This is a Syllabus in the form of the official University Template. It details the course policies. A summary of course policies is available at Policy Overview

More information is on the course web page: https://pages.graphics.cs.wisc.edu/765-25/.

Key Course Offering Information

General Identifying Information

Institution Name: University of Wisconsin­­–Madison

Course Subject, Number and Title: Computer Sciences 765, Data Visualization

Credits: 3

Course Designations and Attributes: Grad 50% - Counts toward 50% graduate coursework requirement

Requisites: Graduate/professional standing

Repeatable for Credit: No

Last Taught: Fall 2024

Course Description: (official from Guide) Principles of the visual presentation of data. Survey of Information Visualization, Scientific Visualization, and Visual Analytics. Design and evaluation of visualizations and interactive exploration tools. Introduction to relevant foundations in visual design, human perception, and data analysis. Encodings, layout and interaction. Approaches to large data sets. Visualization of complex data types such as scalar fields, graphs, sets, texts, and multi-variate data. Use of 2D, 3D and motion in data presentations. Implementation issues. S

Meeting Time and Location: Mondays and Wednesdays 11:00am-12:15, September 3, 2025 to December 10, 2025. Room 2522 Morgridge Hall (the new CDIS building).

Instructional Modality: In-person, attendance required

Instructor Contact Info: Prof. Michael Gleicher (he/him/his), gleicher@cs.wisc.edu, 6588 Morgridge Hall. Open office (student meeting) hours 2pm-3pm Wednesdays (except Oct 29, Nov 5, Nov 26) or by appointment.

Teaching Assistant Contact Info (if applicable): Cat Nelson (they/them/theirs) cwnelson4@wisc.edu. Student meeting hours by appointment.

Course Learning Outcomes

(these are unofficial - I re-wrote them August 2025 and they have not been “approved” by the University)

After completing the course, students will be able to:

1. Design and assess visualizations as effective solutions to data exploration and communication problems
2. Articulate and apply foundational and principles from design, perception, cognition, and computation to the design and analysis of visualizations.
3. Recognize common problems, such as high-dimensional data, volumetric images, networks and uncertainty, and apply a toolkit of accepted solutions.
4. Apply design and research process to create and analyze visualization solutions and knowledge.
5. Select appropriate implementation strategies based on an awareness of a wide range of available approaches and tools.

See Course Learning Outcomes (CLOs).

How Credit Hours are Met by the Course

This class meets for two, 75-minute class periods each week over the fall/spring semester and carries the expectation that students will work on course learning activities (reading, writing, problem sets, studying, etc) for about 4 hours out of the classroom for every class period. The syllabus includes more information about meeting times and expectations for student work.

Instructor-to-Student Communication

This material is a summary of what is available on the course web: https://pages.graphics.cs.wisc.edu/765-25/

Course Overview

This course is designed to give a rigorous overview of data visualization.

The course takes a broad view of what data visualization is, and focuses on the idea of effectively addressing viewer (user) tasks in data communication and exploration problems.

The focus is on visualization design and analysis. Put simply, the class is concerned with “what pictures to make, not necessarily how to make them.” See What Is This Class and Why? (2025 Edition).

The course will provide a practical introduction so that students can design and assess visualizations for problems they encounter. However, our path to that is through the foundations: by understanding how visualizations are created from building blocks, and how these pieces can be assembled and analyzed. This creates more effective/adaptable strategies for design than simply memorizing a set of best practices or standard solutions. We will connect design to principles in perceptual and cognitive science.

The course will emphasize design process, as this is not commonly taught in the field. Skills such as user-centric thinking, iterative redesign, and critique are applicable beyond developing visualizations. Similarly, we will consider the skills of a visualization (or a more general) researcher, such as presentation, critical evaluation of papers, and the application of empirical results.

The course will provide students with exposure to common categories of “hard problems” and a toolbox of solutions. However, these “advanced topics” will not be covered in depth: our focus is the set of foundations, with the idea these can be applied to more specialized problems as well.

The course does not emphasize implementation issues. It is “programming optional” (doing some programming may be useful for completing assignments). Some assignments will be “paper and pencil”. Others will require students to use some tools to create visualizations from data sets. We will provide students with access to Tableau (a commercial visualization system), and provide some guidance for using it. We will also examine it as a manifestation of visualization concepts. Students are not required to use Tableau (or any particular tool) - but we do recommend trying it.

Course Website and Digital Instructional Tools

The primary source of course information is the course web: https://pages.graphics.cs.wisc.edu/765-25/ Students should be aware of, and are responsible for, the content there. Important additions will be announced on Canvas.

