Data Visualization 7.5 credits

Visualisering av data
Second cycle, D7055E
Course syllabus valid: Autumn 2020 Sp 1 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

Syllabus established
by Jonny Johansson, HUL SRT 21 Feb 2020

Last revised
by Jonny Johansson, HUL SRT 21 Feb 2020

Education level
Second cycle
Grade scale
Systems Science
Subject group (SCB)
Informatics/Computer and Systems Sciences
Main field of study
Information Systems Sciences

Entry requirements

In order to meet the general entry requirements for the course, you must have accomplished a minimum of 180 ECTS of university studies, out of which 60 ECTS in the areas of computer or system science. The studies shall have included Introductory Programming (for example D0009E or D0007N) and Fundamentals of Databases (for example D0004N or D0018E). Good knowledge in English equivalent to English 6. More information about the English language requirements []

More information about English language requirements


The selection is based on 20-285 credits

Course Aim

The objective of the course is for the student to develop their knowledge and skills in Data Visualization. After passing the course, the student should be able to: 

  1. Explain and use the concepts in data visualization
  2. Describe the data visualization techniques
  3. Explain how data visualization techniques are used to represent data in organizations.
  4. Evaluate the use of certain data visualization
  5. Analyze and reflect on the results obtained via specific data visualization 

The Data Visualization course is aimed at providing knowledge to the students with regards to how to represent data, visually. The course will enable students to connect to  various datasets and sources while preparing for the visualization. Various topics and techniques will be explained in the course such as: use of colors, data-ink ratio, storytelling with graphs, and the taxonomy of data visualization methods. In a nutshell, the course aims to discuss the know-how to represent the data visually so as the visualization is appropriate, interactive, annotated, and using the right colors and graphical techniques. 


Lectures, labs, assignments, case studies and/or project work. During the course, the students work with individual tasks and/or group work. Some assignments or case studies in the course might contain work in contact with or about the industry. The student uses different methods and techniques, and it is important to choose the right method, technique or computer support for each task. Before and after the tasks are solved, there are lectures to present and discuss different solutions.
Teaching is in English and on the Internet for distance students or on campus for students living here. IT support: Learning management system, e-mail and phone.  The learning management system is used for delivering course material, information and submissions. Knowledge is shared and created within the course through virtual meetings with teachers and other students for discussions, supervision, teamwork and seminars. For students on campus, there will be meetings on campus.  


Through written tests, individual and group/project assignment, different student abilities are examined. Those are: the ability to explain and use data visualization techniques and the ability to solve business problems using data visualization individually and in groups. 


  Technical Requirements: access to computer with administrative rights, web camera, microphone and Internet connection. 

Jörgen Nilsson

Literature. Valid from Autumn 2020 Sp 1 (May change until 10 weeks before course start)
Title: Data Visualization: A Handbook for Data Driven Design
Author: Andy Kirk
Publisher: SAGE Publications, 1st edition, 2016

Title: Effective Data Visualization: The Right Chart for the Right Data
Author: Stephanie Evergreen
Publisher: SAGE Publications, 1st edition, 2016

Title: R Data Visualization Cookbook
Author: Atmajitsinh Gohil
Publisher: Packt Publishing - ebooks Accunt, 2015

Course offered by
Department of Computer Science, Electrical and Space Engineering

CodeDescriptionGrade scaleHPStatusFrom periodTitle
0001Written examU G VG4.00MandatoryA20
0002Individual taskU G#1.50MandatoryA20
0003Group-/Project workU G#2.00MandatoryA20

Study guidance
Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.