Module detail

SHE level	11
SCQF credit points	15
ECTS credit points	7.5
Module code	MMI226820
Module Leader	Zoe Tieges
School	School of Science & Engineering
Subject	Computing
Trimester	B (January start), C (May start), B (January start), C (May start)

Summary of content

This course aims to equip students with knowledge and skills required for successful data visualisation. The goal of data visualisation is to expose the underlying structure of a dataset using visual representations which are targeted towards the human visual perceptual system. Students will gain experience in the theory, design, implementation and evaluation of data visualisation systems. Appropriate software tools will be utilised throughout the module to create effective visual representations to translate complex data into clear information, allowing the target audience to extract value from it. Students will learn how to use advanced visualisation techniques such as data storytelling and interactive visualisations to communicate information clearly. By the end of this module, students will be capable of analysing the appropriateness of a given visualisation in relation to a problem, select and implement appropriate data visualisation systems for a given dataset and evaluate the effectiveness of this system.

Module details

The learning and teaching strategy for this module has been informed by the university's 'Strategy for Learning' design principles. The course material is introduced through lectures and laboratory sessions that draw upon and extend the lecture material to deepen students' knowledge. Lectures are supplemented by directed reading to relevant sources both hard and electronic format and varied further reading is encouraged. The laboratory sessions are designed as a set of formative exercises and a substantial summative exercise spanning several weeks. The formative exercises introduce a range of technologies that allow students to gain confidence and build knowledge of the range of solutions that can be applied to specific problems. Summative exercises provide experience in real-world problem-solving and challenges students to demonstrate analytical skills and capacity for divergent thinking. Tutorials will be used to help explain and elaborate on both the lecture material and the laboratory exercises. During all lab and tutorial sessions students receive formative feedback on their performance in undertaking the laboratory and tutorial exercises. Summative feedback and grades are also provided for the coursework assignments undertaken as part of the module, using GCULearn. GCU Learn is also used to provide the students with module specific Forums and Wikis to stimulate student and lecturer interaction outwith the normal lecture, and laboratory and tutorial sessions. Flexible learning is encouraged and supported. All teaching materials and self-testing exercises are made available on GCULearn and links are provided to external materials such as podcasts, MOOCs, videos and relevant literature. All the computing resources used for laboratories are made available either by virtual machine images (supplied to students for use on their own computers) or online using industry standard cloud computing services provided by major global computing industry vendors. Due to the provision of all material and computing faciliti

Module structure

Activity	Total hours
Lectures	11
Tutorials	0
Practicals	22
Seminars	0
Independent Learning	99
Assessment	18
Placement	0

Assessment methods

Component	Duration	Weighting	Threshold	Description
Course Work001		30	35	CW1: Presentation
Course Work002		70	35	CW2: Practical Assessment

Academic essentials

Data Visualisation

Summary of content

Module details

Module structure

Assessment methods