DM878: Visualization

The Study Board for 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: 07-04-2025


Duration: 1 semester

Version: Approved - active

Comment

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

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

Content

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

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Autumn and January

Tests

Portfolio exam with oral defense

EKA

N340072102

Assessment

Second examiner: External

Grading

7-point grading scale

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

No aids allowed
The use of generative AI tools (e.g., ChatGPT, Microsoft Copilot, Gemini) for producing content for element 1 is forbidden!

ECTS value

5

Additional information

The portfolio exam consists of the following elements:
  1. a group project with a written scientific short paper (2-4 pages) and a short demonstration video describing the group's visualization project (to be submitted)
  2. a shared poster presentation of the visualization project with an oral group discussion (after element 1 has been reviewed)
  3. a short individual oral exam with questions on theoretical contents of the course (at the same day after the completion of element 2)
To achieve a passing grade overall, all elements must independently meet the objectives. The assessment of element 1 will take place in conjunction with the completion of elements 2 and 3.
Element 1 counts for 50%, element 2 counts for 30%, and element 3 counts for 20%, in which a overall evaluation is applied.

Indicative number of lessons

36 hours per semester

Teaching Method

Total number of planned lessons: 36

    Common lessons (lectures) in classroom/auditorium: 20
    Team lessons (exercise classes) in classroom: 16

The lecture takes the form of a flipped classroom. All related lectures are pre-recorded and available on itslearning at course start. The lectures include several hands-on activities to strengthen the understanding of the theoretical contents. The exercise classes include the discussion and creation of visualization programs in Python. In addition, students will get feedback on group projects in exercise classes and on request by the teacher of the course.

Other planned teaching activities:
    Self study of various parts of the course material
    Programming experiments with material from exercise classes

Teacher responsible

Name E-mail Department
Stefan Jänicke stjaenicke@imada.sdu.dk Data Science

Timetable

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Registration

NAT

Offered in

Odense

Recommended course of study

Profile Education Semester Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.