Course plan

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Syllabus:

Computer Engineering MA, Visualization, 6 credits


General data

Code: DT056A
Subject/Main field: Datateknik
Cycle: Second cycle
Credits: 6
Progressive specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements
Answerable department: Department of Information Systems and Technology
Answerable faculty: Faculty of Science, Technology and Media
Established: 10/1/2017
Date of change: 6/20/2018
Version valid from: 7/1/2018

Aim

The course aims towards a good understanding of visualization principles and algorithms. The course also gives insight into visualization techniques and tools as support to end users in their analytical reasoning. Increasingly complex data types including scalars, vectors, images, volumes and non-spatial datasets are explored with respect to their proper visualization techniques and algorithms. Issues such as data representation (data encoding), presentation (layout) and interaction (with user) are discussed. Visualization software systems and libraries are central to creating successful visualizations and are also introduced.

Course objectives

After finishing this course the student should be able to
- describe basic concepts in visualization
- describe desirable properties of a good visualization mapping
- apply required steps towards a good visualization given a defined problem
- use a visualization system or frame work for visualization of scalar data, vector data and volume data including time dependent data
- apply methods for visualization related to a specific problem using a visualization frame work or as plugins for a visualization system
- evaluate the performance of a visualization design using relevant quality metrics
- analyze a practical problem using a visualization system or frame work.

Content

- Overview of data and information visualization
- Visualization pipeline
- Data representation
- Scalar, vector, image, and volume algorithms
- Information visualization
- Interactive visualization
- Visualization systems, -frame work, and -APIs

Entry requirements

Bachelors Degree in Computer Science or Computer Engineering, including courses in programming in C++, 15 credits. Mathematics, Linear Algebra, 7.5 credits; Probability Theory, 7.5 credits.

Selection rules and procedures

The selectionprocess is in accordance with the Higher Education Ordinance and the local order of admission.

Teaching form

The course is taught using lectures, laboratory sessions, and finally a written exam. The large part of the course is with limited supervision, where the student is assumed to work on lecture material, and laboratory work.

Examination form

4.5 Credits, T101 Exam
Grades A, B, C, D, E, Fx and F. A-E are passed and Fx and F are failed.

1.5 Credits, L101 Laboratory work
Grades Pass or Fail

Grading criteria for the subject can be found at www.miun.se/gradingcriteria.

The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.

Grading system

The grades A, B, C, D, E, Fx and F are given on the course. On this scale the grades A through E represent pass levels, whereas Fx and F represent fail levels.

Course reading


Required literature

Author: Alexandru C Telea
Title: Data visualization: principles and practice
Edition: 2014
Publisher: CRC Press