8. Module Conclusion - Data Visualization
Recap
Congratulations! You have successfully completed the "Data Visualization" module. To recap:
- Data visualization is useful for helping to explain or explore data in more depth.
- There are different kinds of visualizations and some kinds express particular data types better than others (e.g., line graphs for changes over time, or maps for geographic data). Spend some time researching which visualization type best suits your data.
- Use design principles, like striving for simplicity, using color to highlight your most significant points, and incorporating white space to reduce visual clutter, to help your audience connect with your ideas.
Module Evaluation
Before you go, we would appreciate hearing your feedback on this module:
Data Visualization Canvas Module Feedback
Links to an external site.
Additional resources
- Article: Mason, Betsy. Why scientists need to be better at data visualization. 2019 Nov 12. https://www.knowablemagazine.org/article/mind/2019/science-data-visualization Links to an external site.
- Book: Tufte, Edward R. The Visual Display of Quantitative Information. Graphics Press, 1983. Available at U-M Library: https://search.lib.umich.edu/catalog/record/000314979 Links to an external site.
- Book: Evergreen, Stephanie D. H. Presenting Data Effectively : Communicating Your Findings for Maximum Impact. SAGE, 2014. Available at U-M Library: https://search.lib.umich.edu/catalog/record/013030754 Links to an external site.
- Book: Knaflic, Cole Nussbaumer. Storytelling with Data a Data Visualization Guide for Business Professionals. Wiley, 2015. Available online at U-M Library: https://search.lib.umich.edu/catalog/record/014703164 Links to an external site.
- Guide: Research Data Management (Health Sciences) - Data Visualization: https://guides.lib.umich.edu/datamanagement/dataviz Links to an external site.
- Blog: Evergreen Data. https://stephanieevergreen.com/blog/ Links to an external site.
Acknowledgments
This module was created by Sara Samuel and is based on presentations by Justin Joque and Kate Saylor. Additional colleagues at the U-M Library provided great feedback to improve the module.