The Difference Between Teaching and Doing Data Visualization—and Why One Helps the Other
We asked six professors from different fields how and why they teach data visualization
“Why do you teach data visualization?” I’ve been asked this question many, many times. Between working full-time and teaching part-time, I had virtually no free time, so people would ask again: “Why do you teach data visualization?” Initially, my answer was: “…because I really enjoy it?!” Over time, the more I had to answer this question, the more I realized that I was teaching because “teaching is the highest form of understanding.”
These are not my original words. As contemporary as they might sound, they were first written over 2,000 years ago in Greece by Aristotle. Back in 2016, when I discovered data visualization, I learned, like many others have noted (see previous essays by Nick Brown, Will Chase), by READING a ton of books, PRACTICING again and again, and WRITING very diligently. What I realized as I started teaching was that there was a fourth, key pillar of learning: TEACHING.
I spoke with six different data visualization professors about how and why they teach data visualization. This article compiles some of my insights and takeaways from those conversations. But first, let me tell you a bit more about myself and about how I became interested in this topic.
In 2018, a great opportunity came knocking at my door. DePaul University in Chicago asked me to create and teach a data visualization course for the university’s Public Relations and Advertising students. At first, I found the idea daunting. I found myself wondering how teaching is or should be different from learning. I spent countless hours researching various teaching strategies, and trying to understand what type of exercises, tools, and readings would make most sense for my students. Remember, my students’ backgrounds were different from mine, so I had to put myself in their shoes and think deeply about how they could best learn data visualization. I accepted the offer, not knowing how much I would learn from the experience.
A year later, a second opportunity came about: The University of Chicago asked me to teach data visualization and storytelling as part of The University of Chicago Professional Education (UCPE) program. Unlike the program at DePaul University, which catered to advertising and PR students, this certificate was designed for business professionals with a few years of work experience who wanted to improve their data understanding, analysis, and visualization skills. The program focuses on teaching students how to translate data into insights and strategy. Teaching data visualization to professionals coming from a wide range of industries (e.g. finance, data science, engineering) made me realize that data visualization is as valuable to anyone working with data as grammar is to anyone working with words. One should not write an essay without proper grammar, just as one should not create a graph without first mastering data visualization best practices.
As I was embarking on this new journey and working to adjust my teaching approach to address business professionals, I started to recognize that data visualization has a very broad and diverse audience. For example, some practitioners (including myself) teach data visualization in public or corporate workshops. Bootcamps all over the world have incorporated data visualization into their curriculum. Online platforms, such as Coursera or Udemy, offer dozens of course options. Universities have incorporated classes in data visualization across a broad range of disciplines, including business, psychology, computer science, communications, and journalism.
And this takes me to the reason why I decided to write this article. As I was deep into researching teaching strategies, it occurred to me that there’s very limited information published online about teaching data visualization. The data visualization community has talked and written extensively about how to learn data visualization, but one, myself included, could learn so much by understanding how and why data visualization is being taught across various universities and departments.
“It is important for students to also understand what comes before and after creating visualizations.” —Isabel Meirelles
With this mission in mind, I reached out to six different data visualization professors:
—Alberto Cairo, University of Miami
—Niklas Elmqvist, University of Maryland, College Park
—Steven Franconeri , Northwestern University
—Jessica Hullman, Northwestern University
—Isabel Meirelles, OCAD University
—Lace Padilla, University of California Merced
We talked about where they teach data visualization, their backgrounds, teaching philosophies, and strategies and tools they use in the classroom.
Introductions
What stood out immediately and led to rich conversations, was that the folks that I interviewed had very distinctive backgrounds, ranging from cognitive or computer science, to design, poetics, and journalism. This made sense. While there are many BA and MA degrees that incorporate data visualization classes, there are very few degrees in the U.S. that focus solely on data visualization. Three that I was able to find pretty quickly were the Data Visualization MS at Parsons, the Data Analytics and Visualization MS at the Pratt Institute, and the Information Design and Visualization MS at Northeastern University. Isabel Meirelles, one of the instructors that I interviewed for this essay, helped create the latter.
Alberto Cairo
Alberto has a background in journalism. He received his BA from the University of Santiago de Compostela and his MA, as well as PhD from the Universitat Oberta de Catalunya in Spain. In late 1990s, Alberto started working on print graphics. Later, his career moved towards data-journalism and information design. Currently, Alberto is the Knight Chair in Visual Journalism at the School of Communication of the University of Miami. He is also the author of several books. Among them, How Charts Lie: Getting Smarter about Visual Information and The Truthful Art: Data, Charts, and Maps for Communication.
