CIS607 - Survey of Predictive Analytical Techniques

This course addresses the foundation of using predictive statistics on big datasets to guide the decision-making process. The focus will be on applied examples using realistic data associated with marketing research and operations. Models implemented include multiple logistics regression, principle component analysis, factor analysis, propensity score matching, classification, decision trees, and clustering with analytical estimations using spreadsheet software or SPSS. Hypotheses formulation and testing, sampling methodologies, determining an appropriate sample size, levels of significance, confidence intervals, interpreting results (p-value and critical value approaches), and the application of A/B testing will be covered. In addition, market based analysis and product launch models will be explored. Finally, students will be introduced to exporting models into operating systems (OS).
Prerequisite(s): CIS 621 or HA607 or PA 601 and CIS 622
Fulfills: 4+1
Credits: 3