|Kursusnavn (dansk):||Data Visualisation and Data-driven Decision Making |
|Kursusnavn (engelsk):||Data Visualisation and Data-driven Decision Making |
|Semester:||Forår 2019 |
|Udbydes under:||Bachelor i datavidenskab (b-ds) |
|Omfang i ECTS:||7,50 |
|Min. antal deltagere:||1 |
|Forventet antal deltagere:||0 |
|Maks. antal deltagere:||50 |
|Formelle forudsætninger:||Changes may occur
The course is only open to BSc DS third semester.
|Læringsmål:||1. Define data visualization (versus information graphics, or information visualization) and explain the differences in visualization purposes, from exhibitory to exploratory and explanatory.
2. Identify the most appropriate visualization strategy given the result of an data analysis process and an intended communication objective.
3. Follow a checklist of established design guidelines when building a visualization to account for human perception, communicating uncertainty, and reducing potential miscommunication.
4. 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.
5. 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.
6. 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
7. 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.
8. Likewise, participants will explore methods for relentlessly expanding the truthfulness of their models when abstracting data from the real world.
|Fagligt indhold:||The following subjects will be covered in the course.
1) Introduction to Data Visualization – Why We Visualize
2) History of Information Graphics
3) Roles within Big Data & Decision Support (Visual analytics / dashboarding)
4) Visual Vocabulary: A Tour through the Dataviz Zoo
5) Human Perception and the Visual System
6) The Details of Information Design
7) Design Science and Design Thinking
8) Geospatial Visualization
9) Network Visualization
10) Storytelling & Data-Driven Journalism
11) Uncertainty: How to Visualize it
12) Graphicacy & Xenographics
13) Ethics & Miscommunication – The Truth Continuum and How to Spot Lies
|Obligatoriske aktivititer:||Weekly individual exercise, completion of 50% of these is require to be entitled to take the final exam. |
|Eksamensform og -beskrivelse:||D. submission of written work with following oral (7-scale; internal exam)|
D22 (Submission of written work with following oral exam supplemented by the work submitted).
Duration of oral examination: 30 minutes
The students will develop an individual visualization project throughout the course. They will choose a dataset and an intended communication objective and apply the theory of data visualization to their objective. The final project can take any form the student finds appropriate for their communication objective including -- but not limiting to --: static visualization gallery (e.g. a paper), interactive visualization to explore data (e.g. a website), a poster. The work will be submitted before the examination, which will consist in an oral exam where the student will present their communication objective and motivate their choices. Questions will be asked to connect the choices to the theoretical concepts presented during the course.