Introduces predictive analytics with applications in engineering, business, and econometrics. Topics include data preprocessing, predictive modeling with various regression and classification models (e.g., linear, logistic regression, tree-based methods, SVM, neural networks, etc.), time series analysis, and case studies. Provides a foundation of basic theory and methodology with applied examples to analyze large engineering, business, and econometric data for predictive decision making. Hands-on experiments with R will be emphasized. Offered by Systems Engr & Operations Rsch. May not be repeated for credit. Equivalent to SYST 568.
Applied Predictive Analytics
George Mason University
OR 568 DL1
Systems Engineering, Operations Research and Engineering Management
Times and Days
STAT 515 or Graduate Standing at the MSOR or MSSE programs.