Description
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
- Prerequisite(s): STAT 2210 .
Conditions and arrangements
- Precludes additional credit for Precludes additional credit for STAT 3509 or STAT 3559 .
- Prerequisite(s): STAT 2210 .
- Lectures three hours a week, laboratory one hour a week.
Reference text in its original language
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.
- Prerequisite(s): STAT 2210 .
- Precludes additional credit for Precludes additional credit for STAT 3509 or STAT 3559 .
- Lectures three hours a week, laboratory one hour a week.
Sources and references
Dates and sources are retained to help you verify the information. Translations are provided to facilitate reading; the official source governs conditions and requirements.
Source reference : https://calendar.carleton.ca/undergrad/courses/STAT/