SYS 5435 - Data Analytics

(4 credits)

Various techniques for analyzing data with multiple dependent and independent variables are introduced, with an emphasize on applied methodologies and applications in management and engineering fields. Topics covered include regression, logistic regression, multivariate analysis of variance, principal components analysis, cluster analysis, neural networks, ARIMA and data visualization. Effective use of advanced data analysis software for solving real-world engineering problems is addressed.

Corequisite(s): ISE 4435 and ISE 5435

Course revisions made after the Graduate Catalog publication date will be posted in the Graduate Catalog Addendum.