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 5-10h / week, roughly split into
- 1h video material (at own pace)
- 1h reading (optional)
- 2h tutorial (optional)
- 1h Q&A sessions (optional)
- 1-2h individual project work
- Recorded video lectures, mostly organised into bite-sized chunks of 5-10min, with a few longer videos 20-25min, 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 with 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 will be discussed in general Q&A sessions, once 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 direct feedback on assignments.
- Drop-in support to help with data visualisation briefs, 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.
- At least once a fortnight we will host a 1h live seminar by a guest speaker from academia or 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.
Each week will focus on one key topic:
- Weeks 1 to 3 focus on mandatory topics to provide a general understanding of the main concepts in data visualisation design.
- Weeks 4 & 5 focus on visualisation techniques for specific data types as relational data, temporal data, geographical, etc.
- Weeks 6 & 7 look 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 8 will take a breather from lecture material and focus on project 1-on-1s, where we will go through projects with participants. These sessions will allow participants to discuss their ideas and work to this point with one of the organisers, and to receive interim feedback.
- Weeks 9 & 10 conclude the course with a set of advanced topics, from which participants will select options to focus on, ideally in line with their project.
- Week 11 is not a taught week. It's to finish your projects which you have to hand in at the end of that week.
Introduction into basics of data visualization. Why are we using data visualization? Why does visualization work? What are forms of visualization? What is the difference between exploration and explanation. What is of visualization literacy? The week's second session introduces the visualizaton design process, basics of design thinking, and understand the idea behind Exploratory Data Analysis (EDA).
The week will see a sketching exercise, and tutorial on basic charts and common flaws in visualizations. You will formulate a visualization challenge for your class project.
Basic concepts of visualization design: data types, visual variables, color, design guidelines.
You will engage in sketching and ideation for your class project.
This lecture overviews and introduces common software applications (tools) to help with both: data analysis and the creation of visualizations. We will overview tools for programming environments such as python (e.g., Seaborn) and JavaScript (e.g., D3), but also tools using common user interfaces (e.g., Rawgraph, Datawrapper). The lecture will not teach how to use these tools, but focus on a high-level overview of the many different tools and workflows exist to create data visualizations.
The tutorial will discuss visualization tools with us and the peers. We also run a sneak-preview tutorial on D3.
Choose between a selection of lectures and readings on
- Basic Charts
- Multidimensional data
- Geographic data
You will work on preparing and exploring your data for your class project.
Choose between a selection of lectures and readings on
- Networks
- Trees + Hierarchies
- Temporal Data
You will engage in critical analysis of your class project progress.
This lecture focuses on effective presentation and communication techniques when using visualizations in e.g., infographics and presentations. Other presentation media can include videos, posters, or data comics. The lecture investigates how presenting and talking with visualizations is different than using visualizations for exploring and analyzing data.
In the tutorial, you will have the chance to work on a storyboard for the story you want to tell with your data.
Physical visualization and visualization of personal data.
This week gives time to work on your project.
This week is featuring no content but gives you time to work on your project. You will have the chance to meet the organizers in a private session to discuss your projects.
This lecture covers techniques to assess if a given visualization technique (existing or your own creation) is “successful”. Successful is a broad term and refers to both effectiveness and efficiency in which a user or audience are supported in their analysis or understanding of the data. The lecture proposes a simple heuristic, Readability, Understandability, Supportiveness, Truthfulness, Insightfulness and Communication
This lecture talks about the need for interaction in visualization and will present a range of interaction techniques for specific visualization related tasks: select, explore, reconfigure, encode, abstract, filter, connect.
You will continue work on your project and run a mini-evaluation with your peers.
We will host a variety of guest talks, some of which will be run in conjunction with the Edinburgh Data Visualisation Meetup. Talks last year covered a variety of areas, with speakers' backgrounds across diverse areas including:
- digital humanities
- knowledge representation
- data journalism
- personal and social data
- bioinformatics/biological data visualisation
- data-driven storytelling
- interaction techniques
- immersive environments – MR/VR/AR/XR