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2019/2020  KAN-CDASO1040U  Visual Analytics

English Title
Visual Analytics

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Lester Allan Lasrado - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 11-09-2019

Relevant links

Learning objectives
  • Characterize the phenomena of visual analytics
  • Summarize fundamental concepts, techniques and methods of visual analytics
  • Analyze and apply visual analytics techniques for big/business datasets in organizational contexts
  • Understand the linkages between business intelligence and visual analytics and the potential benefits for organizations
  • Summarize the application areas, trends, and challenges in visual analysis
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved: 3
Compulsory home assignments
Each assignment is 1-3 pages in group of 1-4 students.
The students have to pass 3 out of 5 assignments.

There will not be any extra attempts provided to the students before the ordinary exam.
If a student cannot hand in due to documented illness, or if a student does not get the activity approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (max. 10 pages) which will cover 3 mandatory assignments.
Visual Analytics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Project
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) Internal examiner and external examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach




Course Introduction

Visual Analytics & Dashboards


Inspection of and Interaction with Visualizations

Lecture 0

Lecture 1


Exercise 1

Course Description & Course Structure Documents on its learning

(Few, 2007; Heer, Bostock, & Ogievetsky, 2010; Pantazos, Lauesen, & Vatrapu, 2013)


Visual Analytics: Roskilde Festival 2017

Foundations of Data Visualization

Elements: Environment, Optics, Resolution, & Display


Human Perception Experiments (Online)

Lecture 2

Lecture 3

Lecture 4


Exercises 2 -4

Lecture Slides & (Zimmerman, Madsen, Eliassen, & Vatrapu, 2016)

Chapter 1  of (Ware, 2013)

Chapter 2 of (Ware, 2013)


Visual Salience and Finding Information

Interacting with Visualizations

Visual Thinking Algorithms



Tools: Tableau & Power BI

Lecture 5

Lecture 6

Lecture 7



Exercises 5-7

Chapter 5  of (Ware, 2013)

Chapter 10 of (Ware, 2013)

Chapter 11 of (Ware, 2013)

Social Set Visualiser: Benjamin Flesch

Like What: Sets


D3.js Coding Assignment

Lecture 8



Exercise 8

(Flesch, Vatrapu, & Mukkamala, 2017)

What: Topical Data


With Whom: Trees


With Whom: Networks

Lecture 9


Lecture 10


Lecture 11


Exercises 9-11

 Lecture Notes


When: Temporal Data


Where: Spatial Data


Evaluation of Dashboards

Perceptual Evaluation

Sensory-Motor Evaluation

Physiological Evaluation

Lecture 12




Lecture 13




Exercises 12-15

Lecture Notes




(Le Pape & Vatrapu, 2009; Pantazos et al., 2013; Pantazos & Vatrapu, in press/2016; Vatrapu, Reimann, Bull, & Johnson, 2013)

Project Supervision: Prototype

(Remote: Dropbox)

Dashboard Prototype

Project Report Guidelines Document

Project Supervision: Report

(Remote: Dropbox)

Project Report

Project Report Guidelines Document

Description of the teaching methods
The course consists of lectures, exercises, and assignments. Each lecture is followed by an exercise session, and there will be a teaching assistant providing technical support for assignments and course projects.

The presented theories, concepts and methods should be applied in practice and exercise sessions. The students work in the entire semester on a mini project displaying the understanding of the concepts presented in the lectures and exercises. CBS Learn is used for sharing documents, slides, exercises etc. as well as for interactive lessons if applicable.
Feedback during the teaching period
Feedback on mandatory assignments will provided in general
Student workload
Lectures 24 hours
Exercises 24 hours
Prepare to class 48 hours
Project work & report 100 hours
Exam and prepare 10 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy the books.


  • Few, S. (2007). Data Visualization: Past, Present, and Future. IBM Cognos Innovation Center, http:/​/​perceptualedge.com/​articles/​Whitepapers/​Data_Visualization.pdf.
  • Flesch, B. (2014). Design, Development and Evaluation of a Big Data Analytics Dashboard. Master Thesis. Copenhagen Business School. Frederiksberg.
  • Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. ACM, 53(6), 59-67.
  • Le Pape, M., & Vatrapu, R. (2009). An experimental study of field dependency in altered Gz environments
  • Proceedings of the 27th international Conference on Human Factors in Computing Systems (Boston, MA, USA, April 04 - 09, 2009). CHI '09 (pp. 1255-1264). New York, NY: ACM.
  • Pantazos, K., Lauesen, S., & Vatrapu, R. (2013). End-User Development of Information Visualization. In Y. Dittrich, M. Burnett, A. Mørch, & D. Redmiles (Eds.), End-User Development (Vol. 7897, pp. 104- 119): Springer Berlin Heidelberg.
  • Pantazos, K., & Vatrapu, R. (2016). Enhancing the Professional Vision of Teachers: A Physiological Study of Teaching Analytics Dashboards of Students’ Repertory Grid Exercises in Business Education. . Proceedings of HICSS 2016, IEEE Press.

  • Vatrapu, R., Reimann, P., Bull, S., & Johnson, M. (2013). An eye-tracking study of notational, informational, and emotional aspects of learning analytics representations. Paper presented at the Proceedings of the Third International Conference on Learning Analytics and Knowledge, Leuven, Belgium.

  • Ware, C. (2013). Information visualization: perception for design (Third ed.): Elsevier.

Last updated on 11-09-2019