BUS 397 Data Mining (Business Intelligence)
This course introduces the cutting-edge computing methods for the analysis of business and marketing big data which help in inferring and validating patterns, structures and relationships in data, as a tool to support decisions at all levels of management. Students learn key descriptive, predictive, and prescriptive data mining methods with both supervised and non-supervised machine learning algorithms, which produce information for non-structured and semi structured decision making. While the course introduces a systems approach to business data processing, emphasis will be given to empirical applications using modern software tools such as Data Mining in Solver-Analytics More specifically, students will become familiar with and demonstrate proficiency in applications such as Cluster Analysis, Market Basket Analysis. Logistic Regression, Naïve Bayes Classification, Entropy Calculation, Classification Trees. Engagement-based learning is provided by using real world cases as well as computer based hands-on for real data analysis. Ultimately, working in teams, students will make the month long projects in applying Data Mining analytical techniques on the real world business problems, and will make suggestions for improvement which will be backed by the new information, gained from DM. Projects are presented in groups. Research papers, which are based on the projects, are individual.