as clearly as possible

66. http://www.visualisingdata.com/index.php/2013/01/book-review-the-functional-art-by-alberto-cairo/
Oversimplification = obscuring

67. “Data visualisation is about
immediate understanding”

68. http://oecdbetterlifeindex.org/
Some things can be quickly consumed...

69. http://13pt.com/projects/nyt071211/
...other things take longer

70. Some visualisation techniques are familiar...

71. http://www.bloomberg.com/infographics/2014-01-16/tracking-super-bowl-ticket-prices.html
...others are less familiar

72. Learnability: Everything is new, once

73. “Visualising ‘Big Data’ requires
special techniques”

74. Visualising big data isn't a data problem, it's a summarisation
problem. You've only got so many pixels on the screen.
[Paraphrasing] Hadley Wickham
https://twitter.com/jsteeleeditor/status/434039532535562241
“Big Data” visualisation

75. “Big Data” visualisation
http://demographics.coopercenter.org/DotMap/index.html

76. “Big Data” visualisation
http://demographics.coopercenter.org/DotMap/index.html

77. CLOSING REMARKS

78. In the absence of perfect, optimisation is the pursuit:
Maximum effectiveness through maximum efficiency
Data visualisation is always about compromise

79. To make the best decisions you need to be familiar with all your
options and aware of the things that will influence your choices.
Data visualisation is always about good decisions
THINGS YOU
COULD DO
THINGS YOU
WILL DO

80. BOOK! Data Visualisation: A Handbook for Data Driven Design

84. Separating Myth from Truth
in Data Visualisation
Andy Kirk
www.visualisingdata.com
@visualisingdata