This document summarizes a presentation by Kristen Sosulski on teaching data visualization. It discusses her background and experience teaching courses on data visualization to MBA and analytics students. It outlines challenges in teaching students to design visualizations that provide insights rather than just being visually appealing. The presentation covers using software like Tableau to incorporate annotation, animation, and interactivity into visualizations. It also provides techniques for effectively presenting visualizations, including identifying key takeaways, putting findings in context, and presenting key numbers. Students practice these skills through individual and group projects involving live and video presentations with feedback.

1. Teaching with Tableau: Showing
insights and telling data-driven stories
Professor Kristen Sosulski

2. Today’s Speaker
Kristen Sosulski, Ed.D
Associate Professor
Director of Education
W. R. Berkley Innovation Lab
New York University
Leonard N. Stern School of
Business

3. Bio
Dr. Kristen Sosulski develops innovative practices for higher education as the
Director of Education for the NYU Stern W.R. Berkley Innovation Lab. She also
teaches MBA students and executives data visualization, R programming, and
operations management as an Associate Professor at NYU’s Stern School of
Business.
As a leading expert on data visualization, Kristen regularly consults, delivers
seminars, and leads workshops on data visualization techniques and best
practices. You can find her speaking on the subject at events like Social Media
Week NYC and to organizations like the National Association of Pubic Opinion,
Digital Analytics Association, and the National Economic Research Associates.
Follow Kristen on Twitter at @sosulski and learn more at
http://kristensosulski.com

4. College/University Background
Course 1: Data Visualization
Audience: 50 MBA students from NYU Stern
Format: Blended Online
Course 2: Data Visualization
Audience: 70 NYU Stern Master Science in Business Analytics students
Format: Intensive 2-day session with asynchronous online activities

5. The Challenge
In data visualization courses, students learn to present data in visual form. This
involves working with data, learning new software, and applying visual design
principles.
Sometimes imaging software by default enables us to create beautiful
visualizations.
However, designing visualizations that are readable and provide key insights is
much more difficult.
Visualizations are only as effective as the insights they reveal.
How can professors support students in their process of creating purposeful and
interpretable visualizations and use they as powerful tools in their presentations?

6. As visualization designers we are “melding the
skills of computer science, statistics, artistic
design, and storytelling.”
K. Cukier (2010). Show me: New ways of visualizing data.
http://www.economist.com/node/15557455

7. Visualization is a translation process
Real world Data
Shapes
and colors

8. My courses in data visualization are designed to provide students with the
techniques to communicate insights, allow them to apply those techniques,
and to receive feedback on how well they’ve applied those techniques.
This done through 1) demonstration 2) practice & application 3)
critique and 4) expert and peer feedback.

9. PART I: The use of software to support data
presentation in visual form
PART II: Using visualizations effectively in a
presentation
PART III: Application, practice, & feedback

10. PART I: The use of software to support data
presentation in visual form
PART II: Using visualizations effectively in a
presentation
PART III: Application, practice, & feedback

11. Three features to build into your visualizations
1. Annotation: To highlight and direct the user’s attention.
2. Animation: To walk the user through the visualization, step by step. To
show and explain the data points at a slower pace.
3. Interactivity: To provide summary level data and details on demand.
To engage and involve the audience.

12. Annotation and encodings
• Emphasize the purposeful use of pre-attentive attributes.
• Highlight a data point, using a pre-attentive attribute.
• Avoid highlighting every data point.
• Reserve the use of pre-attentive attributes as cues for your audience.
This can be easily done in Tableau.

17. Animation
• To progressively reveal content.
• To mark specific data points at specific times.
• To show time series data.

18. Animations work well for showing times series
data for one or more categories.

19. Animations allow the user to start, stop, rewind, and
fast forward.

20. Animation provides a navigational feature to a
visualization.

21. Trend animation
• Trend animation shows all trends simultaneously.
• It works best for presentation rather than analysis.
• It is limited to approximately 200 data points on a single display.

23. • Use the pages card
• Drag year (or time-
based data) to the
pages card
• Filter time series data
using the filters card
• Determine the speed
(slow, normal, or fast)
• Select color of
encodings from the
marks card.

24. Trace animation
• Trace animation uses fade-in bubbles/links to show the direction of
the flow of data points and history.
• Traces work best for analysis when the result is not cluttered.
• Beware of too many data points and visual clutter.
• Consider small multiples for analysis rather than animation.

26. • Select Show History
from the Pages marks
card.
• Select show history for
all
• Select show Marks
• Select a color and
transparency for the
marks

27. Transition animation
• Short animation keeps users in context during view/data
transitions.
• These usually follow an action, such as connect, select,
or explore, for interactive displays.

29. • Click to highlight the
data point that you
want to keep
highlighted
• Works nicely when
combined with trace
animation for time
series data where
categories change
over time.

30. Interactivity
• To enable audience interaction and involvement
• To filter details on demand
• To traverse the data set to compare and contrast different
attributes.

