Probability & Statistics
Master theoretical foundations, asymptotic inference, and scalable computational statistics for quantitative roles.
This intermediate course delivers a comprehensive treatment of probability spaces, random vectors, and inferential statistics. Students advance from calculus-based distribution derivations and asymptotic limit theorems to modern computational workflows, including resampling methods and Bayesian modeling. The curriculum provides the mathematical rigor needed for advanced academic study along with practical data analysis techniques for industry.
Curriculum
Two complete tracks. Study either or both — each has its own exam and certificate.
A mathematically rigorous foundation covering measure-theoretic probability concepts, univariate and multivariate distributions, asymptotic convergence, and classical estimation theory.
Rigorous formulation of probability measures, continuous and discrete random vectors, transformations, and generating functions.
- Sigma-Algebras and Axiomatic Probability Lab40 min
- Transformations of Multivariate Distributions Lab45 min
- Generating Functions and Characteristic Transforms Lab35 min
Careers this prepares you for
- Data Scientist
- Quantitative Analyst
- Machine Learning Research Engineer
- Biostatistician
- Risk Modeling Specialist