Aims and Fit of Module

The module aims to introduce the environment and the main application domains where Big Data Analytics (BDA) takes place, to introduce general framework and process of BDA, to study technologies, platforms and tools that are currently used in BDA, to study methods and algorithms that support BDA, and to gain an understanding of the best practice in BDA.
In that this is an optional module, to well utilise the university resource, the module delivery is subject to meeting a minimum number of students (10).

Learning outcomes

A. Demonstrate a solid understanding of concepts, processes and issues related to Big Data Analytics (BDA);

B. Identify applications of BDA that can help improve business operations;

C. Determine the appropriate use of analysis methods, algorithms, technologies, tools, and software packages to support data analysis involving practical scenarios;

D. Demonstrate proficiency with at least one data analytics software package.

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.


The module will be delivered via lectures and supervised laboratory sessions, as well as private studies.
In that this is an optional module, to well utilise the university resource, the module delivery is subject to meeting a minimum number of students (10).