CSC 442 - Introduction to Data Science

Catalog Description:
Overview of data structures, data lifecycle, statistical inference. Data management, queries, data cleaning, data wrangling. Classification and prediction methods to include linear regression, logistic regression, k-nearest neighbors, classification and regression trees. Association analysis. Clustering methods. Emphasis on analyzing data, use and development of software tools, and comparing methods.
Contact Hours: Prerequisites: [MA305 or MA405] and [ST305 or ST312 or ST370 or ST372
Co-requisites: None
Restrictions: None
Coordinator: Dr. Rada Chirkova
Textbook: Data Mining with R

Course Outcomes:
Our goal is to help you gain skills in handling and analyzing data from 'end to end.' To mirror data science working environments, some of your assignments and in-class work will be done in multidisciplinary teams. By the end of this course, we want you to be able to:


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