Official course description, subject to change:
Basic info last published 15/03-21

Navigating Complexity: Mapping, Visualisation and Decision-making

Course info
Language:
English
ECTS points:
15
Course code:
KBNCMVD1KU
Participants max:
130
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price (single subject):
21250 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
MSc in Digital Innovation & Management
Staff
Course manager
Assistant Professor
Teacher
Associate Professor, Head of study programme
Teacher
Associate Professor
Teacher
Associate Professor
Teacher
PhD student
Course semester
Semester
Efterår 2021
Start
30 August 2021
End
31 December 2021
Exam
Abstract

The course will teach students to analyse complexity within an empirical case that explores a current topic or controversy within the field of science, technology, and innovation.

Description

The complex challenges of digitalisation often demand the professional use of both qualitative methods and data-exploration to diagnose issues and to act upon them.

In Navigating Complexity, students will be introduced to a range of conceptual and technical tools for generating and visualizing data and analysing complexity. Throughout the course students will experiment with different techniques for generating data and visualising complexity. Based on case work, students will be learn to reflect on how visualisations work as simplifications and can inform decision-making.

Students will learn a variety of qualitative approaches to quantitative data, focusing on inductive, exploratory inquiry. After the course, students will be capable of dealing with, communicating, and acting constructively in situations faced by complex challenges without straightforward solutions.

The course will cover topics such as complexity thinking, storytelling with data, data and digital methods, situational analysis, problematisation, data politics and technical tools for data visualisation and exploration.

Formal prerequisites
Please note that this course is only available for students enrolled in the programme Digital Innovation & Management. Moreover the student must always meet the admission requirements of the IT University.
Intended learning outcomes

After the course, the student should be able to:

  • Develop research questions that allow for exploratory and inductive inquiry into an empirical case through an iterative process of data collection, analysis, and storytelling.
  • Apply selected methods and conceptual tools to analyze complexity in an empirical case.
  • Interpret the data and the visualizations generated using the technical and conceptual tools provided in the course.
  • Reflect upon the decisions made in the research process relating the data and the visualizations to the development of the research question or focus.
  • Discuss the relationship between chosen methods, theories, data, and their implications for the findings in a concise manner.
Ordinary exam
Exam type:
D: Submission of written work with following oral, Internal (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.