Foundations of Probability and Statistics Specialization
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 AI. You’ll start by mastering core probability concepts, including random variables, quantifying uncertainty, and the Central Limit Theorem. You’ll then learn to use discrete-time Markov chains to model dynamic systems, analyze long-term behavior, and apply Monte Carlo sampling techniques. Finally, you’ll develop practical skills in statistical estimation, including maximum likelihood, method of moments, and interpreting confidence intervals. By the end, you’ll be equipped to make data-driven decisions and model real-world processes for advanced AI applications.
In this specialization, you will learn how to:
- Explain core probability concepts and their role in statistical analysis and data science
- Analyze and model stochastic systems using discrete-time Markov chains and assess long-term behavior
- Apply Monte Carlo simulation techniques to generate samples from complex probability distributions
- Construct, evaluate, and compare statistical estimators using maximum likelihood and method of moments approaches
Courses
- Probability Foundations for Data Science and AI
- Discrete-Time Markov Chains and Monte Carlo Methods
- Statistical Estimation for Data Science and AI
This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Artificial Intelligence (MS-AI) degree offered on the Coursera platform. Learn more about the MS-AI program.