Classes at North Carolina State University
- ST 442: Introduction to Data Sciences.
Data Science has become increasingly important in nearly every industry
section and academic field. It has gained significant national
attention and interest by combining techniques from several fields like
Computer Science, Statistics, and mathematics to extract knowledge from
data. This course provides an overview of several foundational topics
in Data Science and will expose students to the theory and algorithms
underlying these techniques, as well as the use of common commercial
packages. The class will include a mix of lectures and programming
- ST 515: Experimental Statistics for Engineers
General statistical concepts and techniques useful to research workers
engineering, textiles, wood technology, etc. Probability distributions,
measurement of precision, simple and multiple regression, tests of
significance, analysis of variance,enumeration data and experimental
- ST 740: Bayesian Inference and Analysis.
Introduction to Bayesian inference; specifying prior distributions;
conjugate priors, summarizing posterior information, predictive
distributions, hierachical models, asymptotic consistency and
asymptotic normality. Markov Chain Monte Carlo (MCMC) methods and the
use of exising software.
Classes at George Washington University
- ST 6289.16: Bayesian Computation.
Applied Bayesian methods with special emphasis on Bayesian
computational tools. Bayesian estimation, intervals, inference,
hypothesis testing, linear and generalized linear models.
Classes at Iowa State University
- STAT 101: Principles of Statistics.
concepts in modern society; descriptive statistics and graphical
displays of data; the normal distribution; data collection (sampling
and designing experiments); elementary probability; elements of
statistical inference; estimation and hypothesis testing; linear
regression and correlation; contingency tables.
- LAS 290G: Special Problems/Understanding
Technical Risk: Culture and Analysis.
Freshman seminar exploring social and technical aspects of risk through
a series of case studies including bioterrorism, HIV/AIDS, and nuclear
- STAT 326: Introduction to Business Statistics
regression analysis; regression diagnostics; model building;
applications in analysis of variance and time series; random variables;
distributions; conditional probability; statistical process control
methods; use of computers to visualize and analyze data.
- STAT 544: Bayesian Statistics.
of probability models; subjective, conjugate, and noninformative prior
distributions; hierarchical models; analytical and computational
techniques for obtaining posterior distributions; model checking, model
selection, diagnostics; comparison of Bayesian and traditional methods.