Official course description, subject to change:
Basic info last published 29/10-19

Data Visualisation and Data-driven Decision Making

Course info
Language:
English
ECTS points:
7.5
Course code:
BSDVDDM1KU
Participants min:
1
Participants max:
75
Offered to guest students:
yes
Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
Programme
Level:
Bachelor
Programme:
Bachelor of Science in Data Science
Staff
Course manager
Assistant Professor
Teacher
Part-time Lecturer
Course semester
Semester
Forår 2020
Start
27 January 2020
End
31 August 2020
Abbreviation
20201
Exam
Exam type
ordinær
Internal/External
intern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract
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 effective visual encodings (charts), data-driven stories, dashboards and storyboards. The course will empower students with the underlying assumptions, guidelines and trappings of visualizing quantitative information. In doing so, the curriculum will put the field of data visualization in context; touching on its history from the first historic examples, right up until the propensity today of misleading people.
Description

The clear communication of analytic results is the primary end goal of a scientific endeavor. 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, a core activity needed from the very early exploratory phases to the eventual explanatory phases. 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 to better understand them. 

One cannot rely on the unprincipled application of 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. In addition, the accumulation of smaller design decisions can result in a wide range of effectiveness. In light of these challenges, this course aims to substantially equip Data Science students with methods, tools and tactics for critical reasoning along their future paths of working with quantitative data.

The following subjects will be covered in the course:
  1. Introduction– Why We Visualize and a History of Information Graphics
  2. Visual Vocabulary: A Tour through the Dataviz Zoo
  3. Human Perception and the Visual System
  4. The Details of Information Design
  5. Human Computer Interaction (HCI) & Decision Support 
  6. Design Science and Design Thinking
  7. Geospatial Visualization
  8. Network Visualization
  9. Storytelling & Data-Driven Journalism
  10. Uncertainty: How to Visualize it
  11. Visual Literacy (Graphicacy) and Xenographics
  12. Ethics & Miscommunication – The Truth Continuum and How to Spot Lies
  13. Visual analytics / dashboarding

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.
  • 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.
  • 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.
  • Likewise, participants will explore methods for relentlessly expanding the truthfulness of their models when abstracting data from the real world.
Ordinary exam
Exam type:
D: Submission of written work with following oral, internal (7-trinsskala)
Exam variation:
D22: Submission of written work with following oral exam supplemented by the work submitted.
Exam description:
D