Introduction to Data Science

Catalog description

Uses computer programming, data science tools and simulation techniques to explore and analyze data and make inferences and predictions. Topics include computer programming to load, access, manipulate, aggregate, pivot, join and visualize tabular data, apply functions on table columns, data standardization, simulation techniques for hypothesis testing, bootstrap, and basic machine learning models, including regression, classification, and k-nearest neighbors. Dual-listed: CS 110 and CS 320 are dual-listed. The courses may be taught together by the same instructor. May not be taken for credit by students who have received credit for CS 110. Prerequisite(s)/Corequisite(s): Either MATH 125 , MATH 126 , MATH 132 , or MATH 160 . Satisfies GE area: UD25