Official course description:
Full info last published 15/11-21

Data Design

Course info
Language:
English
ECTS points:
7.5
Course code:
KADADES1KU
Participants max:
150
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price (single subject):
10625 DKK
Programme
Level:
MSc. Master
Programme:
MSc in Digital Design and Interactive Technologies
Staff
Course manager
Assistant Professor
Teacher
Part-time Lecturer
Course semester
Semester
Forår 2022
Start
31 January 2022
End
27 May 2022
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 visualizations 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. Analysing, designing, and curating information into useful communication, insight, and understanding have become essential in our digital society. Data visualizations have thus 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, students will learn how to conceptualize, visualize, and present data but also to understand the consequences of data visualizations. The course encompasses data visualization as a circular process which moves between a) tools and methods to create visualization designs, b) the conceptualization of data and data visualization, c) application of data visualization and interpretation, and d) addressing its consequences. By understanding data visualization as a socio-technical process, the students will critically dissect visual representations of data 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 or obtained similar knowledge elsewhere.

Intended learning outcomes

After the course, the student should be able to:

  • Sketch and build simple novel or common advanced interactive visualization prototypes novel data visualization designs.
  • Explain and apply fundamental concepts, design principles, and design processes in data visualization.
  • Interpret, deconstruct, and critique data visualizations
  • Reflect on a data visualization design process, as well as the ethical and societal implications of data visualization
  • Describe specific domain areas in data visualization.
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 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 - preliminary activities for the final project - and get feedback from the TAs and lecturers.

Course literature

Kirk, Andy (2019). Data Visualisation: A Handbook for Data Driven Design. 2nd ed. London: Sage. ISBN 978-1-5264-6893-2 and 978-1-5264-6893-5 (paperback).

Other study material relevant for the course will be made available through 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-point scale)
Exam variation:
C1G: Submission of written work for groups
Exam submisson description:
The students will develop a data visualization project. Starting from an existing dataset the students will produce one or more data visualization artifacts and will write a report where they will discuss:
- the data, including any pre-processing,
- the chosen design goals,
- the produced visualization artifact(s),
- the design principles and inspiration considered,
- the design process, as well as the ethical and societal issues of their work.

The report length should be within 20-30 normal pages, excluding figures and appendices.
Group submission:
Group
  • The project will be carried out in groups of 4-5 students.


reexam
Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C11: Submission of written work

Time and date
Ordinary Exam - submission Wed, 25 May 2022, 08:00 - 14:00
Reexam - submission Wed, 27 Jul 2022, 08:00 - 14:00