Module Catalogues

Big Data-Applications in Business

Module Title Big Data-Applications in Business
Module Level Level 4
Module Credits 5.00
Academic Year 2021/22
Semester SEM2

Aims and Fit of Module

This module provides students with a thorough understanding of the advances in the area of data analytics and the role it plays in supporting business and financial decisions. Students will learn how to critically apply data analytical techniques in real-world business problems and how to present the results in the form of reports and presentations intended for non-expert audiences.

Learning outcomes

A. Demonstrate in-depth understanding and critical evaluation of big data analytical techniques.
B. critically apply data analysis techniques to solve business problems in the fields of finance, marketing and management.
C. demonstrate a critical ability to present and report complex analytical results to non-experts in written form.
D. demonstrate a critical ability to present and communicate complex statistical results in front of an audience of non-experts in spoken form.

Method of teaching and learning

The module primarily involves instructor-led lectures including practice-based sessions, supported by guest lectures and company visits. The module leader will illustrate the applications of these techniques to real-world business problems with case studies.
During the course of the module, students will be required to determine the focus of their project assignment. The project will have to be in the areas of finance, marketing or management. Depending on their chosen topic, students will be expected to apply what they have studied to one of the following areas:
(i) Predictive analytics for finance applications (asset pricing, institution risk management and credit risk management, principal components analysis, time series);
(ii) Marketing analytics (social media analytics, collecting, analyzing and deriving insights from social media chatter, techniques of sentiment analysis and text mining);
(iii) Management analytics (operations and supply chain analytics, sourcing, inventory management, manufacturing, quality, sales and logistics).