Course syllabus
Note: the course pages are being updated so changes can occur until the start of the course.
Course-PM
CIU187 / DIT109 Information visualization VT22 (7,5hp)
Revised March 2nd, 2022
Department of Computer Science and Engineering
Course purpose
In this course, we will study the science, craft and art of visualizing information. A basic premise in this endeavour is that visualization is an extension of our minds into the world and a powerful cognitive tool to both perceive and communicate information. It extends our senses and provides new ways to express our ideas, facilitating both exploration of the world (and data representing the world) and sharing of information and understanding. As such, an understanding of human perception, as well as the technological means for creating visualizations, provides the basis for knowledgeable work with information visualization.
Course start
The course starts at 10:15 Tuesday, January 18th. The courses and exercises will be taken place on campus, the supervision sessions will be conducted via Zoom (the links will be provided in the schedule below).
Contact details
Teachers
Jasmina Maric (jasmina@chalmers.se)
Staffan Björk (staffan.bjork@gu.se)
Yuchong Zhang
Sjoerd Hendriks
Course communication
Course communication will be through this Canvas room, including announcements. Students that wish to contact teachers can do so through personal messages in Canvas or through email. Zoom will only be used for supervision.
Course literature
Colin Ware, Information Visualization - Perception for Design, 4th ed. (2021)
Course design
The theory introduced in lectures will be combined with group exercises to introduce tools and technologies. Practical experience can be gained through the group project. A two-part home exam frames the course, the first part testing understanding of theoretical concepts and analytical abilities and the second part testing abilities to design and reflect on information visualizations. The grade will depend on the project in combination with a home exam. See below for more details.
Lectures. Slides will be provided after lectures but they will be designed to be presented live and provide material for discussion and explanations at lectures. They will not be designed to be read independently afterwards.
Exercises are independent group work, completing an assignment.
Schedule
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18/1 |
10.15-12.00 Lecture: Course Intro and Information Visualisation Overview 13:15-16.00 Lecture and Exercise: Properties of Human Perception Online track: zoom link |
21/1 |
10:15-12.00 Lecture: Information Visualization Techniques & Design Process
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25/1 |
09.15-12.00 Lecture: Dark Information Visualization 13:15-16.00 Exercise: Dark Patterns with Jasmina Maric: High and Low |
28/1 |
10:15-12.00 Lecture: Interactivity in Information Visualization & Quantified self |
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1/2 |
10.15-12.00 Lecture: An Introduction of Visual Analytics with Yuchong Zhang Room: Jupiter121 |
4/2 |
9.15-12.00 Guest Lecture: Data Storytelling |
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8/2 |
10.15-12.00 Lecture: Information Visualization in Games
13:15- 16:00 Guest lecture: Maria Redström and Johanna Altenstedt |
11/2 |
09.15-12.00 Exercise: Information Visualization in Games |
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15/2 |
09.15-12.00 Literature seminar 13:15-16:00 Supervision |
18/2 |
9.15 - 12.00 Supervision Deadline Home Exam, pt. I |
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22/2 |
09.15-12.00 Guest Lecture: 13.15-17.00 Exercise: with Beata Wästberg and Liane Thuvander |
25/2 |
09.15-12:00 Supervision 09:15 -09:55 Staffan Björk will be with us |
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1/3 |
09.15-12.00 Peer Review online session 13.15 -16.00 Supervision 15:00 - 16:00 Staffan Björk will be with us |
4/3 |
09:15 - 9:45 Introduction Home Exam part 2 Room: High and Low 10:00 -12.00 Deadline Design project
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8/3 |
09.15-12.00 Supervision 13.15-16.00 Supervision |
11/3 |
09.15-12.00 Supervision |
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15/3 |
09.15-12.00 Supervision
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18/3 |
09.15-12.00 Supervision
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Note: TimeEdit does show the times and dates for activities but does not show location properly, and time and dates may change here; use TimeEdit as the last backup.
Changes made since the last occasion
The most prominent changes from the previous course are:
- Change of the textbook
- Introduction of peer-reviewing
Examination form
The course is examined by means of three modules, namely:
- Group Exercises 1.5 credits (fail/pass)
- Group Project 2.0 credits (fail, 3, 4, 5)
- Home Exam 4.0 credits (fail, 3, 4, 5)
- Part One - Theory and Analysis
- Part Two - Design and Reflection
Grade Scale
The grading scale comprises Fail (U), 3, 4 and 5. To receive a passing grade for the whole course, a student must have a passing mark on the exam, the assignments, the project, peer reviews and the literature seminar. The final grade is calculated as the average.
To pass the course you are obliged to do a minimum of 3 exercises out of 4.
Learning objectives and syllabus
Learning outcomes (after completion of the course the student should be able to)
Knowledge and understanding
* explain well-known information visualization techniques, including the pros and cons they have with respect to types of data and contexts
* describe how the cognitive and perceptive abilities of humans affect the possibilities of information visualization
Skills and abilities
* create concepts for information visualizations taking into consideration specific data sets, users, technologies, and use context
* develop visualizations from concepts to a functional prototype
Judgement and approach
* evaluate different interactive visualization techniques to judge their effectiveness and suitability for both generic and specific use
* analyze and provide creative criticism on specific solutions to visualize information
Student Representatives
André Wahlberg <guswahanx@student.gu.se>
Panpan Zhang <nierang@gmail.com>
Santosh "Sunny" Renukuntla <santoshr@student.chalmers.se>
Yiqian Wu <yiqianwuu@gmail.com>
Yueming Xuan <a874850627@qq.com>
Course Plans
Course summary:
| Date | Details | Due |
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