What do you include—or not—when you’re teaching an overview of the diverse world of data visualization in eight weeks?
Questions about getting started in the field or setting up new courses pop up frequently on the DVS Slack and in community conversations. In this post, we (DVS co-founders Amy Cesal and Amanda Makulec) will walk you through what we included in our overview class and why.
Last year, we had the opportunity to rethink and redesign the Foundations in Data Visualization class at the graduate level for the Data Analytics and Visualization program at the Maryland Institute College of Art (DAV). While we both work across the spectrum of data viz these days, we come from complementary backgrounds, with Amy having more of a design foundation, and Amanda more of a background in data and analytics. Bringing together our different perspectives to design this course helped us to cover wide range of interdisciplinary skills and ways of thinking that are important for a career in data visualization.
Students enter the MICA DAV program with a wide range of knowledge and skills in data visualization analytics, some of them looking to hone already well-developed skills, and some looking for a jump start as they switch careers.
The Foundations course we redesigned is the first class that the graduate students take in this professional degree program. We found this an especially exciting opportunity because the course frames everything the students are going to learn in the months to come.
Considering the diverse student experiences and how the course sets the tone for the program, we needed the course to be both accessible to those with relatively little exposure to the field, while still offering meaningful insights and new perspectives to those with substantially more experience.
It also needed to be fun and exciting. We wanted to get students hooked into the world of data visualization and expose them to new things that they can dive deeper into over the course of the program. Our goal was for the course to provide the framing for other courses that go deeper into topics like statistics, programming in R, design fundamentals, user experience, and storytelling for data visualization.
Course Overview
Goals of the course:
- Give students a taste of a variety of concepts they will dive deeper into in the program
- Expose them to inspiration and history including looking at ways of visualizing data before the use of computers
- Expose students to a variety of tools they can use again throughout the program
This course is a bit like eating at a dim sum restaurant. Students get a little bit of a lot of different things really quickly. Some may be new flavors they’ve never had before, while some may be more familiar—it’s a wide sampling of data viz tastes.
Some topics the class touches on:
- Data structures
- Giving feedback and critique
- Foundational principles for creating charts
- Chart selection and taxonomies
- Audience
- Data visualization style guides
- UX/HCD principles
- Accessibility and inclusive design
- Dashboard design
- Visualizing uncertainty
- Data viz ethics and misinformation
- History and innovations in data visualization
While we wish we could include everything in this course, it’s only eight weeks, so some things we’ve chosen not to include go deeper into specific realms of data visualization, like looking at visualizing health information, focusing on just one tool, or programming.
Course Materials
It was important to us to provide a variety of types of materials for students to learn from. Not everyone has the same learning style, so we wanted to provide students with a variety of visual and auditory materials as well as kinesthetic learning through assignments. We also wanted to present information to students at a variety of technical and difficulty levels. Not all academic papers are easily readable, so we mixed in some more challenging material along with more straightforward summarized information.
By mixing up the types of reference materials used, we were able to provide the most current information relevant to the industry, while also exposing students to where to look, beyond books, for information once they graduate. Here are some of the types of sources we selected for our students:
- Book chapters from well known data viz authors, like:
- Alberto Cairo, Jon Schwabish, Catherine D’Ignazio, Lauren F. Klein, Kieran Healy
- Videos of professional conference talks, including:
- Articles from Nightingale and other professional blogs, including:
- Podcast interviews, including:
- Data Viz Today (Alli Torban)
- Datastori.es (Moritz Stefaner and Enrico Bertini)
- Loud Numbers (Duncan Geere and Miriam Quick)
- Academic papers, for example:
- Tidy Data by Hadley Wickam
- What Makes a Visualization Memorable? – Michelle A Borkin et all
- Online documentation and demos for tools
Course Structure
The whole program is taught entirely online, which increases availability to students in a variety of locations, but it can also cause some challenges. We use a variety of lectures, polls and chat responses, small group breakouts, and tools like Mural and Jamboard to keep students engaged.
