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IE 529 - Stats of Big Data & Clustering

Fall 2021

Official Description

This course will cover various foundational topics in data science. Parametric and non-parametric methods. Hypothesis testing; Regression; Classification; Dimension reduction; and Regularization. Unsupervised and semi-supervised learning, along with a few case studies. Course Information: 4 graduate hours. No professional credit. Prerequisite: MATH 415 and IE 300 or equivalent. All ISE graduate students and students enrolled in the Master of Science in Advanced Analytics (MSAA) are eligible to take the course.

Related Faculty


Stats of Big Data & ClusteringA66823LCD41300 - 1350 M W F  112 Transportation Building Carolyn L Beck
Stats of Big Data & ClusteringO75999OLC4 -    Carolyn L Beck