One Course: Data Visualization
“Students are immersed in digital media today,” says Professor and Co-Chair of Economics and Business Santhi Hejeebu. “Data graphs can be misleading and consumed hastily, not allowing enough time to separate the useless parts from the meaningful parts.”
She developed the Data Visualization course at Cornell College with two aims: to help students consume content more patiently and coherently by explaining how intentional design choices influence viewers; and to enable students to create more honest and impactful visualizations, through questioning datasets and exploring quantitative relationships.
She starts with the history of data visualization and the basics of visual perception. She then shares broad principles of effective design and develops essential quantitative literacy skills. Hejeebu also instructs students to use data visualization in equitable and inclusive ways.
“We are responsible for the implicit messages our visualizations portray,” she says. “We have to be mindful of how our graphs will be used.”
How they learn
Students learn through the daily practices of reading visualizations and creating them. Students use tools like Excel and Tableau, Hejeebu says, as a means to an end and not the end itself. Data visualization tools change over time, but the underlying design principles and quantitative identities remain steady. By the end of the course, even the “numbers shy” students will be comfortable working with quantitative data.
Hejeebu’s interdisciplinary approach draws on economics, descriptive statistics, visual design, and other fields.
“By learning to distinguish between good and bad vizzes, you begin to internalize the Gestalt principles of visualization and understand how the eye sees,” she says.
Students say
“A visualization becomes great when you can communicate the takeaways that are of interest to the target audience.” —Drew Logel ’23
“Effective visualizations tell the story I want to tell about the data.” —Mike Knight ’23
“Data Visualization taught me how to illustrate quantitative data in a way that maximizes the speed and depth of viewer comprehension.” —Kimberly Maitland ’24
Watch Do No Harm: Equity Awareness in Data Visualization, one of the videos Hejeebu assigns to her students. The redline map (the practice of outlining areas with sizable Black populations in red ink on maps as a warning to mortgage lenders) of Richmond, Virginia, was developed for the Homeowners’ Loan Corporation (1923).
This data visualization, Hejeebu says, did a lot of economic harm to the communities depicted.
