Data science skills are increasingly important for research and industry projects. With complex data science projects, however, come complex needs for understanding and communicating analysis processes and results. The rise of data science has accompanied a comparable rise in business intelligence and the demand for visualizations and dashboards that can explain models, summarize results, assist with decision making, and even predict outcomes. Ultimately, an analyst’s data science toolbox is incomplete without visualization skills. This talk will explore the landscape of visualization for data science – using visualization for data exploration and communication, reproducible approaches to visualization, and how to develop better instincts for visualization choice and graphic design.

1. Visualization for
Data Science
Angela Zoss
Duke University Libraries
angela.zoss@duke.edu
NCDS DataBytes Webinar
May 3, 2017
Slides: http://bit.ly/vis4ds

2. Angela Zoss
Data Vis.
Coordinator,
Duke University
Libraries
Angela Zoss
http://library.duke.edu/data

3. Today’s Topics
• Approaches to visualization
• Visualization for data science
• Developing your design instincts
• Reproducibility and visualization

4. VISUALIZATION APPROACHES

5. Vis for
Everyone
http://guides.library.duke.edu/vis_types

6. Vis for
Utility
http://web.mta.info/nyct/maps/subway_map.pdf

7. Vis for
News
https://nyti.ms/Wr1dhZ http://www.stanfordkaystudio.com/
information.html

8. Vis for
Entertainment
http://www.informationisbeautiful.net/visualizations/a-taxonomy-of-
hipster-coffee-shop-names/

9. Vis for Art
http://www.dear-data.com/theproject

10. Vis for
Activism
http://guns.periscopic.com

11. Vis for
Research
http://atlas.cid.harvard.edu

12. Vis for
Business
https://datastudio.google.com

13. VISUALIZATION FOR DATA SCIENCE

14. Transforming
Data
Jeff Heer
http://bit.ly/HeerVisProcess

15. Visualization for
• Exploration
• Communication

16. Visualization
for Exploration
Distributions

17. Visualization
for Exploration
Correlations

18. Visualization
for Exploration
Testing statistical
assumptions
3.1 3.2 3.3 3.4 3.5
−2.0−1.00.01.0
Fitted values
Residuals
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Residuals vs Fitted
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−2 −1 0 1 2
−4−2012
Theoretical Quantiles
Standardizedresiduals
Normal Q−Q
100
98
92
3.1 3.2 3.3 3.4 3.5
0.00.51.01.52.0
Fitted values
Standardizedresiduals
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Scale−Location
100
98
92
0.00 0.02 0.04 0.06 0.08 0.10
−4−202
Leverage
Standardizedresiduals
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Cook's distance
1
0.5
Residuals vs Leverage
99
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34
http://graphicaldescriptives.org
http://dx.doi.org/10.1177/1745691616663875

19. Visualization
for Exploration
Insight through
experimentation
Nathan Yau
http://flowingdata.com/2017/01/24/one-dataset-visualized-25-ways/

20. Visualization for
Communication
Annotations

21. Visualization for
Communication
Storytelling
Hans Rosling
http://www.youtube.com/watch?v=OwII-dwh-bk

22. Visualization for
Communication
Dashboards
Shamik Sharma, via
https://www.perceptualedge.com/blog/?p=1374

23. DESIGNING VISUALIZATIONS

24. Importance of
Design
Insights depend
on using the
right chart
for the right
question
Kandel, Heer, Plaisant, et al. (2011)
http://dx.doi.org/10.1177/1473871611415994

25. Importance of
Design
Insights depend
on using the
right chart
for the right
question
Kandel, Heer, Plaisant, et al. (2011)
http://dx.doi.org/10.1177/1473871611415994

26. Importance of
Design
Insights depend
on using the
right chart
for the right
question
5000-item result limit
Silent failure
Kandel, Heer, Plaisant, et al. (2011)
http://dx.doi.org/10.1177/1473871611415994

27. Importance of
Design
Insights depend
on matching
data properties
to visual
properties
Borkin, Gajos, Peters, et al. (2011)
http://bit.ly/hemovis

28. Design
Instincts
Be comfortable
with a variety
of charts
http://www.datavizcatalogue.com/

29. Design
Instincts
Follow the
work of other
designers
https://medium.com/accurat-studio/the-architecture-
of-a-data-visualization-470b807799b4

30. Design
Instincts
Get feedback
http://dataremixed.com/2016/04/the-
design-of-everyday-visualizations

31. VISUALIZATION AND
REPRODUCIBILITY

32. Reproducible
Research
“[T]he ability to
recompute
results”
“Can I trust this
analysis?”
Not the same as
trusting the
results
Jeffrey T. Leek, and Roger D. Peng PNAS 2015;112:1645-1646
http://dx.doi.org/10.1073/pnas.1421412111
Peer review and editor evaluation help treat poor data analysis.

33. Reproducible
Visualization
The ability to
regenerate
visualizations
Doesn’t ensure
effectiveness
of visualization

34. Scripting
Visualization
JMP Pro (proprietary)
R (fully open)
MATLAB (proprietary)
Python (fully open)

35. Why create reproducible
visualizations?
• Transparency of process
• Easy to recreate previous figures
• Easy to create multiple figures
that have a similar style
…, but often much harder to
customize, add design elements

36. Reproducible
Design
http://vdl.sci.utah.edu/publications/2016_infovis_hanpuku

37. LESSONS FOR DATA SCIENCE

38. Visualization:
• Exploits powerful our visual processing
system
• Can improve data exploration and
communication
• Requires thoughtful design choices for
full impact
• Will become increasingly reproducible

39. RESOURCES

40. Learn more about
visualization
• Data Matters 2017 (Raleigh, NC)
http://datamatters.org
• Stephanie Evergreen’s
Data Visualization Academy (virtual)
http://academy.stephanieevergreen.com
• VDS Workshop at IEEE VIS conference
http://www.visualdatascience.org/
• In-person training, e.g.:
– Andy Kirk
http://www.visualisingdata.com/training
– Cole Nussbaumer Knaflic
http://www.storytellingwithdata.com/public-workshops

41. QUESTIONS?
Angela Zoss
angela.zoss@duke.edu
Slides: http://bit.ly/vis4ds