F20DV Data Visualisation and Analytics

Ben Kenwright

Course co-ordinator(s): Dr Ryad Soobany (Dubai), Ben Kenwright (Edinburgh).


To provide students with the principles and programming tools (e.g. D3.js) to enable them:

  • To create engaging and intuitive graphical and interactive web applications that allow users to search, explore, reveal, partition, understand, discover and communicate the structure and information in large data sets;
  • To convey ideas effectively, considering both aesthetic form and required functionality that will provide insights into different types of dataset (structured and unstructured);
  • To stimulate user engagement, attention and discovery;
  • To be able to implement interactive web-based visualisation systems in D3.js and assess their effectiveness.

Detailed Information

Course Description: Link to Official Course Descriptor.

Pre-requisites: Numeracy and basic OO programming ability (3rd year CS).

Location: Dubai, Edinburgh, Malaysia.

Semester: 2.


Overall aims:

  • Use case scenarios (browsing, search, engagement, summarisation, brain storming)
  • Example data sets and visualisations, problems of big data
  • Design principles & Data source types
  • D3 JavaScript library and programming
  • Data, information and display/infographic types (bar, pie, tree, pack, line, map)
  • Abstraction methods including clustering, topic modelling, dimensional reduction
  • Interaction (tooltips, dashboard interaction, filtering, focussing, transitions)
  • Project requirements (D3 web application)

Learning Outcomes: Subject Mastery

Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Informed Learning)

  • Understanding of the data visualisation and data analysis processes.
  • Knowledge of different infographic types, interactivity and design choices.
  • Knowledge of different information and data types.
  • Demonstrate a critical awareness of the main types of information and the appropriateness and effectiveness of associated visualisation and analysis techniques.

Learning Outcomes: Personal Abilities

Industrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT

  • Rational problem identification, concepualisation and definition.
  • Critical analysis and solution selection.
  • Exercise autonomy, initiative, and creativity in the application of data visualisation & analysis techniques.
  • Demonstrate critical reflection on system development and performance (PDP).
  • Communication via report and interactive web app

Assessment Methods: Due to covid, assessment methods for Academic Year 2021/22 may vary from those noted on the official course descriptor. Please see:
- Maths (F1) Course Weightings 2021/22
- Computer Science (F2) Course Weightings 2021/22
- AMS (F7) Course Weightings 2021/22

SCQF Level: 10.

Credits: 15.