Year of Study:	3 - 4
Credit Units:	3
Duration:	45hours
Prerequisites:	(1) AMS1001 Introduction to Linear Algebra and Calculus, AMS1301 Foundations of Data Science (or AMS1303 Probability and Statistics), and AMS2640 Statistical Computing in Practice; or (2) With the Instructor’s permission and upon endorsement of the relevant Head or Programme Director.

This module aims to provide students with the data mining techniques for solving practical problems. Students will learn a set of tools (e.g, SAS, R, Weka, Tableau) for data visualization and apply data mining techniques such as classification and association rules, cluster analysis and dimensionality reduction to analyse real-life problems. Students are required to work effectively in a team to complete a project.

Upon completion of this module, students should be able to:

1. examine the significance of knowledge discovery process, data quality and preprocessing;


2. apply visualization techniques for effective communication;


3. apply data mining skills and techniques;


4. interpret and present the results in a scientific and concise manner; and work effectively in a team and solve real-life problems.