Data Visualisation Design
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.
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 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.
In this course, students learn how to conceptualize, visualize, and present data but also to understand the consequences of data and 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.
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 novel data visualization designs and build interactive visualization prototypes.
- Explain fundamental theories and design principles in data visualization, apply them in a design process, and reflect on these
- Interpret, deconstruct, and critique data visualizations.
- Reflect on the ethical and societal implications of data visualization.
- Collect tabular data sets, use code-based approaches to process them, and reflect on the role of these activities in data visualization design.
Ordinary examExam type:
C: Submission of written work, External (7-point scale)
C1G: Submission of written work for groups