Official course description:
Full info last published 15/11-19

Data Design

Course info
Language:
English
ECTS points:
7.5
Course code:
KADADES1KU
Participants max:
140
Offered to guest students:
yes
Offered to exchange students:
Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
MSc in Digital Design and Interactive Technologies
Staff
Course manager
Associate Professor
Teacher
Research Assistant
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Assistant Lecturer
Teaching Assistant
Teaching Assistant (TA)
Course semester
Semester
Forår 2020
Start
27 January 2020
End
31 August 2020
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract
The course will enable the students to apply tools and methods for data visualizations and to critically reflect on data design as a socio-technical process.
Description
Data visualizations are used to get fast insight into a topic, to create powerful narratives about data, make connections visible, and to explore, discover and persuade. Analyzing, designing, and curating information into useful communication, insight, and understanding have become essential in our digital society. Data design has become a key component in how we understand our world. For digital design, data visualizations and data-driven design have become essential, but this has consequences. In this course, the students will learn how to conceptualize, visualize and present data but also to understand the consequences of data visualizations. The course encompasses data design as a circular process which moves between a) tools and methods to visualize data, b) the conceptualization of data and data visualization, c) application of data visualization and interpretation, and d) addressing its consequences. By understanding data design as a socio-technical process, the students will critically dissect data visualizations to explore their inherent social, ethical and cultural consequences.
Formal prerequisites
The course builds upon knowledge from the courses of the 1st semester of the KDDIT program and students should have completed those courses.
Intended learning outcomes

After the course, the student should be able to:

  • Identify the knowledge claims underlying different forms of data visualizations.
  • Apply basic design principles and processes to visualising data.
  • Conceptualize data and data visualization.
  • Discuss different debates about the implications of data visualization.
  • Reflect on a data design process and its ethical and societal implications.
Learning activities

  • During the lectures we will introduce tools and methods for data-visualisation; as well as concepts, theories and debates.
  • Individual and group exercises during the exercise sessions where the students work with the data visualisation tools and methods; and discuss and reflect upon the data design process with the concepts, theories and debates introduced in the lecture.
  • We will work with various small cases and current examples, publicly available data sources and manually collected data.
  • The students hand in two assignments as groups and get feedback from the TAs and lecturers.

Course literature

The course literature is published in the course page in LearnIT.

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 25%
  • Lectures: 20%
  • Exercises: 20%
  • Assignments: 15%
  • Exam with preparation: 20%
Ordinary exam
Exam type:
C: Submission of written work, external (7-trinsskala)
Exam variation:
C: Submission of written work
Exam submisson description:
The exam submission will comprise two parts:
1) A data visualisation applying the tools and methods introduced in class;
2) A reflection paper where the students use concepts and theories introduced in class to reflect upon their choices during the data design process, discuss its implications, and relate it to larger debates.


reexam
Exam type:

Exam variation: