DV1659 - Visualisation - Blekinge Institute of Technology

1. Descision

This course is established by Dean 2021-04-16. The course syllabus is approved by Head of Department of Computer Science 2023-08-10 and applies from 2025-01-20.

2. Entry requirements

Admission to the course requires taken courses of an amount of 60 credits in Computer Science as well as taken course in mathematical statistics.

3. Objective and content

3.1 Objective

The course introduces techniques for data visualisation. It is difficult to have an overview and analyse large amount of data. Using data visualisation techniques helps us reading and analysing complex information. Examples of areas where visualisation techniques are used are in gaming, computer science, environment and health.

3.2 Content

The course includes theoretical and practical knowledge and concepts about data visualisation. Fundamentals concepts of visualisation are introduced: visual perception, cognition, techniques and algorithms for data visualisation. Visualisation tools are used practicing on e.g. information visualisation.

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

describe how different visualisation techniques can be used to create an effective visualisation.

4.2. Competence and skills

select and visualise data using visualisation tools.
select appropriate visualisation techniques supporting the analysis of the data.
apply visualisation techniques in a larger project.
describe a visualisation process both orally and in writing.
independently and critically interpret and evaluate information and be able to critically discuss relevant phenomena, issues, and situations.
use terms and concepts in visualisation.

4.3. Judgement and approach

assess relevant scientific aspects from a computer science perspective.
independently review and critically evaluate their own work.

5. Learning activities

The teaching and learning activities take place in form of lectures, laboratory sessions, supervision of projects, presentations of students' work, reading scientific literature and using it practically in the work. Teamwork tasks are included in form of group projects.

6. Assessment and grading

Modes of examinations of the course

Code	Module	Credit	Grade
2205	Written assignment 1	1.5 credits	AF
2215	Written assignment 2	2.0 credits	AF
2225	Written assignment 3	1.0 credits	GU
2235	Project assignment	3.0 credits	AF

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Fail, supplementation required, F Fail.

The final grade is an unweighted and rounded average of the grades on the graded parts. If the resulting grade is exactly between two grade steps, it will be rounded down.

The information before a course occasion states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

The course literature is not compulsory, but these books are recommended:
Main literature
1. Selected scientific articles in visualization provided during the course.
Reference literature
2. Information Visualization: Perception for Design, 3rd Edition, Colin Ware, 2012, (ISBN-13: 9780123814647).
3. Information Visualization: An Introduction, Robert Spence, 3rd edition, Springer, 2014, (ISBN-13: 9783319073408)
4. The Visualisation Handbook , Charles D. Hansen, Chris R. Johnson Jr., and Chris R. Johnson, Elsevier Science & Technology, 2004, (ISBN-13: 9780123875822).
5. Visualization Analysis and Design, Tamara Munzner, A K Peters/CRC Press, 2015 (ISBN-13: 9781466508910).
6. Game Analytics: Maximizing the Value of Player Data, Magy Seif El-Nasr, Anders Drachen, Alessandro Canossa (Editors), Springer, 2013, (ISBN-13: 9781447147695).
7. Links to reference literature available online will also be provided at the beginning of the course.

10. Additional information

This course replaces DV1474

This is not a legal document. If you would like a copy of the legal decision regarding this course plan, contact the registrar at Blekinge Institute of Technology.

Course syllabus