on teaching data viz

wrapping up my first semester facilitating a 10-week data storytelling course

Author

Simran Parwani
April 30, 2025

my approach

“Data visualization is a subjective practice. There’s always a tradeoff to every decision from what you include, to the visual form you choose, to the language you use. My grading and feedback will be based more on your rationale and ownership of your choices, rather than what I perceive as a “good” visualization, especially when my own lens of what is "good" has been shaped by a western lens and visualization research often produced by a hegemonic group. If there is a grade or comment you disagree with, please reach out → in the real world, collaboration is critical for the data storytelling process.”

-My grading policy from the course syllabus

Designing and facilitating a data storytelling course at the University of Vermont was a powerful exercise in clarifying how I think about and practice my craft during a time of jadedness about data visualization. At the same time, I acknowledged the subjectivity of the field and emphasized that there are many ways of creating data stories. Here are some other parts of the course design:

  • Creating a collective classroom approach: referring to myself as the course “facilitator” and creating space for peer mentorship in weekly discussions and in online classroom space. Next semester, I plan to do the assignments alongside the students and share with them for feedback.

  • Including a reflection and rationale component in each assignment: giving students the space to share how they were feeling about their data stories and asking them to explain why they were making the decisions they were. I wanted to bring the human elements of making data viz as part of the course.

  • Acknowledging the difficult aspects of this work and normalizing the struggle of learning new skills in a short period of time: Being vulnerable about my own struggles with data storytelling and being upfront about difficult assignments helped students feel like there was nothing “wrong with them” if they were experiencing difficulty and built a culture of honesty and openness among students

  • Establishing guiding principles to set expectations from the beginning

Guiding principles such as we all have something to learn and to teach, there is no one way to create a data story, and that we have a responsibility to work with data with care and integrity

Guiding principles from the course syllabus

some of the readings!

Cover of Data Feminism book

Data Feminism by Catherine D’Ignazio and Lauren Klein

Data Feminism

The first reading to ground students in examining why a data source may exist or not and the threads of power underlying those decisions.

Screenshot of Invisible Epidemic project shared in newsletter post

Big Charts newsletter by Alvin Chang

Big Charts

Chang is extremely generous in sharing the behind-the-scenes of his projects for The Pudding. His newsletter is helpful for students to see the twists and turns their own data stories may take.

“Emphasize what you want readers to see with color” by Lisa Charlotte Muth for the Datawrapper blog

Color in data viz blog post

Lisa Charlotte Muth’s resources for thinking about color in data viz are practical and thoughtful — I’m very excited for her book on color in data viz.

Image of artwork Library of Missing Datasets 2.0

the Missing Datasets series by Mimi Ọnụọha

The Library of Missing Datasets

This art project by Mimi Ọnụọha got students thinking about what datasets are missing in their own communities.

reflections

This course left a lot of traces on me

  • Working with students was inspiring and grounded me in why this work still matters, especially in this political climate of disappearing datasets.

  • One of my students was very passionate about birds, giving me a newfound appreciation for them and prompting me to enroll in Jer Thorp’s birding and data viz course!

  • I’ve been wanting to become more concise in how I communicate technical and design decisions. Creating lectures gave me more repetitions in distilling and sharing knowledge.

  • My editing skills sharpened. Grading 28 data stories every week honed this muscle of finding what people really wanted to say and ideating ways to showcase that story better.

Some of the feedback I’ve gotten so far!

  • “This was one of the most valuable learning experiences I have ever been enrolled in.”

  • “You clearly have a lot of experience and expertise in this field. Not only that—you are also great at communicating that expertise in an understandable way.”

  • “I really appreciated how you purposefully built authentic community in an otherwise asynchronous setting…”

I will be teaching this course again in the fall and am swirling with ideas for improving it! I also hope to better manage my own time teaching on top of my usual work.

A screenshot of the farewell message to students thanking them for their engagement in the course, reminding them that the skills they've learned are applicable to many areas, and asking them to share their data story project with someone else.

the parting note shared with students