MATH270 — Probability and Statistical Models
5 CR Provides a rigorous introduction to the fundamental principles of probability with emphasis on applications to data-driven problem solving. Starting from an axiomatic definition of probability, students learn how to work with both discrete and continuous random variables and apply these concepts to practical situations. Topics include: conditional probability, Bayes’ theorem; Bernoulli, binomial, geometric, Poisson, uniform (discrete and continuous), normal, and exponential distributions; the law of large numbers; the central limit theorem and its applications; confidence intervals; and the Z-test. A portion of coursework will include techniques and examples in the Python programming language. Recommended: MATH 153 , CS 310 or familiarity with Python Course
Prerequisites: MATH152