MATH 475 Models and Simulation for Data Science
This course develops advanced statistical topics relevant to data science. Topics include multiple linear, general linear, and logistic regression; transforming data; Monte Carlo simulation of stochastic systems; re-sampling based inference (bootstrap, etc.); likelihood theory and Bayesian methods; model selection and performance.
Prerequisite
A “C” or
better
in
class='sc-courselink' href='/en/2019-2020/2019-2020-academic-catalog/course-descriptions-undergraduate-and-graduate/narrative-courses/math-316'>MATH 316 and in class='sc-courselink' href='/en/2019-2020/2019-2020-academic-catalog/course-descriptions-undergraduate-and-graduate/cs-computing-sciences/100/cs-160'>CS 160 or CS 170