Print as pdf if you want a pdf! If you want a nicer printout, click off the browser's automatically added header and footer.

Blekinge Institute of Technology
Department of Computer Science

Revision: 2
Reg.no: BTH-4.1.14-0901-2024


Course syllabus

Introduction to Visual Data Analytics

Introduction to Visual Data Analytics

7.5 credits (7,5 högskolepoäng)

Course code: DV1699
Main field of study: Computer Science, Technology
Disciplinary domain: Technology
Education level: First-cycle
Specialization: G1F - First cycle, has less than 60 credits in first-cycle course/s as entry requirements

Language of instruction: English
Applies from: 2024-09-16
Approved: 2024-09-16

1. Descision

This course is established by Dean 2023-12-25. The course syllabus is approved by Head of Department of Computer Science 2024-09-16 and applies from 2024-09-16.

2. Entry requirements

Admission to the course requires completed courses of an amount of 60 credits of which 30 credits must be in Computer Science and 6 credits in Programming, as well as an attended course in Mathematical Statistics.

3. Objective and content

3.1 Objective

The course aims to develop the student’s knowledge of techniques and principles for data visualization. The course focuses on learning the basic concepts of data visualization and the most effective visualization techniques to apply to analyze, explore, or present complex and large amount of data. Data visualization can be applied to several areas, such as computer science, game analytics, e-Health, environmental science, and economics.

3.2 Content

The course includes both theoretical knowledge and practical aspects and concepts about data visualization. Fundamental concepts of visualization are introduced: the visualization pipeline, visual perception and cognition, visual encoding, user tasks, interactive visualization, techniques and algorithms for data visualization. Visualization programming libraries are used, practicing on different types of data, e.g. abstract data (information visualization).

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

  • be well-versed in key concepts and principles within data visualization
  • describe how different visualization techniques can be used to create an effective visualization, both orally and in writing

4.2. Competence and skills

On completion of the course, the student will be able to:

  • create appropriate static and interactive visualizations using visualization libraries
  • select and apply effective visualization techniques supporting the user task, e.g. the analysis of the data

4.3. Judgement and approach

On completion of the course, the student will be able to:

  • demonstrate the ability to discuss relevant ethical aspects of data visualization theory

5. Learning activities

The teaching and learning activities take place in lectures, laboratory sessions, supervision of projects, and presentations of students' work. The course consists of several course elements that are carried out individually and/or in groups.
During the lectures, the theoretical background will be presented to the students and relevant scientific literature will be selected for practical use in the work. Laboratory sessions will introduce the programming concepts needed for the multiphase summative assessment. Students will then work on a project to apply the theoretical and programming knowledge acquired during the course on a practical use case.
All course material will be shared through the BTH's learning platform. Teaching is given in English, but teaching support in Swedish may occur.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2505 Multiphase Summative Assessment 3 credits AF
2515 Project 4.5 credits AF

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

Written assessments can be followed up orally.

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

Main literature

  1. Visualization Analysis and Design, Tamara Munzner, A K Peters/CRC Press, 2015 (ISBN-13: 9781466508910).
  2. Selected scientific articles in visualization will be provided during the course.

Reference literature

  1. Information Visualization: Perception for Design, 3rd Edition, Colin Ware, 2012, (ISBN-13: 9780123814647).
  2. Information Visualization: An Introduction, Robert Spence, 3rd edition, Springer, 2014, (ISBN-13: 9783319073408)
  3. Game Analytics: Maximizing the Value of Player Data, Magy Seif El-Nasr, Anders Drachen, Alessandro Canossa (Editors), Springer, 2013, (ISBN-13: 9781447147695).
  4. Links to other reference literature available online will also be provided at the beginning of the course.

10. Additional information

This course replaces DV1659

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.