28 courses with the subject STT, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
STT 210Introduction to Statistics with Applications in the Health Sciences3
Topics include descriptive statistics, probability, probability distributions, sampling distributions, confidence intervals, hypothesis testing, correlation and regression. Instruction tailored to applications in the health sciences.
Methods of data collection; numerical and graphical analyses of univariate and bivariate data; axioms of probability; conditional probability; independence; distributions of random variables; introduction to confidence intervals and hypothesis testing; simple linear regression and correlation; use of statistical software to analyze data and simulate random variables.
An introduction to descriptive and inferential statistics with practical applications and an introduction to probability including both discrete and continuous models. Topics include graphical and numerical methods of descriptive statistics, counting techniques, discrete and continuous probability distribution, joint distribution, sampling distribution, confidence intervals, hypothesis testing, and simple linear regression.
Introduction to data science with an overview of the basic techniques for making informed, data-driven decisions using the R statistical software. Students learn the basics of R programming and use it to visualize, wrangle, manipulate, and explore data of many types and sizes.
Introduction to statistical programming in SAS with emphasis on data step programming; applications to data management and report generation, simulation, graphical and numerical analysis of univariate and multivariate data. Comparison of current statistical software.
Introduction to calculus-based probability. Topics include: discrete and continuous random variables and probability distributions; mathematical expectation and variance; sampling distributions and the central limit theorem.
Elementary survey sampling on the design and analysis of sample surveys. Topics include design of questionnaires; methods of data collection; sample-survey designs including simple random sampling, stratified sampling, cluster sampling, and systematic sampling; ratio and regression estimation; two-stage cluster sampling; sampling from wildlife populations; sources of errors.
A survey of statistical methods. Topics include review of exploratory data analysis and hypothesis testing, analysis of variance, linear regression, analysis of contingency tables and categorical data analysis, and non-parametric methods.
STT 411Design of Experiments and Analysis of Variance3
Review of elementary statistics; design of experiments including completely randomized, randomized block, factorial, split-plot, and repeated measures designs; analysis of variance; non-parametric alternative methods of analysis. Statistical software packages will be used as appropriate in problem solving.
Topics include point and interval estimators and their properties, methods of moments, maximum likelihood estimation, likelihood ratio test, and elements of linear model theory and their extensions.
Review of statistical methods used in epidemiologic studies and clinical trials. Topics include measures of association, logistic regression, covariates, life tables and Cox regression. Statistical analysis using a statistical software.
Topics include time series plot, exponential smoothing, decomposition of data into trend, seasonality, and random error, autocorrelation and the correlogram, stationary models: purely random process, AR, MA, ARMA models, non-stationary models: random walks, ARIMA models.
Introduction to the analysis of qualitative data. Basic methods of summary and inference for two- and three-way contingency tables; introduction to the generalized linear model for binary and Poisson data; focus on multinomial responses (nominal and ordinal) and matched pairs data; statistical analysis using SAS.
Theory and methods of non-parametric statistics in the one- and two-sample problems and their comparisons with standard parametric procedures. Non-parametric tests for comparing more than two samples; tests of randomness and independence.
Matrix manipulations; multivariate normal distribution; inference for mean vector and covariance matrix; multivariate analysis of variance; principal components; canonical correlations; discriminant analysis; factor analysis; cluster analysis; statistical analysis using SAS.
Theoretical introduction to the general linear model and its application to simple linear regression and multiple regression. Estimation and hypothesis testing of model coefficients; residual analysis; analysis of covariance.
Introduction to fundamental principles and applications of machine learning techniques. Topics include linear regression, classification, re-sampling methods, model selection, tree-based classification methods, and Support Vector Machines (SVM). Students learn how to analyze large/high-dimensional real-world application data to build effective machine learning systems using standard programming tools.
STT 455Statistical AI and Large Language Models (LLMs)
Explores applied and conceptual Artificial Intelligence (AI) and Large Language Models (LLMs), grounded in statistical foundations. Covers core concepts including transformers and benchmarked deep learning architectures. Provides practical experience with real-world applications through hands-on projects using AI tools and pre-trained models, bridging theory with practice in modern AI.
Exploration of the synergy between deep learning and statistical learning principles emphasizing the integration of statistical foundations in the context of computer vision and pattern recognition applications, ensuring robust and interpretable outcomes. Provides practical experience in modeling high-dimensional real-world data, developing solutions that are predictive and grounded in statistical rigor.
The formulation, analysis and interpretation of probabilistic models. Selected topics in probability theory. Conditioning, Markov chains, and Poisson processes. Additional topics chosen from renewal theory, queuing theory, Gaussian processes, Brownian motion, and elementary stochastic differential equations.
STT 470Applied High Performance Computing (HPC) for Statistical Learning3
Introduction to the foundational principles and techniques of high-performance computing (HPC) and its applications with statistical learning using large-scale real-world data. Includes parallel architectures, distributed computing, big data frameworks, and practical techniques essential for analyzing massive real-world applications.
Review of case studies involving consulting with clients on statistical design of experiments and analysis of experimental and observational data; analysis of available data sets utilizing statistical software; presentation of oral and written reports on a consulting experience.
Involves investigation under faculty supervision beyond what is offered in existing courses. For further information, consult the Enrichment Courses and Programs section in this catalogue.
Academic training and practical experience through work in a private company or public agency; presentation of oral report on internship experience. Faculty supervision and evaluation of all study and on-site activity.
