Topics & Schedule

The schedule is a guide and subject to change. Optional courses and hand-on tutorials especially may be adapted to meet more closely the needs of participants.

The course is expected to require a total time investment of 10-15h / week, roughly split into

• 2h video material (optional)
• 2h reading (optional)
• 4h tutorial (optional)
• 2h Q&A sessions (optional)
• 2h individual project work

The course features

• Recorded video lectures, organised into small 5-10min videos and which can be watched at one’s own pace.
• Reading materials in the form of text book chapters and (easy to understand) scientific papers, associated to topics and lectures.
• Except for Week 1 and 2, participants choose from a set of optional sub-topics (see below)
• Individual sub-topics are tested in mini-quizzes
• Questions about topics can be discussed in general Q&A sessions, twice a week.
• 2h live tutorials are provided for
  • design and critiquing exercises, as well as
  • for a selection of free, open source and commercial data visualisation and visual analytics tools, to demonstrate the range of features available, skills required and user support, for deriving insight into data through visual exploration and analysis. While not compulsory, participants are encouraged to take full advantage of these sessions to engage in discussions with peers and invited subject matter and domain experts, and to obtain first-hand access to tutorials and direct feedback on assignments.
• Drop-in support to help with data visualisation brief, ideally using participants’ own data, create a design to meet the requirements identified, and, within the constraints of the course, build a visualisation-driven solution.
• Complementary teaching material in the form of interactive websites will be provided to participants throughout the course.
• Each week features a 1h life seminar by a guest speaker from academia and industry illustrating application of data visualisation in a variety of fields and the benefits this brings to creating value and gaining a competitive edge,
• A dedicated Slack/Teams channel will support networking and discussions.

Schedule

Each week is structured in to two sessions covering 2 different topics.

• Weeks 1 and 2 focus on mandatory topics to provide a general understanding of the main concepts in data visualisation design.
• Week 3 focuses on visualisation techniques for specific data types as relational data, temporal data, geographical, etc.
• Week 4 looks at the application areas for data visualisation in a variety of scenarios with an aim to help participants identify and select from different solutions for the challenges defined.
• Week 5 concludes the course with a set of advanced topics, from which participants will select options to focus on, ideally in line with their project.

Week 1: Foundations

Weeks 1 & 2 focus on mandatory topics to provide a general understanding of the main concepts in data visualisation design.

• success stories
• perception & cognition
• colour
• visual variables
• Gestalt laws
• visual literacy
• visual metaphors
• exploratory discovery and analysis
• pattern discovery
• explanatory visualisation

Week 2: Design & Tools

• visualisation design process
• misleading/deceptive visualisation
• scenarios
• data challenges
• cultural norms (social, geographical & organisational)
• visualisation taxonomies
• tool overview/introduction (with a focus on features & skill requirements)

Week 3: Techniques:

Week 3 focuses on visualisation techniques for specific data types.

• statistical visualisation / basic charts
• trees and hierarchies / focus+context
• network visualisation
• temporal data
• geographical data
• multivariate / high-dimensional data
• text data visualisation

Week 4: Application areas

Week 4 looks at the application of data visualisation in a variety of scenarios with an aim to help participants identify and select from different solutions for the challenges defined.

• digital humanities
• finance & accounting
• sports
• knowledge representation
• data journalism
• personal and social data
• bioinformatics/biological data visualisation

Week 5: Advanced Topics

Week 5 concludes the course with a set of advanced topics, from which participants will select options to focus on, ideally in line with their project.

• data-driven storytelling
• interaction techniques
• evaluation techniques
• HCI & visualisation
• data physicalisation
• visual analytics
• immersive environments, including VR/AR/XR