MATH 500 - Prob & Stats for Data Science

This course introduces fundamental concepts in probability and statistics from a data science perspective. The topics include basic probability notions, classical combinatorial methods, conditional probabilities, Bayes theorem, random variables, useful distributions, expectations, variance, covariance, and correlations, conditional density, transformations, order statistics, the law of large numbers, the central limit theorem, point and confidence interval estimation, maximum likelihood methods, hypothesis tests. It aims at synergistically presenting rigorous probabilistic reasoning and problem-solving as well as computer-age statistical methods that are widely used in data science. This class will use R to produce graphs and run simulations to demonstrate theoretical results. Offered regularly. Prerequisite: MATH 323 or equivalent.