Foundations of Data Science

Catalog description

Uses computer programming, data science tools and simulation techniques to explore and analyze data and make inferences and predictions. Covers computer programming to load, access, manipulate, visualize, and aggregate tabular data, 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 320. Enrollment Restrictions: For students matriculating prior to Fall 2018, enrollment is restricted to students who have completed the Entry-Level Mathematics (ELM) requirement. For students matriculating in Fall 2018 or later, enrollment is restricted to students in Mathematics/Quantitative Reasoning Placement Categories I and II, or who have completed MATH 101 or MATH 103 or MATH 105 with a grade of C (2.0) or better. Satisfies GE area: 2