DM878: Visualization

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340072102
Assessment: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N340072101
ECTS value: 5

Date of Approval: 07-04-2021

Duration: 1 semester

Version: Approved - active


Co-read with DS808

Entry requirements

The course cannot be followed by students who have passed DS808.

Academic preconditions

Students taking the course are expected to have basic programming skills.

Course introduction

Visualizations are important means for experts in various domains (e.g., social sciences, bioinformatics, digital humanities, sports) to get an overview of data distributions and insight on prevalent data patterns in a comprehensible, intuitive, visual form. The aim of the course is to enable the student to develop appropriate visual interfaces for (domain-specific) user tasks. This is important as many students will be employed in sectors that may demand for visual data exploration solutions.

The course builds on competences in programming and data structures acquired in a bachelor education, and it gives an academic basis for preparing a master thesis with a focus on visual data analytics.

On completion of the course students should have gained knowledge on:
  • Methodologies for visualization design
  • Vision and human perception
  • Data and task abstraction for visual design
  • Visual encoding and interaction means 
On completion of the course students should have acquired the skills to:
  • Assess user requirements for visual design
  • Develop visual interfaces for a multivariate data set
  • Apply interaction means to support interactive visual data exploration
  • Validate the effectiveness of visualization solutions 
On completion of the course students should be competent to:
  • Abstract domain specific visualization tasks
  • Adapt existing solutions to support related visualization tasks
  • Develop new visualizations for unsupported user tasks
  • Argue data features on the basis of visual patterns

In relation to the competence profile of the degree, the visualization course explicitly focuses to:

  • Give the competence to plan and carry out scientific projects at a high professional level, including managing work and development situations that are complex, unpredictable, and require new solutions
  • Give skills to describe, analyze, and solve advanced application-driven computation problems using the learned models, and to adapt and develop new variants of the methods learned
  • Give knowledge and understanding of a variety of specialized models and methods developed in computer science, based on the highest international research, including topics from the subject's research front; a scientific basis to reflect on the subject area and to identify scientific issues

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:
  • Explain visual design approaches for arbitrary user tasks
  • Select appropriate visual features for mapping data features
  • Explain and apply appropriate state-of-the-art visualization methods
  • Evaluate the quality of and suggest improvements for visual mappings
  • Solve visual design tasks in teams


The following main topics are contained in the course:
  • Nested model for visualization design
  • Vision and human perception and their influence on visual design
  • What types of data can be visualized? (data abstraction)
  • Why do we need to visualize? (task abstraction)
  • How do we visualize? (visual encoding)
  • Means of interacting with visual representations
  • Information seeking and visual analytics
  • State-of-the-art visualizations for numerical, textual, geospatial, temporal and network data


See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)


Autumn and January


Portfolio exam with oral defense




Second examiner: External


7-point grading scale


Full name and SDU username


Normally, the same as teaching language

Examination aids

Not allowed, a closer description of the exam rules will be posted in itslearning.

ECTS value


Additional information

The portfolio exam consists of the following elements:
  • a group project with a written scientific short paper (2-4 pages) describing the group's visualization project
  • a (short) demonstration video of the visualization as supplemental material to the paper
  • a shared presentation of the visualization project with an oral group discussion
  • short individual sessions with questions on theoretical contents of the course
While the group project will define a target grade for the partaking students, the individual sessions will adjust those to individual grades by assessing a student's contribution to the project and the adopted theoretical contents taught in the course.

Indicative number of lessons

36 hours per semester

Teaching Method

The teaching method is based on three phase model.
  • Intro phase: 24 hours
  • Skills training phase: 12 hours (tutorials for project discussions)
Activities during the study phase:
  • Solving the project assigments
  • Self study of various parts of the course material.
  • Reflection upon the intro and training sections.

Teacher responsible

Name E-mail Department
Stefan Jänicke Data Science


Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration


Offered in


Recommended course of study

Profile Education Semester Offer period