STAT 4321
Inferential Data Science Foundations II
Inferential data science tools extending to big data using open-source software. Asymptotic properties of likelihoods, parametric and non-parametric approaches, bootstrap, jackknife estimation, frequentist and Bayesian perspectives. Formal tools are developed. Concepts are demonstrated using simulation. Abstract concepts are made concrete through visualization and numerical computation.
Prerequisites & course information
Precludes additional credit for STAT 3509 or STAT 3559 . Prerequisite(s): STAT 2210 . Lectures three hours a week, laboratory one hour a week.
Sources
- Course catalogue ↗2026–2027