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/765-26/.

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 2025 (Course Web)

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.

Meeting Time and Location: Mondays and Wednesdays 11:00am-12:15, September 2, 2026 to December 9, 2026. Room 1524 Morgridge Hall.

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 7, Nov 1, Nov 25) 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 2026 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. 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.

See Course Learning Outcomes.

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/765-26/

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 and Why.

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 that 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/765-26/ Students should be aware of, and are responsible for, the content there. Important additions will be announced on Canvas.

The course Canvas Course Page 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

Attendance is required during lecture periods. Students who miss class should fill out the Missed Class Form on the web. Classes will often have In Class Activities (ICAs) that cannot be made up.

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 class will follow a regular rhythm (which is thrown off by the calendar at the beginning of the semester).

The parts of class are listed at Parts of Class and Rubrics, and summarized here. See Late Policy (and assignment timing) for an explanation of timing policies. See grading for an explanation of the grading system.

• In Class Activities (ICAs) - Activities that students complete during class and collected in the class period. If a student misses the experience, they cannot be made up later. (These used to be called “In Class Experiences”, but the acronym has too many other connotations).
• Reading Surveys - Canvas surveys that help students reflect on the base readings for a module (given in the first week of a module).
• Content Surveys - Canvas surveys that help students reflect on the concepts taught in a module.
• Anonymous Surveys - Canvas surveys that help the staff understand what is going on in the class. These will be anonymous - the staff will know whether or not students complete the survey, but will not be able to connect answers to students.
• Seek and Finds - Assignments (turned in as Canvas surveys) where students find visualizations in response to a prompt and answer questions about it.
• Design Warmups - Assignments (turned in as Canvas surveys) designed to give students practice with concepts.
• Design Exercises - Assignments (turned in as Canvas surveys) where students create visualizations with specific data sets. These assignments are designed to involve students giving and receiving feedback.
• Video (and other) Experiments - We will ask students to complete short video assignments where they submit a short video. We may have other types of experimental assignments.

Most class periods will have ICAs. Every week will have a survey (reading, content, or class). Weeks (except for the first) will have some assignment (Seek and Find, design warmup, design exercise). Video experiments will be scattered throughout the semester.

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.

Graded work is discussed in the “Homework and Other Assignments” category.

Guidelines for Exam Proctoring

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

Course Schedule/Calendar

A course calendar is available on this website and on Canvas.

Each week follows the same pattern: lectures (often with In Class Activities) on Monday and Wednesday, a survey and an assignment due on Friday. The first week of class is slightly different.

The class is divided into four 3-week modules. The first two weeks of class (Wednesday classes only) form a “preface module 0”, and the last week of class is a “wrap-up module.”

Generally, the first week of the module will involve basic readings and class activities (lectures, ICAs, seek and find) to introduce the module concepts; the second week will involve some reading and some initial work to apply the concepts (lectures, ICAs, design warmup), and the third week will focus on a design exercise to utilize the module concepts and class content to reinforce them.

The planned modules (subject to change):

Module 0: Welcome: What is Vis?

A short introduction (2 days over two week) to the topic and the class. 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.

Module 1: Building Blocks

We will look at how we can consider visualizations in terms of building blocks including 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 2: Methodology: Making and Using 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).

Module 3: Principles and Formalism

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.

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 4: Standard (Hard) Problems

We will look at some standard “hard problems” that come up including hard data properties (e.g., uncertainty), 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 5: Wrap Up

The last week of class.

Grading

Grading is at the discretion of the instructor.

Warning: we will not use simple averaging to compute grades (see Final grades). Canvas cannot compute your grade for you.

With a large number of small assignments, grading is more about consistency of completing assignments well rather than excellence on individual assignments. In particular, most of the smaller assignments really don’t provide the opportunity to excel.

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 reserve the right to adjust upwards.

Almost all parts of class are turned in as a Canvas survey. When you turn something in, Canvas should give you a 90 (borderline A). Course staff will not use this number. After a person reviews your assignment, they will change your score to something other than 90.

For assignments that are “participation grading” (e.g., Anonymous Surveys, ICAs), scores will either be 90 or 0.

Each assignment type will give specific rubrics for what scores mean.

Final grades

All aspects of class will contribute towards your grade.

There will be 5 graded design exercises. These will be counted uniformly. The scores will be averaged as your DE grade.

There are 5 Seek and Finds, 5 Reading Surveys, 5 Content Surveys, and 4 Design Warmups. For each of these except Design Warmups, we will drop the lowest score. We will use their averages. There may be additional penalties to your final grade if you miss more than 2 surveys (out of the 14 Reading/Content/Anonymous surveys).

We may deduct points from your final average if you do not satisfactorily complete enough ICAs, anonymous surveys, or Video Experiments. (enough will depend on how many there are - probably missing more than 1 AS or 3 ICAs). We may deduct points for students whose attendance is problematic. Deductions are generally considered in boundary cases - they will be less than a grade step, except under extreme circumstances.

We plan to give students a bonus for consistently completing assignments in a satisfactory manner.

We will average the averages: 40% Design Exercises, 15% of each of the other 4 categories.

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 (and assignment timing)

General principle: we encourage students to do work on time, and will enforce hard cutoffs. If a student does not complete something by the cutoff, we want them moving on to the next assignments rather than falling further behind.

Everything is due on Friday (for consistency) at 12:15pm in Madison’s time zone (the time that class ends on the days we have class). We do not have class on Fridays.

Assignments may be submitted late until the cutoff times, which vary by assignment type (see below). Assignments are only accepted after the cutoff with an explicit extension that must be requested.

There are no explicit penalties for being late (before the cutoff). For Design Exercises, there are benefits for being on time (see below). For all assignments, we will count how often students turn things in late. Chronic lateness will be considered in final grading as adjustments for students who are close to boundaries.

The cutoffs:

1. Hard cutoff 1 hour before the next class (usually Monday, 10am). We want you to do this so you are ready for class Monday morning (Wednesday in week 2). Applies to: Reading Survey, Content Surveys.
2. Hard cutoff Monday (12am Tuesday is not Monday). We want you to turn this in so you can move on to the next thing. Applies to: Seek and Find, Design Warmups, Video Experiments.
3. Hard cutoff one week later. For anonymous surveys, we want you to do this - but at some point, the information is stale. Applies to: Anonymous Surveys.
4. Hard cutoff Wednesday (12am Thursday is not Wednesday). We want students who turned things in on time to have time to revise their assignments based on feedback. Applies to: Design Exercises.

For Design Exercises: Turning the assignments in on time is important because course staff needs to get to work on them. Having the assignments at the 12:15 deadline gives course staff the opportunity to review the assignments, provide initial feedback, and arrange for peer critiques. Students will be asked to do peer critiques and will be rewarded for providing critique promptly enough that it will be useful for the recipient to learn from.

Note: while there is no penalty for a late design exercise, only on-time assignments will receive early feedback and the opportunity to provide peer feedback to others. These contribute to the grade for the design exercises.

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/or 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, an online resource, or an AI), be sure that you have permission to use things in the way that you are using them, 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 (Claude, 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 GAI Policies for more details.