STATISTICAL LEARNING FOR DATA SCIENCE

Catalog description

This course introduces the core statistical concepts for machine learning, including both supervised and unsupervised learning. Topics include classification, regression, clustering, and dimensionality reduction, with particular emphasis on the underlying mathematical principles. Practical implementation using Python is included, along with essential skills in data preprocessing, cleaning, and transformation to address real-world data challenges. By the end of the course, students will be able to analyze data sets, build predictive statistical models, and evaluate their performance. PREREQ: STAT 342 AND MATH 355