Glossary
UW Bothell Course Descriptions
UW Seattle Course Descriptions
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TSTAT 280 Applied Data Science (5) NSc, RSN
Introduces the data science life cycle, focusing on data acquisition, wrangling, exploration, and visualization, model building, model evaluation, and deployment. Students gain practical experience with modern programming environments, working on projects that simulate real-world workflows to develop actionable insights from data. Prerequisite: one of the following: (1) a minimum grade of 2.0 in TMATH 110 and a minimum grade of 2.0 in either TMATH 124 or MATH 124; (2) a minimum grade of 2.0 in TMATH 390.
TSTAT 345 Statistical Theory I (5)
Introduces the fundamentals of probability for statistics; axioms of probability, combinatorics, conditional probability, independence; discrete and continuous random variables, transformation of random variables, expectation, variance, moment generating functions; laws of large numbers and the central limit theorem. Course overlaps with: TMATH 393; MATH 394/STAT 394; STAT 340; and STMATH 392. Prerequisite: a minimum grade of 2.0 in either TMATH 126, MATH 126, or STMATH 126.
TSTAT 346 Statistical Theory II (5)
Introduces multivariate distributions and transformations; Order statistics; Basic concepts of estimation, hypothesis testing, and confidence intervals; Maximum likelihood estimators; introduction to regression; parameter estimates using Bayesian framework. Course overlaps with: STAT 341. Prerequisite: a minimum grade of 2.0 in TSTAT 345; and a minimum grade of 2.0 in TMATH 224.
TSTAT 380 Foundations of Statistical Consulting (4)
Builds skills to provide ethical and effective consulting services to a client from project beginning to project assessment plan. Includes client interviews, development of statement of work, project timeline, client updates, and providing recommendations and preliminary results to client. Emphasizes ethical practices in statistical consulting. Prerequisite: a minimum grade of 2.0 in either TSTAT 280, TMATH 210, TSTAT 345, TMATH 390, or STMATH 390.
TSTAT 420 Applied Non-Parametric Methods (5)
Introduces fundamental non-parametric statistical methods, focusing on practical application through projects and presentations. Methodologies may include the Sign Test, Wilcoxon tests, Mann-Whitney U Test, Kruskal-Wallis Test, bootstrapping, and non-parametric regression. Course overlaps with: STAT 425/BIOST 425. Prerequisite: a minimum grade of 2.0 in TSTAT 345.
TSTAT 422 Introduction to Design of Experiments (5)
Introduces experimental design methods and appropriate analysis for design data, including completely randomized, blocked (fixed effects and random effects models), Latin Square, split-plot, strip-plot, nested, and repeated measures designs. Analyses of real data to illustrate concepts; use of appropriate statistical models and results interpretation. Discipline-specific writing and communication is taught and emphasized. Course overlaps with: STAT 421. Prerequisite: a minimum grade of 2.0 in either TMATH 210 or TMATH 390.
TSTAT 426 Applied Bayesian Modeling (5)
Introduction to Bayesian inference and modeling. Topics include conjugate priors, posterior sampling, Markov Chain Monte Carlo (MCMC) methods, hierarchical models, and model diagnostics. Emphasis on applications and computational tools, including modern probabilistic programming languages. Students will learn to analyze data using Bayesian methods, interpret posterior distributions, and communicate findings effectively. Prerequisite: a minimum grade of 2.0 in either TSTAT 345 or TMATH 390.
TSTAT 430 Applied Linear Regression (5)
Investigates simple/multiple least-squares linear regression models including inference, interpretation, variable selection, model diagnostics and corrections, ANOVA/ANCOVA, analysis of real data to illustrate concepts using modern statistical software. Discipline-specific writing and communication is taught and emphasized. Course overlaps with: TMATH 410 and STAT 423. Prerequisite: a minimum grade of 2.0 in TSTAT 346; and a minimum grade of 2.0 in TMATH 208.
TSTAT 431 Multivariate Statistical Analysis (5)
Introduces statistical methods for analysis of multidimensional data. Methods include tools for exploratory analysis of high-dimensional data, statistical modeling approaches to parameter estimation and hypothesis testing, and nonparametric methods for classification and clustering. Discipline-specific writing and communication is taught and emphasized. Course overlaps with: STAT 441. Prerequisite: a minimum grade of 2.0 in TSTAT 422.
TSTAT 434 Applied Generalized Linear Models (5)
Introduces the statistical theory and methods to extend regression and analysis of variance to non-normal data, including parameter estimation and goodness-of-fit for generalized linear models (GLMs); application to binary, Poisson, negative binomial, and gamma GLMs; quasi-likelihood estimates and overdispersion. Course overlaps with: STAT 424. Prerequisite: a minimum grade of 2.0 in TSTAT 430.