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ST 452 Statistical Learning and Data Analytics
Basic principles and techniques of data analytics, data wrangling, and visualization. Statistical learning techniques, including (a) supervised learning topics like regression and classification, (b) unsupervised learning techniques like clustering and principal component analysis, (c) an introduction to deep learning, and (d) an Introduction to text mining and natural language processing. The concepts will be implemented with R, and the focus will be on practical case studies with real-world datasets.
Prerequisite: ³§°ÕÌý430 and (³§°ÕÌý308 or ³§°ÕÌý114 or °ä³§°äÌý111 or °ä³§°äÌý116 or ±õ³§·¡Ìý135)
Typically offered in Fall only