DS808: Visualization
Study Board of Science
Teaching language: Danish or English depending on the teacher
EKA: N340081102
Assessment: 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: Archive
Entry requirements
Academic preconditions
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
Examination regulations
Exam element a)
Timing
Autumn
Tests
Portfolio exam
EKA
N340081102
Assessment
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
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