THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2021/2022

Information in the Degree Programme Tables may still be subject to change in response to Covid-19

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

Postgraduate Course: Representing Data (fusion online) (EFIE11001)

Course Outline
SchoolEdinburgh Futures Institute CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning 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 3d printing or 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:
- Lectures
- Code-alongs
- Group discussion, and group collaboration
- Supported workshop time focussed on visualisations and other forms of data representation

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 note that their interactions may be recorded and live-streamed. There will, however, be options to control whether or not your video and audio are enabled.

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
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2021/22, Available to all students (SV1) Quota:  5
Course Start Semester 2
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) 5 hours scheduled group work
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework

50% Practical data visualisation computational notebooks, involving evaluating and redesigning data visualisations (individual).

50% final group data representation task - identifying, collecting, preparing and visualising / representing data, alongside detailing the process and thinking behind the representation performed.
Feedback 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. Work on data representation with an awareness of the effects of physical impairments on the perception of such representations.
  5. 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
Data Visualization: A Practical Introduction (2019), Healy
Visualization Analysis and Design (2014), Munzner
Fundamentals of Data Visualization (2019), Wilke
Infographics Designers' Sketchbooks (2014), Heller & Landers
Knowledge is Beautiful (2014), McCandless
The Book of Circles: Visualizing Spheres of Knowledge (2017), Lima
The Functional Art (2011), Alberto Cairo
Cartographies of Time: A History of the Timeline (2010), Grafton & Rosenberg
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
Contacts
Course organiserDr Larissa Pschetz
Tel:
Email: L.Pschetz@ed.ac.uk
Course secretaryMiss Katie Murray
Tel:
Email: Katie.murray@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Prospectuses
Important Information