6 ECTS credits
150 h study time

Offer 2 with catalog number 4023869FNW for working students in the 2nd semester at a (F) Master - specialised level.

Semester
2nd semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Taught in
English
Faculty
Faculty of Sciences and Bioengineering Sciences
Department
Computer Science
Educational team
Maxim Van de Wynckel
Beat Signer (course titular)
Activities and contact hours
26 contact hours Lecture
26 contact hours Seminar, Exercises or Practicals
Course Content

The course covers the following topics:

  • Perception and colour theory
  • Data representation
  • Data presentation
  • Data processing and visualisation toolkits
  • Design guidelines and principles
  • Visualisation techniques
  • View manipulation and reduction
  • Validation and evaluation
  • Dashboards
  • Case studies
Course material
Digital course material (Required) : Slides of the lectures, Learning platform
Handbook (Recommended) : Visualization Analysis & Design, Tamara Munzner, Taylor & Francis Inc, 9781466508910, 2014
Additional info

The lectures are given in English. All relevant course material (slides) is available on the learning platform. For specific course topics, pointers to relevant additional resources (research papers, books and book chapters, website, specifications, online tutorials etc.) will be provided as well.

Learning Outcomes

General competences

  • The student has background knowledge about information visualisation (terminology, principles and issues, visualisation techniques, data presentation): explain, summarise, interlink, analyse, compare, justify, discuss, interpret
  • The student can design, develop and test interactive visualisations: apply, produce, solve, analyse, diagnose, design, create, model, build, organise, plan, decide
  • The student can criticise visualisations: take a step back, analyse, compare, confront, question, check, evaluate, judge, take a position, choose, defend

Grading

The final grade is composed based on the following categories:
Oral Exam determines 60% of the final mark.
PRAC Teamwork determines 40% of the final mark.

Within the Oral Exam category, the following assignments need to be completed:

  • Oral exam with a relative weight of 100 which comprises 60% of the final mark.

    Note: During the exam period an oral exam covering all the course topics will be conducted as well as a discussion of the assigned projects.

Within the PRAC Teamwork category, the following assignments need to be completed:

  • Project with a relative weight of 100 which comprises 40% of the final mark.

    Note: During the semester students have to work on a group project.

Additional info regarding evaluation

The final grade is a weighted average. During the semester an assignment has to be done (counts for 40% of the final grade). During the exam period an oral exam covering all the course topics will be conducted (counts for 60% of the final grade) as well as a discussion of the assigned project to assess a student's individual contribution. In order to pass the course, the final grade has to be at least 10/20. Furthermore, each individual grade (assignment or oral exam) has to be at least 8/20, otherwise the lower of these two grades becomes the final grade.

In case of an overall failure, partial marks for the assignment, if the student obtains at least 10/20 for the assignment, are transferred to the second session. Partial marks for the oral exam, if the student obtains at least 10/20 for the oral exam, are transferred to the second session. Students may not relinquish partial marks.

In the second exam period, assignments that were not satisfactory can be reworked and defended again. Also, the oral exam can be redone if the student failed in first session. The final mark is calculated in the same way as in the first exam period.

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Master of Applied Computer Science: Big Data Technology (only offered in Dutch)
Master of Applied Computer Science: Artificial Intelligence (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Artificial Intelligence (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Multimedia (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Data Management and Analytics (only offered in Dutch)
Master of Applied Sciences and Engineering: Computer Science: Artificial Intelligence
Master of Applied Sciences and Engineering: Computer Science: Multimedia
Master of Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering
Master of Applied Sciences and Engineering: Computer Science: Data Management and Analytics
Master of Teaching in Science and Technology: computerwetenschappen (120 ECTS, Etterbeek) (only offered in Dutch)