This course provides a solid foundation in probability theory, tailored for data science applications. Participants will explore fundamental concepts such as probability rules, random variables, and probability distributions. The course delves into key topics like Bayes’ Theorem, Bayesian inference, and moment generating functions (MGFs), along with advanced concepts such as Markov chains and stochastic processes. Learners will also gain an understanding of the Central Limit Theorem and its implications for data analysis. This course is essential for data science professionals looking to strengthen their understanding of probability and its role in data-driven decision-making.