Analytics Models

Catalog description

Analytical Models: In this course students will learn, apply, and interpret both supervised and unsupervised data mining methods and models commonly used by data analysts. Students utilize Python to implement various models with an emphasis on developing a conceptual understanding of how these models operate, the real-world systems in which they are applied, and interpretation of their results. Key models covered in the course may include, but are not limited to, Naive Bayes, K-Nearest Neighbors, Cluster Analysis, and Logistic Regression. Prerequisite: CDA 250/PHY 351. (Offered every spring term.) Credit: 4.