STSCI 4520
Statistical Computing
This course is designed to provide students with an introduction to statistical computing. The class will cover the basics of programming; numerical methods for optimization and linear algebra and their application to statistical estimation, generating random variables, bootstrap, jackknife and permutation methods, Markov Chain Monte Carlo methods, Bayesian inference and computing with latent variables.
Topics
- Students will be able to enter, manipulate and plot data and run basic statistical analyses in R.
- Students will be able to implement estimators for non-standard statistical problems in R.
- Students will be able to simulate random variables and random experiments in R.
- Students will be able to design and implement Monte Carlo methods to evaluate integrals and perform simulations.
- Students will be able to design and conduct appropriate resampling methods to estimate sampling variance for statistical estimates.
Official roster learning outcomes
Prerequisites & course information
STSCI 3080 or MATH 4710 or equivalent.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Statistical Computing · 4.0 · Undergraduate