IE 529
IE 529 - Stats of Big Data & Clustering
Spring 2024
| Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|---|
| Stats of Big Data & Clustering | IE529 | A | 68102 | ONL | 4 | - | Carolyn L Beck | ||
| Stats of Big Data & Clustering | IE529 | B | 75698 | LCD | 4 | 1300 - 1350 | M W F | 2200 Sidney Lu Mech Engr Bldg | Carolyn L Beck Vincent Leon |
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Official Description
Covers 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 416 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.