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Blekinge Institute of Technology
Department of Computer Science
Revision: 2
Reg.no: BTH-4.1.14-0694-2026
Course syllabus
Introduction to Visual Data Analytics
Introduction to Visual Data Analytics
6 credits (6 högskolepoäng)
Course code: DV1689
Main field of study: Computer Science, Technology
Disciplinary domain: Technology
Education level: First-cycle
Specialization: G2F - First cycle, has at least 60 credits in first-cycle course/s as entry requirements
Language of instruction: English
Applies from: 2026-09-01
Approved: 2026-09-01
1. Decision
This course is established by Dean 2023-06-12. The course syllabus is approved by Head of Department of Computer Science 2026-09-01 and applies from 2026-09-01.
2. Prior knowledge
2.1 Entry requirements
Admission to the course requires completed courses of an amount of 60 credits in Computer Science.
3. Objective and content
3.1 Objective
The course aims to develop students’ understanding of the principles and techniques of data visualization. It focuses on foundational concepts of data visualization and on teaching to select effective visualization methods for analyzing, exploring, and communicating complex and large datasets. The course highlights the broad applicability of data visualization across domains such as computer science, game analytics, e-health, environmental science, and business analytics.
3.2 Content
The course integrates both theoretical and practical aspects of data visualization. It introduces fundamental concepts including the visualization pipeline, visual perception and cognition, visual encoding, user tasks, interaction, and techniques for visualization. Practical work involves the use of visualization programming libraries to create visualizations for different types of data within the context of information visualization.
4. Learning outcomes
The following learning outcomes are examined in the course:
4.1. Knowledge and understanding
On completion of the course, the student will be able to:
- describe fundamental concepts and principles of data visualization
- explain common visualization techniques based on given frameworks
4.2. Competence and skills
On completion of the course, the student will be able to:
- design and develop appropriate interactive visualizations using relevant visualization libraries
- choose and justify suitable visualization techniques based on data characteristics and user needs
4.3. Judgement and approach
On completion of the course, the student will be able to:
- discuss critically relevant ethical aspects of data visualization application
5. Learning activities
The course combines several teaching and learning activities, including lectures, laboratory sessions, project supervision, and student presentations. These activities are organized through a number of course components, which are completed individually and/or in groups.
Lectures introduce the theoretical foundations of data visualization. Relevant scientific literature is also integrated to support understanding and inform subsequent practical work.
Laboratory sessions focus on developing the Python programming skills required for the multiphase summative assessment assignment.
Throughout the course, students work on a project in which they apply both theoretical knowledge and programming skills to a practical use case. Supervision is provided to support this work.
All course materials are made available through BTH's learning platform. Teaching is given in English, but teaching support in Swedish may be provided.
6. Assessment and grading
Modes of examinations of the course
| Code | Module | Credit | Grade |
| 2705 | Multiphase Summative Assessment | 1.5 credits | AF |
| 2715 | Project | 4.5 credits | AF |
The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.
The examiner may carry out oral follow-up of written examinations.
The information before the start of the course 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
Main literature:
- Visualization Analysis and Design, Tamara Munzner, A K Peters/CRC Press, 2015 (ISBN-13: 9781466508910).
Other reference literature:
- Information Visualization: Perception for Design, 4th Edition, Colin Ware, 2020, (ISBN-13: 9780128128756).
- Visual Thinking for Information Design, 2nd Edition, Colin Ware, 2022, (ISBN-13: 9780128235676).
- Information Visualization: An Introduction, Robert Spence, 3rd edition, Springer, 2014, (ISBN-13: 9783319073408)
- Game Analytics: Maximizing the Value of Player Data, Magy Seif El-Nasr, Anders Drachen, Alessandro Canossa (Editors), Springer, 2013, (ISBN-13: 9781447147695).
- Selected scientific articles in visualization will be provided during the course.
The books and the scientific articles are available in digital form via the BTH library.