This course focuses on data mining tools and techniques that are useful for a wide range of applications in manufacturing, service, logistics, health and medical, financial and banking, etc.  We discuss four basic data mining operation steps: business objective identification, data preparation, knowledge discovery, and consolidation/implementation. We cover both supervised learning and unsupervised learning methods and algorithms, including regression, classification, forecasting, clustering, association rules, and market basket analysis etc. The methods will be illustrated with case studies in credit card fault detection, telecommunication, express mail service, inventory management, customer relationship management, and bioinformatics.

Continuous Assessment: 100%

25% Coursework; 35% Midterm Test; 40% Group Project