ISE4435

Data Analytics

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.Additional Requirement(s): Major standing

School of Egr. and Comp. Sci.