The course Canvas (link soon) will be the primary mechanism for (1) announcements, (2) handing in assignments, (3) quizzes and surveys, and (4) group discussions.

Students can communicate with the course staff by email.

Students will be expected to complete assignments using tools of their own choosing. We will provide access to Tableau, and some guidance for using it and other options.

Lecture, Discussion and/or Laboratory Sessions

The class has no scheduled meetings outside of the lecture times.

Required Textbook, Software, and Other Course Materials

All required readings will be provided online without fees. Access to some of the required readings may be through the University of Wisconsin Libraries’ digital collections.

Links to required readings will be provided on the course web. For protected files (provided under academic fair use), the files will be on canvas (with links from the course web).

We will provide students with access to Tableau, both desktop and online. Access to Tableau is made possible through the Tableau for Teaching program. Students can also obtain student licenses.

Students will probably want some “data programming” environment (e.g. Python or R, with appropriate tools).

Homework and Other Assignments

The course will have regular assignments announced via the course web.

The course will have “In Class Experiences” (ICEs) during lectures. These will be collected. Students will be responsible for completing them (during class). If a student misses the experience, they cannot be made up later. Students may be penalized for missing too many ICEs.

The course will have regular (roughly biweekly) online assignments. These will be described on the course web page and turned in via Canvas (or other online survey mechanism).

The types of assignments will include:

• Seek and Find assignments - where students find examples of visualizations and answer questions about them.
• Content Surveys - where students answer questions designed to provoke their thinking about the readings and lectures.
• Design Exercises - where students will be asked to create or analyze solutions to data problems.
• Class Surveys - anonymous surveys for the staff to assess how things are working. Participation in the anonymous surveys will be tracked.
• Online Discussions - (generally optional) students will be given the opportunity to discuss class topics online, for example to provide each other feedback on other design assignments.

Exams, Quizzes, Papers, and Other Major Graded Work

There will be no final exam. Students may be allowed to turn in final assignments during the summary period.

Quizzes (surveys) will be used as a hand-in mechanism for the assignment types listed above.

The “Design Exercises” will be the major graded component of the class.

Guidelines for Exam Proctoring

Not Applicable: There will be no exams in this class.

Course Schedule/Calendar

This is still under development as the schedule is being redesigned for 2025.

The class is divided into 2 week “modules” (the last module will be the last 3 weeks of class because of Thanksgiving break and the summary period).

Each module will contain Lectures, required Readings, a Seek and Find, (one or more) Content Surveys, (zero or one) Course Surveys, multiple In Class Exercises, and multiple Design Exercises. Students have flexibility to complete the module’s work within each module. We will provide a recommended schedule, which may include incentives for submitting work before the end of the module.

The class consists of these (generally smaller) regular assignments. The design exercises may be larger in some weeks (and the other aspects will be adjusted accordingly).

The planned modules (subject to change):

Module 1: Visualization and Effectiveness

We introduce a broad definition of visualizations as solutions to user data tasks and consider how they can be effective at this. We’ll use the basic notion of “what does a visualization make easy to see” to develop intuitions about what makes visualizations effective (good). We’ll work out course mechanics.

Module 2: Visualization Building Blocks

We will look at how we can consider visualizations in terms of the building blocks of data and task abstractions and visual encodings. We will see how this allows designing and analyzing visualizations. We will practice design process (including critique). Students will begin to design and analyze visualizations (pen-and-paper).

Module 3: Creating and Exploring with Visualizations

We will look at strategies for implementing (creating) visualizations and how visualization can be used for exploring data (in addition to communicating stories with it). We will focus on implementation and scale. Students will practice creating and critiquing visualizations.

Module 4: Evaluation and Formalization

We will be more explicit in how we evaluate visualizations and visualization research. We will provide more formal approaches to considering aspects of design and analysis. Students will create and critique visualizations.

Module 5: Deeper Foundations and Principles

We will explore the design, perceptual, cognitive, and data foundations in more depth. We will examine how the science can influence design and practice. Students will apply these ideas to analyze data and create visualizations.

Module 6: Standard Problems

We will look at some standard “hard problems” that come up including hard data properties (e.g., uncertainty, scale), data types (e.g., networks, high-dimensional records), and applications. We will look at some of the standard approaches to address these problems. The design exercises will give more practice at creating and critiquing visualizations.

Module 7: Advanced Topics and Skills

We will look at emerging themes in visualization to see where visualization research is providing new solutions. We will look at design and research process in visualization.

