Postgraduate Course: Representing Data (EFIE11562)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | *EFI Skills and Methods Suite*
Please Note:
This course is only available to students enrolled on one of Edinburgh Futures Institute's postgraduate programmes.
This course introduces the theory and practice of data representation. Students develop an understanding of data visualisation principles while critically challenging and extending these concepts throughout the course. They examine a range of methodologies for representing data across digital, computational, physical and embodied formats, engaging with storytelling, design and critical perspectives. Through individual and collaborative work, students design, prototype and reflect on data representation projects using their choice of tools and environments. |
| Course description |
The course will give an overview of key aspects of data representation, from analysis of data to aesthetics, form and ergonomics. Students will be introduced to selected readings on the theory of data representation and be asked to engage with a variety of datasets. From this they will discuss different ways to represent, or potentially misrepresent, data as well as the role of narrative and medium in designing an effective representation.
Students will explore these datasets through different visualisation concepts and techniques, supported by notebook-based computer worksheets. Then, working in groups, they will also explore a dataset related to a challenge theme and work up a data representation as a visualisation, or physicalisation using other construction techniques. Alongside this the groups will document this process and the considerations made in producing their final output.
Taught sessions will cover a mix of time spent on:
1) Lectures
2) Code-alongs
3) Group discussion, and group collaboration
4) Supported workshop time focussed on visualisations and other forms of data representation
Edinburgh Futures Institute (EFI) - Hybrid Course Delivery Information:
The Edinburgh Futures Institute delivers many of its courses in hybrid mode. This means that you may have some online students joining sessions for this course. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities.
Students should be aware that:
- Classrooms used in this course will have additional technology in place: in some cases, students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- All presentations, and whole class discussions will be recorded (see the Lecture Recording and Virtual Classroom policies for more details).
You will need access to a personal computing device for this course. Most activities will take place in a web browser, unless otherwise stated. We recommend using a device with a screen, a physical keyboard, and internet access.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2026/27, Not available to visiting students (SS1)
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Quota: 100 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 12,
Seminar/Tutorial Hours 8,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
The course will be assessed by means of the following components:
1) Digital Annotated Portfolio (Individual) (50%)
Digital portfolio documenting at least four data representation exercises across analogue and computational methods. Includes brief reflective annotations (at least 5 annotations per visualization, approx. 40 words each).
Learning Outcomes Assessed by Component: 1, 2
2) Data Representation Group Project (50%)
Collaborative design, prototyping and reporting of a data representation project responding to a chosen dataset and audience. Includes illustrated written report (max 2000 words) and documentation of design process.
Learning Outcomes Assessed by Component: 1, 2, 3, 4 |
| Feedback |
Feedback on any formative assessment may be provided in various formats, for example, to include written, oral, video, face-to-face, whole class, or individual. The Course Organiser will decide which format is most appropriate in relation to the nature of the assessment.
Feedback on both formative and summative in-course assessed work will be provided in time to be of use in subsequent assessments within the course.
Feedback on the summative assessment(s) will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).
Formative Feedback Opportunity:
Formative feedback is ongoing feedback which monitors learning and is intended to improve performance in the same course, in future courses, and also beyond study.
Programming feedback will be in-person at drop-in times, and provided by a mix of peer discussion and discussion with tutors (either online or physical).
Automated quizzes will be used to provide regular immediate feedback on coding and other elements.
Peer feedback and/or instructor feedback will be provided on visualisation redesign / critique. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Analyse the form of a dataset and demonstrate basic skills in producing various representations of these data.
- Engage critically with the fundamentals of theory and practice of data visualisation and representation.
- Work well in a team to effectively communicate data to a particular audience.
- Engage in constructive critiques of the design and narrative of data representations.
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Reading List
The Visual Display of Quantitative Information (2001), Tufte
How Charts Lie - Getting Smarter about Visual Information (2019), Cairo
Visualization Analysis and Design (2014), Munzner
The Functional Art (2011), Alberto Cairo Design for Information (2013), Mireilles
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Data visualisation,pyhton,data representation,storytelling with data,critical data studies |
Contacts
| Course organiser | Mrs Dorsey Kaufmann
Tel:
Email: dkaufma2@ed.ac.uk |
Course secretary | Miss Abby Gleave
Tel: (0131 6)51 1337
Email: abby.gleave@ed.ac.uk |
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