Niklas Elmqvist
Niklas has a background in science. He holds a BA and MA in Computer Science and Engineering, and a PhD in Computer Science, all from the Chalmers University of Technology in Gothenburg, Sweden. Niklas is currently a professor in the iSchool (College of Information Studies) at University of Maryland, College Park. He studies information visualization, human-computer interaction, and visual analytics. Since 2016, Niklas is also the director of the Human-Computer Interaction Laboratory (HCIL) at University of Maryland.
Steven Franconeri
Steven’s undergraduate training was in computer science and cognitive science at Rutgers University, followed by a PhD in Experimental Psychology from Harvard University. He is a Professor of Psychology in the Department of Psychology at Northwestern University. In addition, Steven is the director of the Visual Thinking Laboratory at Northwestern University, where a team of researchers explores how leveraging the visual system can help people think, remember, and communicate more efficiently.
Jessica Hullman
Jessica’s background includes both science and art. After an interdisciplinary undergrad degree split between science and the humanities, she did an MFA (Experimental Poetics and Prose), but then quickly vacillated back towards science, earning a Master’s in Information Analysis & Retrieval followed by a Ph.D. from the University of Michigan. Jessica is currently an Assistant Professor of Computer Science & Engineering at Northwestern University, where she directs the MU Collective, a lab devoted to information visualization and uncertainty cognition. She has a secondary appointment as an Assistant Professor in the Medill School of Journalism at Northwestern University.
Isabel Meirelles
Isabel has a background in design. She studied Architecture and Urban Design at Faculdade de Belas Artes in São Paulo, Brazil. She also holds two master’s degrees, one in history and theory of architecture from the Architectural Association School of Architecture in London, and one in communication design from the Massachusetts College of Art and Design, Boston. Isabel is currently a Professor in the Faculty of Design at OCAD University in Toronto, Canada. She is also a researcher at the Visual Analytics Lab, OCAD University and the author of the book Design for Information: An introduction to the histories, theories, and best practices behind effective information visualizations. Isabel is the co-founder of Information+ Conference, a biannual interdisciplinary conference, workshop, and exhibition that brings together researchers, educators, and practitioners in information design and data visualization.
Lace Padilla
Lace has an interdisciplinary background. Her first degrees, both bachelors and masters, are in art and design. When she discovered that there is science that tries to understand how people decode visual information, she switched paths and went on to graduate with a MA and a PhD in Cognitive and Neural Sciences from the University of Utah. Currently, Lace is an Assistant Professor in the Cognitive and Information Sciences department at the University of California Merced. She studies visual decision-making and spatial cognition.
Teaching Philosophy
While teaching philosophies vary, one main theme emerged. Data visualization is both craft and science. The degree to which each person interviewed sees data visualization as being more of a craft or more of a science varied, but everyone agreed that data visualization as a field of study is somewhere on a sliding scale between craft and science.
“There are some incorrect answers; but there are many correct answers in data visualization,” Niklas told me when I asked him about his teaching philosophy. He views his role as helping students increase their data visualization vocabulary. While there is some science behind data visualization (primarily when it comes to color and pre-attentive attributes), data visualization has only seen the beginnings of formalism. It is important that students increase their data visualization vocabulary by seeing examples and understanding the existing research in this field. Ultimately, Niklas believes that there is a prominent craft aspect to data visualization, and each student’s personal background will have an impact on how they understand and visualize data.
Alberto sees himself as a mentor and his classes are very small, set up more like a studio than like a lecture hall. His approach to teaching is rooted in his belief that we learn by copying others and, more importantly, that we learn by doing. Not necessarily through books, lectures, and video tutorials (although those are important), but through practice. Given Alberto’s background in journalism, he makes a parallel between writing and data visualization — they are both crafts, but are grounded in rules and technique.
Isabel brings a unique perspective to teaching data visualization. She believes that her primary role is to give students the tools to think critically about data and avoid pitfalls. She says, “it is important for students to also understand what comes before and after creating visualizations.”
She takes students through the entire process, from collecting to cleaning and transforming data, and, finally to visualizing and understanding its implications. Isabel’s teaching approach is influenced by her own experience as a graphic designer — she discovered, time after time, that if one focuses too much on creating and designing the visualization, it becomes very easy to fall into the trap of misinterpreting the data.
