by Mike Gleicher on July 30, 2017

Are you really looking for the Course Schedule?

There will be a syllabus (a description of the class rules) here soon. For now, you can look at last semester’s syllabus. Last semester, grading (student assessment and evaluation) was messed up. We’ll do better this semester. Although, there might not be a TA, which may make this challenging.

I always thought a “syllabus” was a description of the topics covered in class.

But, it is actually a more general discussion of official information about the class. (see for the university’s definition).

This brief syllabus has the basic info. Really the course web is the syllabus, and most topics here are discussed in more detail somewhere (links provided from this document – although this document is written first, so that the links may appear later). In general, you should look at the “Course Policies” pages, the individual postings about readings, and the “Class Schedule.”

Basic Info:

Course Name and Number: CS765 Data Visualization


Michael Gleicher, Professor
Office: 6385 Computer Sciences
Office Hours: By appointment. Wednesday 2:30-3:30 (please check my web page for travel schedule)
Preferred contact is by email: gleicher[at]


It is unclear whether there will be a TA for this class.

Class Meetings:

The class is scheduled for: Monday Wednesday and Friday, 11am-12:15pm, Room 3024 Engineering Hall.

Note: this class is overscheduled. We will generally meet twice a week, but please check the course schedule.

Note: class attendance is required.

See the course policies page for details.

Exam Period:

Will be assigned by the registrar. We probably will not have a final exam.

Main Topics:

(see an organizing post describing the topics in the class from last year)

  • Motivations for visualization and the types of visualization
  • Data and Task Abstractions
  • Evaluation, Validation, and Design Process
  • Human Perception, and its impact on Design
  • Encodings and Layout
  • Graphs, Networks, and Multi-Variate Data
  • Clutter and Dimensionality Reduction
  • Experiments and statistics (for visualization, and visualization for)
  • Animation and Motion
  • Scientific Visualization
  • Depicting 3D

Learning Outcomes:

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

Things that we will not do (Non- Learning Outcomes):

This class does not emphasize the use of specific tools, or implementation details. It is much more about “what pictures to make and why” than “how to make them.”

We will give students the opportunity to use tools they know, or to learn new tools, to make use of the concepts they are learning. We will try to provide some guidance for people to learn about tools, but we will not devote too much class time to it.

Text and Readings (summary, see the other postings)

All readings will be made available online. The textbooks for the class have been placed on electronic reserve via the library. See the textbooks posting.

There will be many other readings distributed via the web. There will be a combination of book chapters (provided under terms of academic fair use), research articles, and web pages.

Class Activities and How Students will be evaluated:

See the course policies page for details.

This class will consist of am lot of small things (assignments in-class and out of class), and a few (3-4) “medium sized” things (design exercises). There will be no “big project.”

The kinds of activities will be:

  • Participation / In-Class Experiences: We will not only grade participation in the usual sense (speaking and interacting in class). We will keep track of in-class experiences.
  • Reading: There will be a substantial amount of reading for this class. Most readings will be connected to an assignment that will give you a chance to demonstrate that you have done the readings and learned something from them.
  • Online Discussion: There will be assignments where your job is to participate in an online discussion. Generally, we will break the class into small groups and each group will have a separate discussion.
  • Seek and Find: You will be asked to find an example of a visualization (generally on the web) that satisfied some criteria, and to answer a question or two about it. Generally, the critiques will happen seperately.
  • Design / Re-Design Challenges: You will be asked to make a visualization that solve a problem: either from scratch, or based or to improve an existing visualization.


All aspects of the class (online participation, assignments, …) will count towards the grade.

In order to get a B or better, students must do (at least) an acceptable job in all parts of the class.

See the course policy page for more details.

Course Infrastructure (summary – see infrastructure posting)

We will use email to the students email address for individual communications.

We will use the university provided class mailing list (to students’ mailing lists) for announcements. However, the class web will be the primary mechanism for announcements.

The class web will be the primary source of information on the class, and students are required for its content. The website provides a facility to send email notifications of new information. You must sign up for this if you prefer this mechanism.

We will use Canvas as the course management system. Students will be required to submit assignments there, as well as to participate in online discussions.

We will provide readings online through Canvas to comply with academic fair use standards.

Course Policies:

See the details on the course policies page.

Software and Computing:

Students will be expected to be able to use basic computing software (a spreadsheet, a presentation preparation program, …). Using more advanced tools (for software development, data analysis, statstical analysis, etc.) is optional.

For students interested, Tableau has made their software available to the class through the Tableau for Teaching program. Tableau’s data visualization software is provided through the Tableau for Teaching program. Use of this software is optional.

You can use computers in the CS computing labs if you need to. Contact course staff to make arrangements. It’s probably best if you use your own computer (if you have one), or at least one you have more convenient access to.

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