This course is an introduction to computational statistics through numerical methods and computationally intensive methods for statistical problems. Topics include statistical graphics, root finding, simulation, randomization testing, and bootstrapping. Covers intermediate to advanced programming with R.
This course considers the impacts that data collected today have upon individuals and society. The rapid increase in the scale and types of data collected has impacted commerce and society in new ways.
This course is a survey of computational methods that are especially useful for statistical analysis, with implementations in statistical package R. Topics include matrix analysis, multivariate regression, principal component analysis, multivariate analysis, and deterministic optimization methods.
This course is a survey of Monte Carlo methods and numerical integration. Importance and rejection sampling. Sequential importance sampling. Markov chain Monte Carlo (MCMC) sampling techniques, with emphasis on Gibbs samplers and Metropolis/Hastings.
This course is designed for social sciences graduate students and advanced undergraduate students seeking training in data issues and methods employed in social sciences.
The Bruin Learn course page is here.
This course is a introduction to the analysis of social structure, conceived in terms of social relationships. Major concepts of social network theory and mathematical representation of social concepts such as role and position.