Official course description:
Basic info last published 7/11-18

Data Visualisation and Data-driven Decision Making

Course info
ECTS points:
Course code:
Offered to guest students:
Offered to exchange students:
Offered as a single subject:
BSc in Data Science
Course semester
Forår 2019
28 January 2019
31 May 2019

The course is focused on forming the basis of a data communicator. The students should recognize the various features of the human perception system, and operate around those limitations. We expect a student to be able to create data-driven stories, dashboards and storyboards. We will put in context data visualization touching on its history, from the first historic examples, and on the power of misleading via the incomplete presentation of statistics and graphs. The course will empower students with the underlying assumptions, guidelines and trappings of visualizing quantitative information.


Data visualization is a core activity in a data science project, and is needed from the very early exploratory phases to the eventual explanatory phases. The clear communication of analytic results is the primary end goal of a scientific endeavor. One cannot rely on the unprincipled application of the graphical capabilities (and defaults of standard software) such as bar/line/pie charts, because each visual choice has repercussions in perception, interpretation, and internalization of information. The accumulation of design decisions can result in a wide range of effectiveness.  Moreover, creating visualizations is not simply an activity to wrap up a project at the end: it is rather an objective that informs all phases of data analysis, including the early stages of interrogating datasets. The origin of a data science project is the question “What do we want to communicate and how?” The “how” heavily depends on data visualization.

The following subjects will be covered in the course.

  • Introduction to Data Visualization – Why We Visualize
  • History of Information Graphics
  • Roles within Big Data & Decision Support (Visual analytics / dashboarding)
  • Visual Vocabulary: A Tour through the Dataviz Zoo
  • Human Perception and the Visual System
  • The Details of Information Design
  • Design Science and Design Thinking
  • Geospatial Visualization
  • Network Visualization
  • Storytelling & Data-Driven Journalism
  • Uncertainty: How to Visualize it
  • Graphicacy & Xenographics
  • Ethics & Miscommunication – The Truth Continuum and How to Spot Lies

Formal prerequisites
Changes may occur The course is only open to BSc DS third semester.
Intended learning outcomes

After the course, the student should be able to:

  • Define data visualization (versus information graphics, or information visualization) and explain the differences in visualization purposes, from exhibitroy to exploratory and explanatory.
  • Identify the most appropriate visualization strategy given the result of an data analysis process and an intended communication objective.
  • Follow a checklist of established design guidelines when building a visualization to account for human perception, communicating uncertainty, and reducing potential miscommunication.
  • Conversely, students should be able to readily identify instances where general guidelines should be broken, and provide practical and theoretically informed arguments supporting their design decisions.
  • Produce alternative visualizations for the same message, compare their relative strengths and weaknesses, and make a motivated choice for the preferred one given the intended communication outcome.
  • Produce alternative visualizations for the same message, compare their relative strengths and weaknesses, and make a motivated choice for the preferred one given the intended communication outcome.
  • Generalize standard visualization techniques and customize them to better fit the visual literacy and/or intended communication outcome for a particular audience with domain-specific problems
  • Identify deceptive usage of visual communication, or unintentionally misleading graphical representations of data (evidence). By learning to spot the most common techniques for lying with data, students also become more in-tune with ethical considerations and how not to visualize data.
  • Likewise, participants will explore methods for relentlessly expanding the truthfulness of their models when abstracting data from the real world.