Steven wants his students to have “the practical knowledge of what to do, plus the confidence that, when they see a dataset, they know how to get someone else to see the pattern.” His class incorporates concepts from data storytelling, as well as cognitive and perceptual studies. Given that Steven is a Professor of Psychology, that is not surprising. While two separate entities, Steven’s research and teaching do converge. Every time a student asks a question that does not yet have a scientific answer, Steven gets a new idea for a potential research project.
Jessica’s teaching approach is rooted in computer science, as well as in cognitive psychology. Her teaching revolves around both the effectiveness and expressiveness of data visualization, although she admits that her background in computer science makes her emphasize a great deal on the rules of data visualization. Ideally, students would be able to apply a set of rules to create highly effective and engaging data visualizations. But it is not that simple. That’s what Jessica finds most challenging when it comes to teaching data visualization: the tension between mastering the rules and students’ ability to be creative and know when to bend the rules.
Lace has an interdisciplinary education, which informs her approach to teaching. Her graduate course at the University of California Merced is called Global Good Studio. The goal of the course is different than any of the previous ones described here. Lace’s role is to help students solve problems of the world. While that might sound like a very ambitious goal, Lace’s students are asked to identify issues impacting the world and will work backward to develop skills and knowledge to move the needle on the problems they’ve identified. Data visualization is part of the curriculum, and students are expected to use both visual analytics and data science to solve problems. An example that Lace gave was about a group of students who wanted to understand how COVID-19 tweets were spreading and if misinformation was an issue. The group conducted a network analysis and then visualized the results.
Teaching Tools and Strategies
When it comes to teaching strategies, two common themes emerged. The first one: Learning data visualization is an iterative process. Most classes, whether they are being taught in journalism, psychology, computer science, design or business departments, include exercises and feedback. While the type of feedback that students receive varies based on each interviewee’s background, everyone agreed that feedback and iteration are fundamental in the process of learning data visualization.
A second theme that most folks mentioned was the importance to expose students to many tools. By doing so, students become more aware of the various options that exist and can decide which one is the best fit for their work.
Alberto said that 60% to 70% of the time students spend in class and outside consists of practicing and receiving feedback. Through practice, feedback, and iteration, students eventually develop their own style. Developing a style is at the core of Alberto’s teaching style and guides the tools and strategies that he uses. His classes at the University of Miami cover a broad range of data visualization tools, such as Datawrapper, Flourish, Adobe Illustrator, and Excel. In fact, Alberto’s class covers one tool per week. The goal is for students to learn the basics of each tool, and then decide which one they prefer. Adobe Illustrator stood out for me, as it is not a data visualization tool per se. When I asked why he teaches Adobe Illustrator, Alberto made a great point:
“Learning Adobe Illustrator in the design world is like learning Word in the writing world.”
Niklas’s students at the University of Maryland are majoring in information science. His course is very design oriented—“design, as in using rules and creating artifacts,” Niklas clarified. The course is so popular that, just last fall, 30 masters and 130 undergraduate students attended. Niklas incorporates basic principles on how to use data and databases, as well as tools, such as Excel, Tableau, Power BI, and Gephi. While the course includes both individual and group exercises, one that stood out as unique was the “white hat, black hat” exercise. For the white hat, students are asked to create a visualization that is truthful, transparent and fair. The black hat visualization is expected to be misleading. The goal: students should learn that data visualization is not intrinsically objective or fair.
Isabel teaches a data visualization course for graphic design undergraduates, as well as one for graduate design students. She wishes she had more time or a second course to incorporate D3 as well. Given that courses are relatively short, Isabel spends the first part of the semester lecturing and asking students to complete small individual exercises. The second half of the semester is focused on group projects. Isabel integrates tools such as Datawrapper, Tableau, and Flourish, while always stressing on the process that comes before creating visualizations (data collection, cleaning, and transformation), including ethical considerations. Like Alberto, Isabel also emphasizes on writing in addition to visualizing data, and the final project is a data story. It was fascinating to find out that Isabel started teaching data visualization over 16 years ago. One of the first exercises that she created was asking students to collect and visualize data about themselves, much in the spirit of current quantified self data visualization practices.
