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. Precludes additional credit for STAT 3509 or STAT 3559. Prerequisite(s): STAT 3210. Lectures three hours a week, laboratory one hour a week.
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. Precludes additional credit for STAT 3509 or STAT 3559. Prerequisite(s): STAT 3210. Lectures three hours a week, laboratory one hour a week.
