| DSST 189 | Introduction to Statistical Modeling | Topics will include exploratory data analysis, correlation, linear and multiple regression, design of experiments, basic probability, the normal distribution, sampling distributions, estimation, hypothesis testing and randomization approach to infere... |
| DSST 289 | Introduction to Data Science | Multiple linear regression, logistic regression, ANOVA and other modeling based topics. Exploratory graphical methods, model selection and model checking techniques will be emphasized with extensive use a statistical programming language (R) for data... |
| DSST 329 | Probability | Introduction to the theory, methods, and applications of randomness and random processes. Probability concepts, independence, random variables, expectation, discrete and continuous probability distributions, moment-generating functions, simulation, j... |
| DSST 330 | Mathematical Statistics | Introduction to basic principles and procedures for statistical estimation and model fitting. Parameter estimation, likelihood methods, unbiasedness, sufficiency, confidence regions, Bayesian inference, significance testing, likelihood ratio tests, l... |
| DSST 389 | Advanced Data Science | Computational statistics and statistical algorithms for building predictive models from large data sets. Topics include model complexity, hyper-parameter tuning, over- and under-fitting, and the evaluation of predictive performance. Models covered in... |
| DSST 390 | Directed Independent Study | Topics independently pursued under supervision of faculty member. |
| DSST 395 | Special Topics in Data Science & Statistics | Selected topics in data science and statistics. |