Topics in multiple linear regression, estimation of model parameters, inferences, diagnostics, model assumptions, ANOVA formulation.
Introduction to elementary sampling concepts. Includes random sampling, stratified sampling, cluster sampling and systematic sampling. Inferences and assumptions are presented for all sampling methods.
This course is an introduction to the elements of data science. Topics include data visualization, data wrangling, statistical learning and predictive analytics, text mining and spatial data.
Individualized investigation under the direct supervision of a faculty member. (Minimum of 37.5 clock hours required per credit hour.)
Special Notes
Maximum concurrent enrollment is two times.
This is a project course in data science and related fields. Interdisciplinary teams will analyze a new data science problem, develop a model, and control for error and overfitting.