Information Visualisation

(706.057 Information Visualisation 3VU SS 2022)

Lecturer: Ao.Univ.-Prof. Dr. Keith Andrews
Course Web Site: https://courses.isds.tugraz.at/ivis/
My Web Site: https://isds.tugraz.at/keith
Email:

kandrews@iicm.edu

Office Hour: By appointment (during CoViD restrictions).
Room D.2.16, ID01054 (D.2.16), ISDS, Inffeldg. 16c, 1st floor.
Classes:

Face-to-face (in-person).

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

I intend to teach this course entirely face-to-face. You should:

  • bring a 2.5G-Nachweis and student ID to every class.
  • wear an FFP2 mask until seated in your place.

See the details of the university's regulations.

Should the COVID situation necessitate, classes will be moved online with Webex.

Schedule:

Starting Wed 02 Mar 2022 at 14:00
See the course schedule.

Description:
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
Registration:

In TUGRAZonline.
Starting Mon 21 Feb 2022 13:00.
Ending Fri 25 Feb 2022 23:59.
Registration has now closed.

This is an advanced course at postgrad (Master's) level. The number of students is limited to 12 to accomodate three groups of four. Depending on the exact number of participants, there may end up being some groups of three rather than four.

The seminar room can only be used at 50% capacity. Should it become necessary to move online, I will schedule dedicated weekly one-hour Webex slots with each of the three groups, for the survey and project parts of the course.

On signing up for the course, everyone is initially placed on the waiting list. One place is reserved for an incoming exchange student. Places will be allocated on Sat 26 Feb 2022, 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.

Prerequisites:

This course assumes that you are experienced at developing software (JavaScript or Java) and that you have experience with the basic web technologies (HTML5, CSS3, SVG, JavaScript).

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:

https://courses.isds.tugraz.at/ivis/ivis.pdf [143 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:

Exercises: https://courses.isds.tugraz.at/ivis/exercises/
Course Newsgroup:

tu-graz.lv.ivis

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 should refer to Chapter 2 of my INM 2014 course notes (or similar material elsewhere). There are rules and conventions you should respect. I recommend using Thunderbird as a news client.

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:

Write in your own words. 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.

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, a set of guidelines, and regulations regarding academic integrity. Breaches of academic integrity are very serious and will be punished appropriately where discovered.