Data Analytics and Predictive Modeling

Catalog description

In systems where parameters can vary, we often want to understand the effects that some variables exert on others and their impact on system performance. “Data Analytics and Predictive Modeling” describes a variety of machine learning and data analysis techniques that can be used to describe the interrelationships among such variables. In this course, we will examine these techniques in detail, including data cleansing processes, data clustering, associate analysis, linear regression analysis, classification methods, naïve Bayes, neural networks, random forests, variable screening methods, and variable transformations. Cases illustrating the use of these techniques in engineering applications will be developed and analyzed throughout the course. Note: Students required to take ISEE-561 for credit may not take ISEE-661 for credit.