STATISTICAL THINKING FOR DATA SCIENCE

Catalog description

This course introduces core ideas in probability and statistics with a focus on building a foundation for data science. Topics include probability theory basics, common probability distributions, sampling and estimation, confidence intervals and hypothesis testing, and simple linear regression, resampling methods, smoothing techniques, and an introduction to the Bayesian inference. The course also offers a brief introduction to programming in R and Python