Course Learning Outcomes

Course learning outcomes explain what we are trying to achieve in the course. For the student, it gives you a sense of what I (as instructor) am trying to do. As instructor, it guides the design of the class.

There were CLOs written when the course was first developed. However, over time, these were lost. I rewrote them in the summer of 2026, but they have not been approved by the university processes.

After completing the course, students will be able to:

1. Design and assess visualizations as effective solutions to data exploration and communication problems.
2. Reason about visualizations in abstract in terms including data, task, layout and encoding and assemble visualizations from these building blocks.
3. Articulate and apply foundational principles from design, perception, cognition, and computation to design and analysis, and trace those principles to their evidence.
4. Recognize standard hard problems including high-dimensional data, networks, sets, scale, and uncertainty and apply a toolkit of accepted solutions.
5. Apply design and research process to create and analyze visualization solutions and knowledge.
6. Create visualizations by selecting appropriate implementation strategies based on an awareness of a wide range of available approaches and tools.

Everything in the class should be tied to a learning outcome: all class activities should be designed to help the student achieve an outcome, and any assessment should measure the students’ success at achieving an outcome.

More detail with commentary

1. Design and assess visualizations as effective solutions to data exploration and communication problems
   • As a practitioner you should be able to create and assess visualizations.
   • The key is effectiveness - making bad visualizations is easy.
   • Effectiveness needs to be considered with respect to the viewer’s task
   • The core skill is to develop intuitions about effectiveness - not just trying to memorize a set of rules/best practices.
2. Reason about visualizations in the abstract in terms including data, task, layout, and encoding, and assemble visualizations from these building blocks.
   • Thinking about visualizations in terms of building blocks, not as just chart types, is core to the class.
3. Articulate and apply foundational principles from design, perception, cognition, and computation to the design and analysis of visualizations, and trace those principles to their evidence.
   • As a practitioner and scientist, you should have a solid grasp of the foundations of visualization so that you can use these in designing and analyzing visualizations and other things you might need to design/analyze.
   • The design principles allow us to build and interpret visualizations “ground up” from building blocks so you can understand (and generalize/apply) the reasoning rather than just trying to memorize a set of rules/best practices.
   • An awareness/appreciation/basic knowledge of the relevant perceptual, cognitive, and data science will be useful beyond just designing visualizations.
   • As a researcher (or graduate-student level advanced practitioner), you should be able to look for the evidence of principles.
4. Recognize common problems, such as high-dimensional data, volumetric images, networks and uncertainty, and apply a toolkit of accepted solutions.
   • I want you to know about some common categories of problems (e.g. hard data types like medical images, networks, and uncertainty) and the common solutions/methods used to address them.
5. Apply design and research processes to create and analyze visualization solutions and knowledge.
   • These skills are broad and useful beyond visualization.
   • I want you to be able to use design process (critique, redesign, generation, …)
   • I want you to get some practice with key research skills - some of which are rarely explicitly taught. I want you to develop an appreciation for what makes a good presentation, how to critically assess a research paper, how to apply ideas from an empirical study in practice, etc.
6. Select appropriate implementation strategies based on an awareness of a wide range of available approaches and tools.
   • This class is not about implementation.
   • I want you to be able to have enough awareness of the options so that you can make good choices when you have real problems.
   • We will need to use tools in order to practice some of the things above.

Module Learning Outcomes

Each module is designed to have more specific learning outcomes - each ties into a course learning outcome, but is more specific (so we can make progress towards the goals).

The Older Learning Outcomes

These learning outcomes were on the 2022 web site - but I think they are from the original course proposal (from 2016).

1. Students will understand the potential of effective data visualization.
2. Students will understand the key principles for the design of effective visualizations.
3. Students will be able to design and evaluate data visualizations for a variety of tasks.
4. Students will understand the relevant basics of visual perception and its role in design.
5. Students will understand some standard visualization methods and their applicability, and have exposure to standard kinds of data interpretation problems and their standard solutions.
6. Students will gain exposure and practice with some of the skills required to be a researcher and practitioner in the field of Visualization.

I don’t think these are that different from the new ones.