31. 31

32. Slider
Input field
Highlight
Filter

33. PART I: The use of software to support data
presentation in visual form
PART II: Using visualizations effectively in a
presentation
PART III: Application, practice, and feedback

34. Three techniques for presentation

35. Technique #1: Identify the key takeaway
• Provide clear takeaways for each visualization.
• Write it in the notes or the title of the slide.

36. Today, the largest shipping ports are in Asia, with three of five located in
China.
Kristen Sosulski | Source: World Bank - Container Port Traffic (2014).
36

37. Technique #2: Put your findings in context
• Provide a context for your findings.
• Without a context, data is meaningless.
• In the example on the next slide, an insight is communicated that puts
the number one position (Shanghai) in context and provides an
explanation for the rise to the top.

38. Since 2004 the capacity at the Port of Shanghai has grown from 14 million
TEUs to more than 32 million in 2013, giving rise to its # 1 position in terms
of TEU volume.
Kristen Sosulski | Source: World Bank - Container Port Traffic (2014).

39. Technique #3: Present the key numbers
• It is important to summarize the key findings and present the numbers
in a meaningful context that is comprehensible to the audience.
• For example, it may be more helpful to show a percentage change
from year to year when presenting an increase over time, rather than
with absolute numbers.
• Specifically, if the core point is to compare the change from year to
year, percentage change is an effective metric.

40. 20,172.3
23,640.2 24,416
25,816.8 26,433.5
2010 2011 2012 2013 2014
China’s total import and export value from 2010 to 2014 (in billion Yuan)
There was a 31% increase from 2010 to 2014
Kristen Sosulski | Source: Statistica (2014).

41. Summary
By incorporating these tips students can tell better stories with their data and use
visualizations to reveal important insights about the data
Identify the takeaway Contextualize findings Present the key numbers

42. PART I: The use of software to support data
presentation in visual form
PART II: Using visualizations effectively in a
presentation
PART III: Application, practice, and feedback

43. Application: How do you have students practice
applying these techniques?
Individual Project
1) 2 minute live presentation
2) 2 minute video presentation
Group Project
20 minute live presentation with peer feedback and critique

44. Individual
Project

45. 34
PITCHES

46. 2 MINUTES
EACH

49. NOTE &

50. VOTE

51. Student / Peer voting criteria

52. TOP 9

53. MEET

54. DISCUSS

55. EAT

56. Instructor - Assessment Rubric: Individual Project
Criteria
0 = Lacking, 3 =
Excellent
Creative Idea 3
Compelling Idea 3
Well-conceived idea 2
Clear proof of concept 2
Visuals targeted @ appropriate audience 1
Data represented accurately 2
Data represented adequately 2
Visuals exhibit good design principles 3
Well-designed slide presentation 3
Persuasive / compelling presentation 2

57. Observed Pitfalls
• Over time limit (#4)
• Talked over visuals without explicitly
referencing them (#8)
• Looking at the board
• Lack of audience engagement (#1)
• Lack of a clear vision or story (#1, #7)
• Too much information (#5)

59. Group project

60. Proposed workflow
Plan Storyboard Design Build
Rehearse,
test, and
revise

61. • 20 minutes
• 10 minute Q & A
• All team members must be present, but all do
not have to present. Peer feedback and
ratings will be factored into your grade.
• Review the presentation testing & delivery
standards

62. • Is sitting the new smoking?
• Refugee crisis: Is it really that bad?
• Measuring good neighbors: Social cohesion index
• Do you believe in UFO sightings?
• Use movie compass to pick your next flick.
• Making decisions about student loan debt and college
• Election outcome prediction. Can twitter data help?
• How does AirBnB affect branded hotels?
• Twitter and the response to current events
Group project topics

63. Audience guidelines and role
• No laptops
• Respond to prompts by the presenters (if
applicable)
• Take note of what worked well and areas for
refinement
• Share you comments and feedback with the
team during the critique

64. Critique – 10 minutes
• How well did the team tell a story with data?
• How well did the team select visual displays to
present their data?
• How well did the team do at communicating key
insights?
• What are the key takeaways from the
presentation?
• What worked well? Do you have suggestions for
future work?

65. Assessment Rubric: Group Projects

66. Assessment Rubric: Group Projects

67. Assessment Rubric: Group Projects

68. What worked well?
• Concise stories
• Use of data and visuals as evidence
• Questions or prompts for the audience
• Dynamic presenters
• Interesting data and presentation of that data
• Data points were put in context
• Provided reference points or points of comparison

69. Interesting in learning more and keeping in touch?
Go to my website and sign up for my mailing list at:
http://bit.ly/datavisupdates
You’ll immediately receive access to:
“Ten common pitfalls for presentations and data
visualizations?

70. Upcoming Talks
May 16th: Digital Analytics Association - Engaged
Storytelling with Information Visualization: Techniques
for Audience and Presenter Driven Stories
November 16th: Plot Com. The Plot.ly Conference

71. Live Q&A