ECON381

Analytics is the process of transforming data into insight in order to make better-informed decisions. Predictive analytics is the branch of analytics problem type that focuses on the central question of “what will (or could) happen?” This involves making predictions by describing static and dynamic relationships using a collection of techniques including, but not limited to response surface modeling, simulation, and forecasting. This course will focus on developing a toolkit for solving two important and common types of prediction problems: 1) formulating a continuous prediction; 2) formulating a categorical (discrete) prediction. With these goals in mind, methodologies will be introduced by leveraging modern-day software implementation and machine learning when appropriate. By the end of the course, you will know how to estimate and assess the performance of (validate) a variety of predictive models for applications in business. Prerequisites: (ITMG 100 with a minimum grade of C- or BUSN 101 with a minimum grade of C- or ISYE 330 with a minimum grade of C-) and (ECON 216 with a minimum grade of C- or ECON 217 with a minimum grade of C- or ISYE 330 with a minimum grade of C- ) and (MATH 130 with a minimum grade of C- or MATH 133 with a minimum grade of C- or MATH 150 with a minimum grade of C-) and ((BUAN 314 with a minimum grade of C- or BUAN 370 with a minimum grade of C-) or (ECON 201 with a minimum grade of C- and ECON 202 with a minimum grade of C-) or ISYE 330 with a minimum grade of C-).