Introduction to Data Analytics.

Catalog description

Students will be introduced to Data Analytics and will utilize a variety of tools while they practice skills to describe, summarize, and make inferences about data sets leading to modeling for decision making. Topics within this course will include: probability and probability distributions, regression analysis, logistic regression, supervised learning (Decision Trees) and unsupervised learning (Clustering). Spring. Prerequisites: ENG 101 , MAT 143 , CIS 121 ; CIS 150 or CIS 109 , CIS 133 (corequisites) or permission from the Dean.