Aims and Fit of Module

The module aims to introduce the environment and the main application domains where Big Data Analytics (BDA) takes place, and the general framework and process of BDA. It also provides students to study technologies, platforms and tools that are currently used in BDA. Students will study methods and algorithms that support BDA, and gain an understanding of the best practice in BDA. Finally, students will gain practical experience in data visualisation methods for analysing the structure and dependencies of data sets. Students will also study techniques for creating effective visual data presentations.

Learning outcomes

A. Demonstrate a solid understanding of concepts, processes and issues related to Big Data Analytics (BDA);
B. Apply appropriate analysis methods, algorithms, technologies, tools, and software packages to analysis a given data set in a practical scenario;

C. Select appropriate data analysis and visualisation methods to highlight particular features for a given data type and a set of analysis objectives or user requirements.

Method of teaching and learning

The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments.
This module is taught using a combination of lectures, seminars and computer lab sessions. In the computer lab sessions, students will gain experience with software tools for big data analytics and data visualisation.