This project (2020-1-SE01-KA203-077872) has been funded with support from the European Commission. This web site reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Information Systems, 15 credits

School of natural science, environmetal studies, and technology , Södertörn University
Web Site
Syllabus, Lecture, Course material, Exercise
Thematic Area
Factual description
The course runs in the third semester of a bachelor program in media technology. It is a 10-week course that covers different aspects of information systems, such as business analysis, service design, and visual data analysis.
The course is based on lectures, workshops, writing assignments, and lab exercises with software tools.
Complex systems are not explicitly mentioned, but is present in analyses and modelling of organizations and services, as well as in visualizations of organizational and other data.
Relevance in complex systems
Of most relevance for complex systems education is the part on visual data analysis. Here the students work with a visualization software; Qlik Sense to visualize and analyze big data sets.
Visualization is highly relevant for education in the realm of complex systems
Strong points
The course uses a well-established tool for visualization, Qlik Sense, that can make use of a huge number of public data sets.
The teachers in the course make some relevant and interesting points with respect to visualizations:
* A good visualization can be useful to help grasp a complex system. However, more important is the actual experimentation with the data using different visualizations. This is a good way to gain an understanding of complex relationships.
* Visualization literacy is important. For example to understand that visualizations are never objective/neutral depending on e.g. what data is selected/omitted, choices of scale etc.
Transferability potential
What is the potential to generalize this type of visualizations to courses in other disciplines?
Limited - different types of data needs different visualization tools. Also data looks very different in different disciplines.
The general thoughts and lectures about visualization literacy, and the approach to foster understanding by active experimentations with visualizations should be generalizable.