Teaching data mining by coalescing theory and applications

We report on our experience for the first time departmental offering of the data mining course in Spring 2005. The course was cross-listed such that both the upper level undergraduates and graduate students could attend. However, the majority of the registered students were undergraduates. Data mining, being a confluence of multiple fields, offers an interesting addition to the computer science curriculum. The main objective of the course was to provide grounding on both the theoretical and practical aspects of data mining and machine learning. In addition, the course used concepts learned in various courses throughout the undergraduate degree. The course utilized a machine learning toolkit, Weka, by the University of Waikato, New Zealand. In this paper, we present the various components of the course, structure, innovative assignments and discussions, and the project life cycle