The Foundations course is made up of only weekly assignments; no tests, no multi-week projects, no finals. In the rest of the program, many assignments build on each other, but with weekly assignments it’s easy for our students to move on quickly if they didn’t totally get a particular assignment. That’s okay: students will be exposed to these same concepts again later in the program, and the next time they will at least be somewhat familiar. Learning is an iterative process.
The assignments are designed to expose students to a variety of tools including:
- Pen and paper
- Datawrapper
- Tableau Public
- Excel
- RAW Graphs
- Observable Plot
We wanted to design a course where students at any level could start making charts immediately. Our operating theory was that the more visualizing, and thinking about visualizing, the students got to do, the better. And we didn’t want a lack of training in a specific tool (which we know will come later in the program) to get in the way of that.
At the same time, we also wanted them to start to learn new tools that they could continue to develop mastery in through the rest of the program. Familiarity with a wide range of tools is also a resume builder. By having some assignments that use no technical tools (just pen and paper), some that use tools they may already have familiarity with (Excel), and some that may push their boundaries a bit more (Tableau, Observable), we hope we are able to meet these competing pedagogical demands.
At the end of the course, they will have experience in at least four tools. This is a great foundation for them to build on.
Two of our favorite assignments just force students to create:
- 30 chart sketches by hand of a small data set. This assignment forces students to think creatively and figure out how to get stuck and move beyond that point. This is a twist to the classic design school assignment of sketching 100 logos.
- Create the same chart in 3 tools. Students get to see the different ways tools handle data and what’s easy or hard to manipulate with each. It also forces them to experience and play with new tools; the assignment includes a reflection component, asking what was different about working in each of the tools.
We provide data for the assignments throughout the course, due to the pace of the work. Rather than asking students to focus on sourcing data each week, we wanted them to be able to focus on creating. They can use data that they choose for many other classes throughout the program, where they spend more time sourcing and munging data for visualization purposes, though we do teach and emphasize principles of tidy data to set a foundation for future success.
This approach exposes them to a variety of data types that they may encounter later in their coursework and careers:
- Survey data
- Personal data (self collected)
- Narrative data
- Global data (via World Bank and UN) from OurWorldInData.org
Continued Iteration in Course Design
As instructors, we review feedback and modify the course after each time it runs. For us, continuous iteration helps us stay on top of the evolving data viz landscape and student needs. We blend in new examples of great work and consider where adding exposure to new tools merits shifting some of the existing content. Continuing to iterate on the course each time we teach it means a better student experience and nudges us to stay up to date on the tools, methods, and innovations in this field.
For example, we changed the order in which some lessons were taught. After feedback and some lower assignment grades, we realized that some students didn’t understand data types coming into the program. Moving this lesson earlier in the course reduced frustration in both the students’ experience and our grading. We also dropped an assignment and created a new, better one for the first class. After reading evaluations and student feedback about their favorite assignments, this particular assignment didn’t stick out to anyone. So we dropped it and came up with something new that’s more interesting and memorable.
After three terms of running the course with around 70 participants total, the current version offers a lot of value to students, and sharing it with the larger data visualization community might both help other learners get a sense of where to start, help other instructors designing their own courses, and generate discussion that could help improve introductory curricula of this kind.
An Ask to the Community
Whether you’re a student, fellow instructor, or practitioner, we’d love constructive feedback from the community and to hear what kinds of assignments, reading, and activities have helped you. Find Amy Cesal and Amanda Makulec in the #topic-teaching DVS slack channel.
Amy Cesal is a data visualization designer and instructor. She is a co-founder and board member of the Data Visualization Society. Amy is a 3 time Information is Beautiful award winner and enjoys creating unusual data visualizations. She holds a Master’s Degree in Information Visualization from the Maryland Institute College of Art, where she is an adjunct professor. Amy has pioneered the use of data visualizations style guidelines, and writes and speaks on the topic.
Amanda Makulec is a health data visualization designer, teachers, and speaker based in Washington D.C. who volunteers as the Executive Director for the Data Visualization Society. She holds a Masters of Public Health from the Boston University School of Public Health, and worked in more than a dozen countries leading teams and developing user-centered data visualization products for federal, non-profit, and private sector clients.
