Statistical Methods for Data Science

3.00 credits · Undergraduate

Prereq: MATH 1151; 3 credits from MATH 1311, 2311; or permission of the Department This is a second course in the concepts and techniques of probability, statistics, and data science. It further emphasizes practical computational techniques for modelling, understanding, and making predictions with large-scale data, as well as the mathematical basis of those techniques. Topics covered may include: basic combinatorics, independence and conditional probability, Bayesian methods, linear and nonlinear regression, correlation estimation and prediction, goodness-of-fit tests, and machine learning. (Format: Lecture 3 Hours, Laboratory 1 Hour) (Exclusion: Any version of MATH 2321 previously offered with a different title)