Foundations of Probability and Statistics

Coursera MOOC / Non-credit USD 49
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Foundations of Probability and Statistics

About this course

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.

What you'll learn

  • quantify uncertainty
  • work with random variables
  • apply the Central Limit Theorem
  • model dynamic systems
  • analyze long-term behavior
  • apply Monte Carlo methods
  • construct and evaluate statistical estimators
  • interpret confidence intervals

Course objectives

  • build a strong mathematical foundation in probability and statistics
  • gain analytical skills for data-driven decisions
  • develop expertise in statistical estimation

Skills you'll gain

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