Statistics for Data Science with R

Catalog description

This course covers statistics topics that are used in data science and introduces coding in R, a programming language for statistical computing. Descriptive Statistics: descriptive measures, probability concepts, Bayes theorem, discrete random variables, normal distribution, Poisson distribution. Inferential Statistics: sampling distributions, p-values, confidence intervals, hypothesis testing, Chi-square procedures, correlation, linear regression, ANOVA. Data Science: modeling, exploratory data analysis, categorical variables, non-parametric statistics, basic data visualizations, multi-variable linear regressions, logistical regressions. Prior coding experience is recommended, but not required, however students should expect to code their statistical computations in R. Section information text: Cross-Listed with DATA 215