3.00 Credits
.The course will introduce students to the process of extracting insight about the world through data. This includes collecting, organizing and visualizing data, understanding statistical and machine learning methods, training these methods on a particular data set, and validating and testing the results. The methods will include both supervised and unsupervised learning. Discussions will also include the importance of the bias-variance trade-off. Though the course will make use of appropriate statistical software such as SAS, R, or Python, no prior coding experience is necessary.
Prerequisite:
STAT 220