In this three-course Specialization, you’ll build a strong mathematical foundation in probability, statistics, and basic stochastic processes, with direct applications to data science and artificial intelligence. You’ll begin by mastering the fundamentals of probability, learning to quantify uncertainty, work with random variables, and apply the Central Limit Theorem. Next, you’ll explore discrete-time Markov chains, discovering how to model dynamic systems, analyze long-term behavior, and apply Monte Carlo methods to sample from complex distributions. Finally, you’ll develop expertise in statistical estimation, learning to construct and evaluate estimators, apply maximum likelihood and method of moments estimation, and interpret confidence intervals. By the end of the specialization, you’ll have the analytical skills to make data-driven decisions, model real-world phenomena, and support advanced AI applications.
Applied Learning Project
Throughout this specialization, learners will apply concepts through hands-on programming exercises in R and Python using Jupyter Notebooks designed to reinforce mastery and track your progress. In addition, learners will test their knowledge by completing benchmark quizzes throughout the courses.
