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 programs Capstone.
Prerequisite
Student has completed all of the following course(s) ISOM 330 - Applied Stats & Pred Analytics
Offered
Fall, Spring