Timetable information in the Course Catalogue may be subject to change.

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : Edinburgh Futures Institute : Edinburgh Futures Institute

Postgraduate Course: Representing Data (fusion on-site) (EFIE11002)

Course Outline
SchoolEdinburgh Futures Institute CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course will introduce students to practical data representation. It will enable students to understand data visualisation theory and practice, while simultaneously inviting them to challenge and extend these concepts throughout the course. Students will examine a range of different methodologies and practices for representing data in a variety of formats including physical and embodied formats.
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) - On-Site Fusion Course Delivery Information:

The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. This approach (our 'fusion' teaching model) offers students flexible and inclusive ways to study, and the ability to choose whether to be on-campus or online at the level of the individual course. It also opens up ways for diverse groups of students to study together regardless of geographical location. 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: students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- Unless the lecturer or tutor indicates otherwise you should assume the session is being recorded.

As part of your course, you will need access to a personal computing device. Unless otherwise stated activities will be web browser based and as a minimum we recommend a device with a physical keyboard and screen that can access the internet.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  130
Course Start Semester 2
Course Start Date 15/01/2024
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 3, Seminar/Tutorial Hours 2, Supervised Practical/Workshop/Studio Hours 10, Online Activities 5, Other Study Hours 5, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 73 )
Additional Information (Learning and Teaching) Other Study: Scheduled Group-work Hours (hybrid online/on-campus) - 5
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Summative Assessment:

The course will be assessed by means of the following assessment components:

1) Digital Annotated Portfolio (Individual Assessment) (50%)

Practical data visualisation computational notebooks, involving evaluating and redesigning data visualisations (Individual).

2) Data Representation Group Project (50%)

Assessment comprises of:
(i) Data Set and Early Sketches
(ii) Data Representation Group Project: Final Data Representation and Report

Final group data representation task - identifying, collecting, preparing and visualising / representing data, alongside detailing the process and thinking behind the representation performed.
Feedback Feedback on the 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 will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).

Programming feedback will be in-person at drop-in times, and provided by a mix of peer discussion and discussion with PG 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 + tutor feedback on visualisation redesign / critique.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Analyse the form of a dataset and demonstrate basic skills in producing various representations of these data.
  2. Engage critically with the fundamentals of theory and practice of data visualisation and representation.
  3. Work well in a team to effectively communicate data to a particular audience.
  4. Engage in constructive critiques of the design and narrative of data representations.
Reading List
Indicative 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
Additional Information
Graduate Attributes and Skills Students will develop key visualization skills by directly engaging with complex real world data.

For each final visualisation product, they will produce collaboratively a written design document.

Working in small interdisciplinary teams, they will develop communication, autonomy, accountability and skills in working with others.
KeywordsData,Data Representation,Data Visualisation,Data Physicalisation
Course organiserDr Uta Hinrichs
Tel: (0131 6)51 5615
Course secretaryMiss Katarzyna Pietrzak
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information