We list here answers to questions from applicants and more detail about discussions on key topics raised during interviews with potential course applicants. This is a live document and will be updated regularly; please e-mail us if you have any questions not addressed here.
Eligibility criteria for a fee waiver are laid out here.
Applications are not managed by the organisers of this visualisation course but centrally through the University of Edinburgh. Applications for 2021 are now closed.
We have provided answers to common questions from applicants on the application process, along with detail on eligibility for fee waivers.
The Bayes Centre will continue to accept applications for this course till close of play Fri, 21st April 2021.
Offer letters refer to the wider Bayes Centre’s data skills portfolio, with course detail:
Provided you stated in the personal statement section of your application for this course you should have been assigned to it. Otherwise please e-mail Bayes to clarify this.
There are no restrictions on how many courses you may take each year. However, each course requires up to 100 hours of study on average, so you may wish to consider whether course loads may pose a problem where schedules overlap.
If you are reliant on fee waivers, please note that these are normally restricted to one per applicant for each run of the Workforce Development portfolio. However, the current disruption to work, normal schedules and processes means that exceptions may be made on a case by case basis. Please contact the Bayes Centre if you wish to apply for another course in addition to this one.
We will use the Blackboard Collaborate virtual classroom for interactive sessions (tutorials and lecture Q&As). To join you will need an internet connection that can support web conferencing and a web browser. Blackboard will also normally work on a mobile device via its app. We also have full access to Teams, so will have this as a backup option if this provides more reliable connectivity for the class.
We will provide information on accessing and/or installing any other software used in tutorials as and when needed. This is not required as we will provide live demos where necessary. If you wish to try a new tool, some may be accessed from a web browser. Installing desktop tools will remain optional.
We have a range of potential participants with proficiency ranging from basic – using mainly office-based tools, to people who use dedicated visualisation tools, to people with some knowledge of programming. We will provide support to all participants regardless of technology proficiency, to acquire understanding of basic and more advanced visualisation concepts, and link those to practical application.
Each participant will have the opportunity to focus on a sub-set of advanced visualisation options, based on their project definition and other skills desired for work or personal interests.
The tutorials, while optional, are considered the backbone activity of the course. Tutorials map to each week’s lecture topics, to deepen understanding of concepts discussed and map theory and guidelines to practical application using your project data and ideas. They are practical, hands-on sessions to help you to develop, share and evaluate ideas you have for your project.
Please note that if you choose not to attend tutorials you must still follow the tutorial scripts; these are linked to brief reflection exercises that guide you through the process of completing your final project.
We encourage participants to come with an idea and/or data for their project, from work or of personal interest.
Please note you do not need to have an idea formulated in advance; we will address projects and input data in the first week’s tutorials. We will also provide pointers to public, open datasets.
Where necessary we will provide support for working with sensitive data, including the use of non-disclosure agreements (NDAs), anonymisation of data or the generation of synthetic data from your source data.
To take full advantage of the (optional) hands-on tutorial sessions, you may wish to share some aspects and visualisations about your data, to obtain peer and tutor feedback. We will provide guidance for doing so without revealing sensitive information.
We anticipate most participants will carry out individual projects. However, you may work in small groups, for example, colleagues in the same company may wish to collaborate to complete a larger task, or self-forming groups may bring together different perspectives to develop a more complex project than could be completed individually within the constraints of the course.
Please note that in either case each participant will complete a brief, individual reflection exercise to accompany their project.
It depends, visualization is a broad field with many techniques covering many data types. For example, the course covers networks, hierarchies and set-relationships. If you have textual data that you are analyzing and seek to represent structures in the data and the concpets, the course can help you.
The course is certified by the University of Edinburgh; it therefore has a formal assessment component. Overall assessment will combine results from brief reflection exercises linked to tutorials and your final project. To structure the learning process participants will receive general and one-on-one feedback as the course progresses.
We aim to provide a moderated discussion forum to support peer learning and informal networking, alongside the virtual classroom, which may be used for smaller, informal discussions outwith timetabled tutorials.