Statistical Methods in Data Science. 3 credits, 3 contact hours

Catalog description

Prerequisites: MATH 661 or MATH 663 , or permission by instructor. This course introduces students to concepts in statistical methods used in data science, including data collection, data visualization and data analysis. Emphasis is on model building and statistical concepts related to data analysis methods. The course provides the basic foundational tools on which to pursue statistics, data analysis and data science in greater depth. Specific topics include understanding the aims of statistical learning and data science, principles of data analysis, linear and logistic regression, discriminant analysis, tree-based approaches, nonlinear modeling techniques, resampling methods, model/variable selection, black-box modeling, unsupervised learning. Students will use R statistical software for the coursework.