Grading

Grading is at the discretion of the instructor.

• We will consider engagement. Students will be penalized for not completing “enough” of the in-class exercises, class surveys, or discussions. These assignments will be recorded as check (acceptable), no check (completed but not acceptable), or not turned in. This will be applied as adjustment after the final average is computed. Note: we will “drop the lowest” in each category (so students can miss 1 in each category without penalty).
• Assignments (seek and finds, content surveys, design exercises) will be graded roughly on a 100 point scale (below). Not all assignments allow for students to score more than 90 points - those assignments do not have ways to demonstrate excellence.

Grading Scale

(grade cutoffs) 90=A, 85=AB, 80=B, 75=BC, 70=C, 60=D, below that F. Note that the University does not give “A+” grades, but we might (for exceptional work). On this scale, 100 is not “full points” but rather “A++” (a usually unattainable score). The scores are cutoffs (e.g, 84.99 is a B, not an AB).

We will use the score 87 to denote “turned in, but not yet manually graded” (Canvas may give it automatically on successful hand in). 87 basically represents “assumed to meet high expectations, but we didn’t check.”

Final grades

We will consider all graded assignments, with rough weighting to emphasize larger assignments. We don’t know the number or size of the assignments, but roughly, 25% of the grade will be the seek and finds and content surveys, 40% small design exercises (roughly 10), and 35% large design exercises (roughly 3). The final weighted average will be adjusted by an engagement score.

We will drop the lowest score in each “small assignment” category (seek and find, content survey, small design exercises). We will ALSO drop the second lowest score if it is not zero. (that is, you can miss doing 1). However, not completing these assignments may still be penalized as part of engagement.

Do not expect Canvas to correctly estimate final grades.

Final grades are not curved, but rather, calibrated such that a student meeting the (generally high) expectations of a University of Wisconsin Graduate student will received an AB. Students exceeding these expectations receive an A. If everyone exceeds my expectations, I have a smart class. If too many students don’t meet my expectations, I will consider adjusting my expectations.

Regrading

If you believe we have made a mistake in assessing your work (either an administrative error, or if you disagree with our assessment), you must complete the Regrade Request Form within one week of receiving the (incorrect) grade.

We may not respond immediately: we will process regrade requests in bulk.

Late Policy

With the exception of In Class Experiences (ICEs), everything will have a due date and be due on that date, Madison time. 12:01am Saturday is not Friday.

Assignments are due the last day of the module (a Friday). Some assignments will have suggested “early recommended hand ins” for which there will be incentives (e.g., we might be able to provide early feedback, or allow you to participate in peer review).

• In Class Experiences cannot be made up. If you miss the experience, you miss the experience. Students who miss class should use the Missed Class Form. If you come late to class and miss the ICE, do not turn in the form. It is academic misconduct to turn in a form for an ICE you did not experience.
• Surveys and Seek and Finds are due at the end of the module (Friday) with hard cutoff dates the following Monday, enforced by Canvas. Responses will not be accepted after that. There are no penalties for turning things in before the hard cutoff.
• Design Exercises are due at the end of the module (Friday). There are no penalties for turning things in over the weekend (Saturday or Sunday). Late assignments will be accepted until the following Friday, but will be penalized. Late assignments cannot earn As, and are subject to a 1 point per day penalty (a grade will be given and the penalty will be recorded separately).

The incentives for optional early handins (when we have them) are hard cutoffs.

Academic Policies and Statements

Additions to the standard syllabus statements:

Unintentional misconduct: It is the responsibility of the student to understand all the details of the syllabus and UW-Madison policies. Lack of understanding regarding how to properly cite, the presence of specific course policies, and University expectations, does not excuse behavior.

Collaboration: Learning is a team sport. We hope to foster a collaborative environment where students learn together. Students are encouraged to discuss aspects of class with their peers. However, most assignments are to be done individually. Students are expected to substantially complete assignments on their own (or in assigned groups, when permitted). Do not claim credit for work done by others. If you use something from someone else (whether it is a classmate, or an online resource, or an AI) be sure that you have permission to use things in the way that you are using it, and that you give proper attribution. When in doubt, ask the course staff.

Students should give proper attribution for work they did not do.

AI Usage Policy: Use AI tools (ChatGPT, Co-Pilot, etc.) to help you in your learning, not to do the assignments for you. Treat them like another student: you can get them to help you, but you are responsible for your own work. Give them proper credit / attribution. Don’t claim their work as your own.. See Course Policy on the use of Generative AI Tools for more details.