AbstractThe 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.
DescriptionData 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.
Intended learning outcomes
After the course, the student should be able to:
- Conceptualize data and data visualization.
- Discuss different debates about the implications of data visualization.
- Identify the knowledge claims underlying different forms of data visualizations.
- Reflect on the ethical and societal implications of data visualization in different contexts.
- Develop and reflect on a data design process.
Ordinary examExam type:
C: Submission of written work, external (7-trinsskala)
C: Submission of written work
The written hand-in exam 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.