| Learning objectives |
- Characterize the phenomena of visual analytics
- Summarize different fundamental concepts, techniques and
methods of visual analytics
- Analyze and apply different 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
- Critically assess the ethical and legal issues in applying
visual analytics
- Summarize the application areas, trends, and challenges in
visual analysis
- Exhibit deeper knowledge and understanding of the topics as
part of the project and the report should reflect on critical
awareness of the methodological choices with written skills to
accepted academic standards
|
| 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.
|
| Examination |
|
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-step 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 and structure |
|
Topic
|
Activity
|
Readings
|
|
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 |
- 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.
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