Fundamental Probability and Statistics for Data Science

Catalog description

This is a prerequisite course for the Master’s program in Data Science who do not have probability and statistics background. This course covers basic concepts in probability theory and illustrates its applications to computer science. The course covers probability spaces, random variables, distributions and density functions, expectations, sampling, limit theorems, statistical inference and hypothesis testing, as well as additional topics such as large deviations, client-server system and Markov chains, as they apply to computing.