Undergraduate Course Descriptions
Note: Many of the courses listed here may be taken by graduate non-statistics majors for graduate credit. For more information, refer to the "Statistics Electives" page.
1301 Statistical Reasoning: A Guide to the Unknown
Prerequisite(s): Freshman standing and consent of statistics undergraduate faculty advisor.
Philosophical, ethical, and sociological issues related to statistical uncertainty and randomness.
1380 Elementary Statistics
Introduction to traditional statistical concepts including descriptive statistics, binomial and normal probability models, tests of hypotheses, linear correlation and regression, two-way contingency tables, and one-way analysis of variance. Credit may not be obtained after receiving credit in STA 2381 or 3381.
1V9R Research (0 to 3 sem. hrs.)
Pre-requisite(s): Consent of the instructor.
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.
2300 Introduction to Data Science (Cross-listed as CSI 2300)
Principles of data science, including problem workflow, variable types, visualization, modeling, programming, data management and cleaning, reproducibility, and big data.
2381 Introductory Statistical Methods
Prerequisite(s): A grade of C or above in MTH 1321.
Parametric statistical methods. Topics range from descriptive statistics through regression and one-way analysis of variance. Applications are typically from biology and medicine. Computer data analysis is required.
2450 Introduction to Computing for the Mathematical and Statistical Sciences
Computer programming for mathematical scientists with emphasis on designing algorithms, problem solving, and coding practices. Topics include development of programs from specifications; appropriate use of data types; functions; modular program organization; documentation and style; and version control and collaborative programming.
2V9R Research (0 to 3 sem. hrs.)
Pre-requisite(s): Consent of the instructor.
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.
3375 Technologies for Sports Analytics
Pre-requisite(s): STA 2300 and STA 2450
Concepts in big data analytics primarily applied to topics in sports focusing on graphical methods through dashboards and inferential methods.
3381 Probability and Statistics
Prerequisite(s): A grade of C or above in MTH 1322.
Introduction to the fundamentals of probability, random variables, discrete and continuous probability distributions, expectations, sampling distributions, topics of statistical inference such as confidence intervals, tests of hypotheses, and regression.
3386 Regression Analysis
Pre-requisite(s): STA 3381, MTH 2311 and MTH 2321.
A development of regression techniques including simple linear regression, multiple regression, logistic regression and Poisson regression with emphasis on model assumptions, parameter estimation, variable selection and diagnostics.
3V90 Undergraduate Research in Statistics (1 to 3 sem. hrs.)
Pre-requisite(s): Consent of instructor.
Independent study or research in topics not available in other courses. Maximum of four hours will count toward the degree.
3V9R Research (0 to 3 sem. hrs.)
Pre-requisite(s): Consent of the instructor.
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.
4330 SAS Programming for Statistical Science
Pre-requisite(s): STA 2381 or 3381.
Concepts in SAS programming including methods to establish and transform SAS data sets, perform statistical analyses, and create general customized reports. Methods from both BASE SAS and SAS SQL will be considered.
4350 Statistical Machine Learning
Pre-requisite(s): STA 3386
Fundamental topics of machine learning including supervised/unsupervised learning, cost function optimization, feature selection and engineering, and bias/variance tradeoff. Learning algorithms including classification methods, support vector machines, decision trees, neural networks, and deep learning are included.
4360 Bayesian Data Analysis
Pre-requisite(s): STA 4385
An introduction to Bayesian inference emphasizing prior and posterior distributions, estimation, prediction, hierarchical Bayesian analysis, and applications with computer implemented data analysis.
4362 Applied Time Series Analysis
Pre-requisite(s): STA 3386.
Statistical methods of analyzing time series. Model identification, estimation, forecasting, and spectral analysis will be discussed. Applications in a variety of areas including economics and environmental science will be considered.
4370 Sampling Techniques
Prerequisite(s): Three hours of statistical methods.
Planning, execution, and analysis of sampling from finite populations. Simple random, stratified random, ratio, systematic, cluster, sub sampling, regression estimates, and multi-frame techniques are covered.
4371 Data Management and Mining
Prerequisite(s): STA 3381.
Terminology, techniques, and management of Data Mining for biostatisticians.
4372 Introduction to Biostatistics
Prerequisite(s): STA 2381 or STA 3381 or consent of the instructor.
Data Analysis for biostatisticians in the biomedical and pharmaceutical fields.
4373 Computational Methods in Statistics
Prerequisite(s): STA 2381 or STA 3381 or consent of the instructor.
Computational methods using statistical packages and programming.
4374 Statistical Process Control
Prerequisite(s): STA 3381 or equivalent.
Development of statistical concepts and theory underlying procedures used in statistical process control applications and reliability.
4382 Intermediate Statistical Methods
Prerequisite(s): A minimum grade of C in either STA 2381 or STA 3381 and a minimum grade of C in STA 3386.
Development and application of two-sample inferences, analysis of variance, multiple comparison procedures, and nonparametric methods.
4384 Applied Multivariate Methods
Pre-requisite(s): STA 3386.
Numerical and graphical descriptive statistics for multivariate data, principal components and factor analysis, canonical correlation, discriminant analysis, multivariate analysis of variance, multidimensional contingency tables, and cluster analysis.
4385 Mathematical Statistics I
Prerequisite(s): MTH 2321 with minimum grade of C.
Introductions to the fundamentals of probability theory, random variables and their distributions, expectations, transformations of random variables, moment generating functions, special discrete and continuous distributions, multivariate distributions, order statistics, and sampling distributions.
4386 Mathematical Statistics II
Prerequisite(s): STA 4385 with minimum grade of C.
Theory of statistical estimation and hypothesis testing. Topics include point and interval estimation, properties of estimators, properties of test of hypotheses including most powerful and likelihood ratios tests, and decision theory including Bayes and minimax criteria.
4387 Introduction to Probability Methods
Prerequisite(s): STA 4385 with minimum grade of C.
Applications of probability theory to the study of phenomena in such fields as engineering, management science, social and physical sciences, and operations research. Topics include Markov chains, branching processes, Poisson processes, exponential models, and continuous-time Markov chains with applications to queuing systems. Other topics introduced are renewal theory and estimation procedures.
43C8 Capstone in Sports Analytics
Pre-requisite(s): Senior standing and consent of the instructor
Applying statistics data science methodology to research problems in sports analytics.
43C9 Capstone Statistics Course
Prerequisite(s): Approval of the statistics undergraduate faculty advisor.
Statistical concepts applied to written and oral reports for consulting. For students majoring in statistics.
4V90 Special Topics in Statistics (1 to 3 sem. hrs.)
Prerequisite(s): STA 2381 or STA 3381.
Topics in probability and/or statistics not covered in other courses. May be repeated for a maximum of 6 hours if the content is different.
4V9R Research (0 to 3 sem. hrs.)
Pre-requisite(s): Consent of the instructor.
Undergraduate research undertaken with the supervision of a faculty member. May be taken for a maximum of 6 hours.