Information Visualisation

(706.057 Information Visualisation 3VU SS 2024)

Lecturer: Ao.Univ.-Prof. Dr. Keith Andrews
Course Web Site:
My Web Site:

Office Hour: By appointment.
Room DH03052, Data House, Sandgasse 36, 3rd floor.

Face-to-face (in-person).

Seminar Room IDEG134 (D1.10), Inffeldg. 16c, ground floor.

I intend to teach this course entirely face-to-face.

Should the COVID situation necessitate, classes may have to be moved online with Webex.


Starting Wed 06 Mar 2024 at 13:15.
See the course schedule.

1. Introduction
2. Visual Perception
3. History of Information Visualisation
4. Visualising Linear Structures
5. Visualising Hierarchies
6. Visualising Networks and Graphs
7. Visualising Multidimensional Metadata
8. Visualising Text and Object Collections (Feature Spaces)
9. Other Kinds of Visualisation
10. Tools and Toolkits
11. Open Data Vis and Data Journalism

In TUGRAZonline.
Starting Mon 26 Feb 2024 15:00.
Ending Fri 01 Mar 2024 23:59.

This is an advanced course at postgrad (Master's) level. The number of students is limited to 16 to accomodate four groups of four. Depending on the exact number of participants, there may end up being some groups of three rather than four.

On signing up for the course, everyone is initially placed on the waiting list. Two places are initially reserved for incoming exchange students. Places will be allocated on Sat 02 Mar 2024, according to the standard algorithm specified in the curriculum.

If you register for the course, but later decide not to participate, please have the courtesy to unregister from the course, to free up your place for someone else.

If you wish to unregister after the unregistration deadline, please contact me by email. Depending on how far the course has already progressed, I will either unregister you without penalty or grade your work up to that point.


The course assumes knowledge of modern web technologies (HTML5, CSS3, SVG, JavaScript) and experience at developing web software (JavaScript or TypeScript).

Aims and Objectives of Course:

Participants will gain an understanding of the methods and principles of information visualisation. They will posess the basic skills needed to develop their own visualisations and to analyse their own datasets using visualisations.

Teaching Method:

A mixture of lecture, seminar, and practical work.

First, I will present current work and results in the field of information visualisation (lecture part). Then, in groups, students will research and present one particular aspect of information visualisation (seminar part). Finally, each group will do a project in information visualisation (project part).

Attendance Policy:

Attendance at the first five lectures is compulsory.

Unless you have a very good reason, backed up with documentary evidence such as a doctor's letter.

Attendance at the survey final presentations and the project final presentations is compulsory.

Unless you have a very good reason, backed up with documentary evidence such as a doctor's letter.

For the remaining seminar and project parts of the course, it is OK for one member of a group to be missing, as long as the other group members attend and are willing to cover for the missing member.

Assessment Method:

Your grade will be determined by a set of exercises.

Lecture Notes: [146 pages PDF]

The lecture notes are never in their final form, but will be updated periodically during the course.

If you teach and would like a zip file of the corresponding lecture slides (the same material but in HTML, SVG, PNG, and JPEG), please contact me by email.

Course Books:

I highly recommend the following books for the course:

Communication Channel:

I set up a room called “Information Visualisation (Ivis)” on as the official communication channel for the course:

This is where I will post news and announcements and where you should ask any questions you might have. It is also the right place to look to see if your questions have already been answered.

See the Guide to Using TU Graz Chat I wrote for my HCI course.

Language Policy:

This course is taught in English. There may be some participants who do not speak German, so please give your presentations and write your reports in English. This course is a good chance to practice using English with (almost) nothing to lose. I will not be grading your English, but the content of your work.

Breaches of Academic Integrity:

The work you submit must be your own. Write in your own words. Do not copy from elsewhere. Do not use generative AI tools to create words or work.

Copying the work of others (from the web, another group, or elsewhere) and then submitting the work as (part of) your own work is known as plagiarism and is a serious breach of academic integrity. Any text passages or code written by others, or images created by others, must clearly be identified as such. By taking this course, you agree to have your work submitted to plagiarism detection services. Your work may also be cross-checked against other work submitted in the same and previous years.

Assembling a collage of stolen text fragments, possibly with some slight editing or rearrangement, and handing them in as your own words is not acceptable, even if you reference the original source. If you are not well-practiced in the ways of academic citation (i.e. how not to plagiarise), I strongly recommend that you read Chapter 5 of my INM 2014 course notes and some of the resources on Debora Weber-Wulff's Plagiarism Portal web site.

AI-based tools may be used solely for spell-checking, translation, and stylistic improvement of your own texts. Any other use of AI-based tools will be regarded as use of unauthorised aids, unless cleared with me in advance. In particular, do not use AI-based tools to generate text. Although such generated text can appear well-written and authoritative, the content is often imprecise and inaccurate. And they are certainly not your own words. Do your own research and write in your own words.

The university has specific guidelines regarding the use of AI tools.

Do not fake. Faking data (for example, inventing the results of a survey or poll) is a serious breach of academic integrity.

The university has guidelines, and regulations regarding academic integrity. Breaches of academic integrity are very serious and will be punished appropriately where discovered.