This course will explore some advanced principles and techniques of data mining, with emphasis on applications in risk management and business intelligence. Topics include data mining process for data transformation and integration, data preprocessing, data mining algorithms and evaluation of data mining models and results. Advanced topics include data stream analysis, using data warehouse for decision support, supervised, semi-supervised and unsupervised learning techniques in data mining. We will cover advanced data mining applications in credit risk analysis, scale up methods for mining massive customer data and various novel applications such as data mining applications in social network analysis. Projects are aimed at familiarize the students with the entire data mining process rather than isolated applications.