Data Design (Spring 2020)

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

Time and date