Overview
Academic contacts
Offerings
Requisites
Other learning activities
Learning activities
Learning outcomes
Evaluate and apply different data visualisation techniques in non-immersive and immersive environments
Perform data visualisation for AI, data exploration and discovery of hidden patterns in innovative ways
Utilise effective data storytelling technique
Articulate and implement the different interactive techniques, interactive analytics, and user interactions with data
Utilise non-real-time and real-time simulation of live-streamed and offline data
Design and develop interactive and collaborative/multi-user data visualisation tools using Python, C#, JavaScript D3.js, Unity3D, Unreal, WebGL, and/or XR technologies and devices
Use suitable Software Engineering approaches during design and development
Work collaboratively and demonstrate effective team-work ability. This skill is especially important for students who will be graduating and plan to work in the Information Technology industry. More importantly, collaborative work is a fundamental requirement when it comes to getting a job at the end of your degree.
Assessments
Additional information
The following topics will be covered:
• Introduction to data visualisation
• Data visualisation design process
• Data visualisation for AI
• Working with data, data exploration and discovery of hidden patterns in innovative ways
• Data visualisation techniques in non-immersive and immersive environments
• Interactive techniques and user interactions with data
• Interactive analytics
• Data storytelling
• Software Engineering and data visualisation
• Design and development of non-immersive and immersive environments, and technologies for collaborative data visualisation tools
Each student is expected to spend on average three hours per teaching week reading the lecture notes, books chapters and other recommended materials relevant to the topic covered in that week and spend a similar amount of time working on the workshop exercises for that week.