Data-Driven Decision Making

Catalog description

Application & interpretation of analytics for real-life decision making. DAAN 881 Data-Driven Decision Making (3) The theory and application of several quantitative decision-making tools will be studied. The usefulness of these tools will be illustrated using projects and case studies throughout the course. Emphasis will be placed on the application of the tools and techniques and the results they generate. Finding patterns in data and appropriately grouping them are essential in the extraction of information in large datasets. This course will use Principal Component Analyses to transform highly correlated sets of data by means of orthogonal transformation. Cluster analysis will be used to properly group data when working with large datasets. When the outcomes involve categorical variables, Logistic regression techniques will be used to estimate the probabilistic values of the output. The decision space will be divided into smaller regions using Regression tree analyses. When factors are too numerous and highly collinear, Partial Least Square Regression methods will be performed.Public access datasets in the healthcare, transportation and finance industries will be used to demonstrate the applications and the limitations of these techniques.