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. Finally, students will study methods and algorithms that support BDA, and gain an understanding of the best practice in BDA.

Learning outcomes

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

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

C.	Show proficiency with at least one data analytics software package.

D.	Demonstrate awareness of issues related to computer and data security

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 will be delivered by a combination of formal lectures, seminars, and computer classes. Lectures will introduce students to the academic content and practical skills. Seminars will be used to expand the students understanding of lecture materials. Computer classes will allow students to practice and be proficient in the skills as specified in this module.  In addition, students will be expected to devote unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material and background reading.