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.
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
Visualization For Data Science
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
3. Today’s Topics
• Approaches to visualization
• Visualization for data science
• Developing your design instincts
• Reproducibility and visualization
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
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.
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
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