Explores the data science lifecycle with a focus on machine learning. Topics include data preparation, unsupervised and supervised analyses, ensemble methods, results illustration, and data communication. Unsupervised methods include clustering, association rule mining, and dimensionality reduction. Supervised models include regression, tree-based models, Bayesian models, and support vector machines. Recommended prerequisites: probability, statistics, multivariate calculus, and linear algebra. Recommended restrictions: This course is specific to Data Science students and the MS-DS degree program, this course would not be suitable for CSCI majors to meet CS degree requirements.
