Introduction to Probability and Statistics

1 credits · Graduate School

Practical foundation in probability and statistics for data science applications. Probability theory, joint, conditional, and marginal probability, Baye’s rule, random variables, and probability distributions. Emphasis on developing statistical intuition and applying methods to real-world data in preparation for graduate-level analytics and machine learning courses. Only open to students enrolled in the MSDS program. [1]