Modeling and analysis of data, usually very large datasets, for decision making. Review and comparison of software packages used for Analytics Modeling. Multiple and logistic regression, multi-stage models, decision trees, network models, and clustering algorithms. Investigate data sets, identify and fit appropriate data analytics models, interpret statistical models in context, distinguish between data analytics problems involving forecasting and classification, and assess analytics models for usefulness, predictive value, and financial gain. Syllabus (2021 Fall).pdf