Applications of Data Science.

Catalog description

This course will apply and deepen student understanding of predictive models for analyzing data to enhance and support decision-making. Students will learn to distinguish between regression vs classification problems and choose an appropriate statistical model to predict a desired quantity of interest. Techniques for comparing model performance, assessing the validity of models, and making predictions on future data will be developed. Basic terminology and concepts in linear algebra such as matrix-vector operations and linear transformation will also be introduced and applied to develop statistical models. Students will also gain practice in effectively communicating the results of their analysis in written and oral format. The use of technology to perform analyses will be emphasized.