DS808: Visualization

Study Board of Science

Teaching language: Danish or English depending on the teacher
EKA: N340081102
Censorship: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N340081101
ECTS value: 5

Date of Approval: 07-04-2021


Duration: 1 semester

Version: Approved - active

Entry requirements

None

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 the knowledge acquired in the course DS800 (Introduction to Data Processing), 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 competence to select, apply and combine programs, libraries and methods to work with large amounts of data in general and within a particular field of study
  • Give competence to use and further develop these tools and methods for designing and performing complex data analysis and working with advanced data
  • Give competence to manage work and development situations that are complex in the field of data processing and analysis

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 Blackboard for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Autumn

Tests

Portfolio exam

EKA

N340081102

Censorship

Second examiner: External

Grading

7-point grading scale

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

Not allowed. 

ECTS value

5

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.

The examination form for re-examination may be different from the exam form at the regular exam.

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 stjaenicke@imada.sdu.dk Institut for Matematik og Datalogi, Datalogi, Datavidenskab & Statistik

Timetable

44
Monday
01-11-2021
Tuesday
02-11-2021
Wednesday
03-11-2021
Thursday
04-11-2021
Friday
05-11-2021
08 - 09
Class f
Forelæsning
09 - 10
Class f
Forelæsning
10 - 11
11 - 12
12 - 13
13 - 14
14 - 15
Class f
Forelæsning
15 - 16
Class f
Forelæsning
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Administrative Unit

Institut for Matematik og Datalogi (datalogi, fiktiv)

Team at Registration & Legality

NAT

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