The presentation will provide an introductory overview of quantitative data visualization and will highlight selected best practices. Topics will include a summary of available visualization tools, tips on successfully leveraging color, and a few points relating to visual perception. Participants will be introduced to some of the key principles of graphical integrity and design promoted by major thinkers in the field, such as Edward Tufte and Stephen Few. A section of the presentation will be devoted display methods. Most of this discussion will focus on charts and graphs, but it will also cover when using a table is more advantageous. The scope of the chart discussion will include a description of the major chart types (bar, line, pie, scatter, etc.), their appropriate uses, and some areas where chart designers should remain vigilant lest they distort or obscure the data. The audience will be engaged through the interactive polling and chat features. For example, direct requests for audience feedback are planned for some of the points relating to perception, comparing chart types, and clean design practices.
Learning Outcomes:
- Participants will be able to identify several best practices for data visualization.
- Participants will be able to apply basic principles of color theory in the creation of their visualizations.
- Participants will be able to understand the appropriate uses for the major chart types, select the best types to showcase their data, and avoid common charting pitfalls that distort the data.
Web Presenter: Tricia Clayton, Collection Services Librarian, Georgia State University
Minglu Wang received a Master's Degree in Sociology from China and a Masters of Library and Information Science from the University of Iowa. She was a Digital Library Fellow at the University of Iowa.She is currently a Data Services Librarian at John Cotton Dana Library of Rutgers University. She teaches workshops on the use of varied statistical software packages, helps individual faculty or graduate students solve their data analysis problems, provides consulting on data management, and conducts data analysis and visualization for faculty, research centers, and other departments on the Rutgers-Newark campus.
3:30 Wrap-up Discussion
You may register for this event by logging into the NJLA CUS/ACRL-NJ website, and clicking the sign-up tab/link associated with this event (if you are not logged in you will not see the Sign-up link). Don't have an account on our website? You can register for an account and sign-up for this event at the same time by visiting this link -- http://cus.njla.org/user/register.
Parking will be available in the lot behind the Gymnasium on College Avenue and in the Parking Deck next to the library. Because it is a group event, no individual passes will be necessary.
If you have problems with the registration process, please email oshead@mail.montclair.edu.