Aims and Fit of Module

This module aims to cover methodology, major software tools and applications in data mining. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining. It focuses more on usage of existing software packages (mainly in R) than developing the algorithms by the students. Students will be required to work on projects to practice applying existing software.

Learning outcomes

A.	Introduce students to the basic concepts and techniques of Data Mining

B.	Demonstrate knowledge of statistical data analysis techniques used in decision making

C.	Apply principles of Data Mining to the analysis of large-scale problems

D.	 Develop skills of using recent data mining software for solving practical problems

E.	 Gain experience of doing independent study and research

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 labs.