ISOM-430 Prescriptive Analytics and Data Mining
This course gives a broad introduction to machine learning concepts, techniques, and algorithms as well as some topics and applications of Optimizations, Simulations, and Data Mining. Students will learn and obtain hands-on experience on applying supervised and unsupervised learning methods. Supervised learning topics include K-Nearest Neighbors, Linear and Quadratic Discriminant Analysis, Decision Trees, Support Vector Machines, Neural Networks, text mining and unsupervised learning topics include k-means clustering and Principal Component Analysis. Students will learn to match the data with the most appropriate and promising data mining algorithms; implement the training, testing, and validation phases of l the modeling process; and determine the optimal decision based on the insights and predictions from the data. This course is the BDBA program's Capstone.
Prerequisite
ISOM-330; this course is the Capstone course of the Big Data and Business Analytics major.
Offered
Fall, Spring