Concentration:
STEM (Foundational), DaBA (Group 2)
Course Prerequisites:
Ross Graduate Standing excluding MM and PhD
Advisory Prerequisites:
TO 501 or 502 or equivalent
Data Mining and Applied Multivariate Analysis --- Innovations in information technology has resulted in data intensive, managerial environments. A virtual flood of information flows through systems, such as enterprise resource planning and the Internet. What to do with all this data? How can it be transformed into actionable information? The objective of this course is to introduce business leaders to powerful methods for understanding and obtaining managerial insights from multivariate data. The course is designed for both managers who have direct responsibility for producing analyses and for managers who have to interact with area experts who produce the analyses. The methods include data reduction techniques - principle component analysis, factor analysis, and multidimensional scaling; classification methods - discriminate analysis and cluster analysis; and relational methods - multivariate regression, logistic regression, and neural networks. Emphasis is placed on the application of the method, the type of data that it uses, the assumptions behind it, and interpreting the output. User friendly and powerful statistical software will be used.
