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.
Course Description: Link to Official Course Descriptor.
Pre-requisites: Numeracy and basic OO programming ability (3rd year CS).
Location: Dubai, Edinburgh, Malaysia.
- Use case scenarios (browsing, search, engagement, summarisation, brain storming)
- Example data sets and visualisations, problems of big data
- Design principles & Data source types
- 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.