Steven’s classes range from 15 to 100 students. Like many other folks that I talked to, he teaches both an undergraduate and a graduate-level class, in both the Psychology and Business departments at Northwestern. Hands-on exercises and interaction are critical, so Steven doesn’t typically spend more than 15–20 minutes lecturing without taking a break and engaging students. One exercise that Steven mentioned as being very important was ideation. In this exercise, students are asked to create several sketches of different ways in which they could visualize the same dataset. The goal truly is ideation, so it is less relevant how effective or ineffective the chart choices are. What happens very often is what Steven calls “design fixation” — some students can’t figure out a different way to visualize the data, as they get stuck absorbing the example that they have in front of them.
Jessica teaches in two different departments at Northwestern University: computer science and journalism. The end goal for the computer science students is to make data visualization software. For journalism students, the end goal is to think critically about data visualization. Based on this difference in expected outcomes, the strategies and tools that Jessica uses to teach in computer science and in journalism vary. Both classes start with an exercise where students are required to create a visualization and then defend their design choices, followed by an exploratory data analysis assignment in Tableau. From there, journalism students are required to write a data-driven article, while computer science students learn to make interactive visualizations for the web. Overall, computer science students are required to code, whereas journalism students do more design exercises. Like Isabel, Jessica mentioned that it is problematic to start data visualization from the data only. Students need to understand some basic inferential stats before creating data visualizations.
Lace is very flexible when it comes to the tools and strategies that she uses, as her graduate course at the University of California Merced is interdisciplinary. Her classes are small, seven to 20 students, and students work in groups throughout the semester. She does not have a set syllabus. Students self-organize in groups depending on the problems that they are trying to solve, and Lace’s role is to guide them. If a group needs to learn R, for example, she would teach it. However, most of her students come in with prior programming knowledge and are encouraged to use the tools that they are familiar with. The final project consists of a communication plan. Whether that is in the form of a presentation or a blog post, students must communicate the analysis that they conducted and how that helps solve a problem that is impacting the world.
Readings
There are so many data visualization books and reading materials, that it was not surprising to see that there wasn’t a trend among the individuals that I talked to. Alberto, for example, uses his books as he wrote them with his students in mind. Tamara Munzner’s book, Visualization Analysis and Design, is the textbook of choice for both Niklas and Lace. Steven finds Cole Nussbaumer Knaflic’s book, Storytelling With Data, to be very accessible. He also sometimes incorporates Bruce Gabrielle’s or Stephen Few’s books into the curriculum. Isabel prefers to remain flexible and will change the readings depending on what articles or blog posts are most current and relevant, including Nightingale Medium essays.
Jessica requires students in both computer science and journalism to read Tufte’s The Visual Display of Quantitative Information. In addition, she asks them to watch short videos that summarize key research papers.
Data Visualization Post-Graduation
Although some of the people that I interviewed don’t typically have full visibility into what students’ career paths look like post-graduation, a few anecdotal stories were shared. Isabel mentioned that, following graduation, her students tend to gravitate towards major newspapers. A small number of them end up working in digital visualization.
Niklas’s students benefit from learning a wide range of data visualization tools, as they tend to get jobs in data analysis or data science. Sometimes, having Tableau listed as a skill on their résumé helps students land a job.
Lace teaches at the University of California Merced, which is situated close to the Bay area. So, it should not come as a surprise that most of her students end up working in data science and data analysis. Even students who study applied math or environment science often land a data science or data analysis job in the area.
Conclusions
After interviewing everyone, I was impressed to find out that data visualization is even more diverse and has many more applications than I had anticipated. I purposefully chose to interview individuals who have diverse backgrounds and teach in very different departments, so that I could paint a comprehensive picture of how and why data visualization is being taught in academia.
Following these six conversations, I started reflecting more on how complex data visualization is as a field of study, and on how many tools and strategies for teaching (and learning!) there are that I never considered before.
When I first proposed the idea to write this article to the team at DVS, my editor posed an excellent question that we often ask in data visualization: “Who is your audience?” I quickly replied that my audience comprised of university professors/instructors and public/corporate trainers. That was wrong! After conducting these interviews, I came to realize that this essay can be helpful not just to those who TEACH data visualization (be it in schools or universities, private or corporate workshops, online or in-person), but also to people who want to LEARN data visualization. While it is possible to pick up a few books and start practicing the craft, pausing and designing a learning strategy will channel your time and effort in the right direction.
HAPPY LEARNING!
Rebeca Pop is the founder of Vizlogue, LLC, a data visualization and storytelling lab that’s on a mission to help people communicate more effectively with data. Rebeca also teaches data visualization and storytelling at DePaul University and at The University of Chicago.
