Year of Study:	3-4
Credit Units:	3
Duration:	45hours
Prerequisites:	AIN4741 Predictive Analytics I, or with the Module Coordinator’s permission and upon endorsement of the relevant Head

This module introduces the elements of predictive analytics and continues from AIN4741 Predictive Analytics I. Topics covered classifications, tree-based methods, and unsupervised learning. Computing software will also be used for implementations. Students will apply the elements of predictive analysis to business problems.

Upon completion of this module, students should be able to:                       Weighting
(if applicable)

a. construct decision trees for both regression and classification;                      55%

b. apply cluster and principal component analysis to enhance statistical           20%
learning; and

c. formulate business problems, analyse data with decision trees,                    25%
clustering, and principal component analysis, and develop predictive
analytics solutions in a scientific manner.

Total: 100%