Information Visualisation

706.057 Information Visualisation 3VU SS 2018

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

Office Hour: Mon. 11:00-12:00 during normal term
Room D.2.16, ID01054, ISDS, Inffeldg. 16c, 1st floor.

Weds. 14:15-16:45.
Seminar Room IICM, IDEG134, D.1.10, Inffeldgasse 16c, ground floor

Sometimes, I have to reschedule a class, because there is a conflict of some kind or I am away. Always check the schedule on TUGrazOnline to be sure.


The approximate course schedule in more detail.

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
9. Visualising Query Spaces
10. Tools and Toolkits
11. Open Data Vis and Data Journalism

In TUGrazOnline.

This is an advanced course at postgrad (Master's) level. The number of students is limited to 20. Places usually fill up very fast. Two places are reserved for incoming exchange students.

Priority will be given to PhD and Master's students in one of the computer science degree programmes (sign up for the Main Group). Students in other degree programmes will be allocated places in the first class in chronological order of registration, as far as places are still available (sign up for the Reserve Group).

After the unregistration deadline, if you wish to unregister from the course, 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.


This course assumes that you are experienced at developing software (JavaScript or Java). Knowledge of the basic web technologies (HTML5, CSS3, JavaScript) and computer graphics is desirable.

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 students will form groups of 4 and 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:

For the lecture part of the class, I expect you to attend every week. If you do not, it is your responsibility to catch up.

For the seminar and project parts of the course, at least one member of every group must attend class every week.

For the final group presentations, it is OK if one member of a group is missing, as long as the remaining members attend and are willing to carry the missing person.

Assessment Method:

Your grade will be determined by four exercises:

  • Ex1 (individual): Intro Excercise

    This exercise is the first subtask and is equivalent to the beginning of the examination (Satzungsteil Studienrecht § 26(7)).

  • Ex2 (group): Survey. A state-of-the-art survey of a current topic in information visualisation.

  • Ex3 (group): Project. Typically, groups either build a visualisation or use visualisation to analyse a particular dataset.

  • Ex4 (individual): Oral Exam.

    This exercise is designated as the partial course requirement which may be repeated (Satzungsteil Studienrecht § 22(4)).

See exercises for more details.

Lecture Notes: [128 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:

If you would like to buy one or two books for the course, I recommend the following:

Munzner, Visualization Analysis and Design Spence, Information Visualization, 3rd Edition Ward et al, Interactive Data Visualization, 2nd Edition Ware, Information Visualization: Perception for Design, 3rd Edition Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics

Note: Amazon credit me a small referal amount, should you purchase a book after following these links.

Course Newsgroup:

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.

If you are not familiar with newsgroups, you may wish to refer to Chapter 2 of my INM 2014 course notes (or similar material elsewhere).

Language Policy:

This course is taught in English and 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.

For the oral exam, you can choose whether the exam is in English or German.

Breaches of Academic Integrity:

Do not plagiarise. Copying the work of others (from the web or elsewhere) or copying from another group and then submitting the work as (part of) your own work is known as plagiarism and is a serious breach of academic integrity. 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.

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.

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 a code of conduct and set of guidelines regarding scientific integrity and ethics. Breaches of academic integrity are very serious and will be punished appropriately where discovered.