Course Catalogue

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Data drives decisions, discoveries, and public discussion. Data visualisations can act as an easily accessible support of these. They are analysis tools as way as the key means to communicate insights from data. This course will enable participants to generate impactful visualisations, to plan and assess graphs, and to report on quantitative data in a systematic, accessible and trustworthy way.

5 weeks, starting w/c 25/Apr/2022.

Pre-recorded lectures, 1h/w [Lecture] followed by collaborative, reflective and self-testing activities (Moodle quizzes, picture boards ("padlets"), and forum activities).

Practical (self-study) workbooks and post-lecture readings [Independent study] - Weekly activities are released each Monday at 9.00am

GTA-led online support hours with demonstrations 1h/w, sessions will run Wednesday-Friday 16:00-18:00 (Week 1-5), learners are encouraged to attend one 1h session [Practical Classes and Workshops]

Mandatory: General computer literacy (e.g. familiarity with MS Office programmes and ability to install software), access to a computer with a stable internet connection as well as Zoom (for help sessions).

Recommended: Prior experience using statistical software especially R - The microcredential course "Data skills for processing and presenting data" (Dr Emily Nordmann) is recommended as an excellent prior training with some technical overlap.

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The summative assessment consists of generating a visualisation and writing a justification in a brief report (500word, approx. one A1 page). - The assignment is set in week 4 and due one week after the course ended. It is supported via the feedback and templates in the formative assignment, as well as a live Q&A session in week 5.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The aim of the course is to teach participants the skills to plan, create, and distribute data visualisations that aid decision making, enable effective report and tell clear messages. Moreover, the course introduces and applies standards for impactful, trustworthy and accessible visualisations.

By the end of this course students will be able to:

- define and discuss different types of data visualisations and their uses, in the context of providing evidence for practical questions. [Knowledge]

- describe and discuss standards which make data visualisations impactful, trustworthy and accessible,* with a view to critically assess data visualisations, considering how they can be more or less effective, and the ways in which they can potentially mislead. [Comprehension]

- use data visualisations to answer questions and tell "data stories" in the context of short reports. [Application and Analysis]

- plan, create, and distribute visualisations that allow easy communication, aid decision making, and empower users to learn from data, using the opensource software R and free web services. [Synthesis]

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.