MATH 5111 - Probability for Data Analytics

3 credit hours
This course introduces students to fundamental concepts and theorems in probability theory. Students will learn basic methods of counting, including permutations, combinations, and multinomial coefficients. Students will develop a deep understanding of probability, including conditional probabilities and Bayes’ Theorem. They will learn about probability distributions, highlighting many distributions that appear frequently in real-world problems, including the normal, binomial, Poisson, and exponential distributions. They will additionally learn mathematical tools for working with and describing probability distributions in general, including deriving expectations, variances, and covariances. Students will be introduced to the Central Limit Theorem.

Registration Restriction(s): MSBA majors only