Applied Statistics for Data Science

Catalog description

An introduction to probability and statistics for students who have knowledge of calculus. Topics include randomness in observed data and descriptive statistics; probability theory: sample spaces, probability of events, Kolmogorov's axioms, conditional probability, Bayesian methodology, independence and dependence, discrete and continuous random variables, probability density and cumulative distribution functions; standard univariate distributions and their statistics; multivariate distributions: joint distributions, marginals, independent random variables, covariance, correlation, and the multivariate normal distribution; introduction to statistical inference: point estimation and confidence intervals. Open source software, such as R or python, will be used for statistical computations. Prerequisite Course(s): Prerequisites: MATH 220 or CSCI 270 Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3