48 courses with the subject STA, 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.
Introduction to statistical literacy and the role of statistics in settin such as elections, medicine, sports, and the sciences. Topics vary by instructor. (QR)
Data collection and visualization, exploratory analysis, introductory probability, inference techniques for one variable, and statistical literacy. (D, QR, QDA)
A foundational course in regression and data science. The course introduces data analysis through statistical computing in R, least-squares and logistic regression, model selection, and data visualization. P-STA 111, a score of 4 or 5 on the AP Statistics exam, or prerequisite override required. (D, QR, QDA)
Seminar designed for students who wish to participate in statistics and/or data analysis competitions. Not to be counted toward any major or minor offered by the department. May be repeated for credit. Pass/Fail only.
A course in statistical models, emphasizing models for count and categorical data, an exploration of likelihood-based modeling, and an introduction to mixed effects models. These models are applied using R. P-STA 112 and MTH 111. (QR)
Experimental designs, observational studies, survey design and estimation with stratified, cluster, and other sampling schemes. P- STA 112 or prerequisite override required.
A course on a range of computing tools for statistics and data science. Topics may include data wrangling and cleaning, fundamentals of relational data, web scraping, working with data types such as text and location data, and core computing concepts such as functions, iteration, and simulation. P - STA 112.
Topics in statistics not considered in regular courses, or which continue study begun in regular courses. May be repeated for credit if the topic varies. Content and prerequisites vary.
Distributions of discrete and continuous random variables, sampling distributions. Covers much of the material on the syllabus for the first actuarial exam. Also listed as MTH 357. P-MTH 112 or prerequisite override required.
To declare the certificate in actuarial science, a student must receive at least a C or AP credit in each of ACC 111, ECN 150, and MTH 112. Upon declaration, students are eligible for reserved seats during registration, as determined by each department and by the School of Business. Students are strongly encouraged to take Society of Actuaries (SOA) Exam P shortly after completing STA 310/MTH 357, and SOA Exam FM shortly after completing FIN 231. For more information on these exams, consult the SOA. Students are also strongly encouraged to seek an actuarial internship in the summer after their junior year. Students can apply for Validation by Education Experience (VEE) credit with the SOA after passing at least two actuarial exams.
Theory of estimation and testing in linear models. Topics include least squares and the normal equations, the Gauss-Markov Theorem, testing general linear hypotheses, model selection, and applications. P-MTH 121 or 205, and STA 310 or MTH 357.
A course in fundamental network theory concepts, including measures of network structure, community detection, clustering, and network modelling and inference. Topics also draw from recent advances in the analysis of networks and network data, as well as applications in economics, sociology, biology, computer science, and other areas. Also listed as MTH 359. P-MTH 117 or MTH 121 or MTH 205, and one course in gs STA at the 200 level or above.
Multivariate and linear methods for classification, visualization, discrimination, and analysis of high dimensional data. P-STA 112 and MTH 121 or MTH 205, or prerequisite override required.
An introduction to supervised learning. Topics may include lasso and ridge regression, splines, generalized additive models, random forests, and support vector machines. P-STA 112 and MTH 121 or 205, or prerequisite override required, experience with statistical computing.
Computationally intensive statistical methods. Topics may include simulation, optimization, numerical integration, Monte Carlo methods, and non-parametric methods. Students will make extensive use of statistical software throughout the course. P-One of STA 310 or MTH 357, MTH 121 or MTH 205, and one of the following courses: STA 214, STA 261, STA 362, STA 363, STA 365.
An introduction to Bayesian statistics and computational methods for performing Bayesian data analysis. Topics may include conjugate distributions, objective prior distributions, Bayesian inference, hierarchica models, and Markov chain Monte Carlo methods. P - STA 112 and one of STA 310 or MTH 357. (QR)
Methods and models for time series processes and autocorrelated data. Topics include model diagnostics, ARMA models, spectral methods, computational considerations, and forecasting error. P-STA 112, and either STA 310 or MTH 357, or prerequisite override required.
Topics in statistics not considered in regular courses or which continue study begun in regular courses. May be repeated for credit if the topic varies. Content and prerequisites vary.
Students integrate knowledge acquired throughout their degree program. Topics include developing a research plan, statistical writing, data visualization, and data ethics. Students will communicate statistical results to both technical and non-technical audiences through written reports and oral presentations. Offered fall semester. P-senior STA major.
A capstone course for students in the B.S. in Applied Statistics. Topics include developing a project plan, team work skills, statistical writing, data visualization, and data ethics. Students will communicate statistical results to both technical and non-technical audiences through written reports and oral presentations. P-STA 363 and junior or senior B.S. in Applied Statistics major. Students in the B.S. in Statistics may request entry into this course by contacting the professor, and may be admitted if there is space available.
A course of independent study directed by a faculty advisor. By prearrangement. May be be repeated for credit. P-Prerequisite override required.
Individual external internship in a professional setting, completed under the supervision of a faculty member. The student is responsible for identifying and obtaining the internship. May be repeated for credit. Pass/Fail only. P - Prerequisite override required.
Independent study or research directed by a faculty advisor by prearrangement with the advisor. P-Prerequisite override required. Combined Bulletin 2026-2027 295
Preparation of a paper, followed by an oral presentation based upon work completed in STA 391.
Fundamental concepts and methods for the design, analysis, and interpretation of statistical data. Focuses on the development and application of parametric and nonparametric statistical methods for common data types as well as using computational approaches to conduct, organize, communicate, and present findings.
Distributions of discrete and continuous random variables, sampling distributions. Covers much of the material on the syllabus for the first actuarial exam. This course is cross-listed as MTH 657.
Derivation of point estimators, hypothesis testing, and confidence intervals, using both frequentist and Bayesian approaches. P-STA 610 or MTH 657 or POI.
Theory of estimation and testing in linear models. Topics include least squares and the normal equations, the Gauss-Markov Theorem, testing general linear hypothesis, model selection, and applications. P-STA 610 or MTH 657, or POI.
A course in fundamental network theory concepts, including measures of network structure, community detection, clustering, and network modeling and inference. Topics also draw from recent advances in the analysis of networks and network data, as well as applications in economics, sociology, biology, computer science, and other areas.
Multivariate and generalized linear methods for classification, visualization, discrimination, and analysis.
An introduction to supervised learning. Topics may include lasso and ridge regression, splines, generalized additive models, random forests, and support vector machines. Requires prior experience with R programming.
Computationally intensive statistical methods. Topics may include simulation, optimization, numerical integration, Monte Carlo methods, and nonparametric methods. Students will make extensive use of statistical software throughout the course, P-MTH 657 or STA 610.
An introduction to Bayesian statistics and computational methods for performing Bayesian data analysis. Topics may include conjugate distributions, objective prior distributions, Bayesian inference, hierarchi models, and Markov chain Monte Carlo methods. P - STA 610 and a previous course in regression.
Methods and models for time series processes and autocorrelated data. Topics include model diagnostics, ARMA models, spectral methods, computational considerations, and forecasting error. P-STA 610 or MTH 657, or POI.
Topics in statistics not considered in regular courses or which continue study begun in regular courses. May be repeated for credit if the topic varies. Content and prerequisites vary.
Reading in statistical topics to provide a foundational basis for more advanced study in a particular area. May not be used to satisfy any requirement in the MS degree with thesis. No more than three hours may be applied to the requirements for the MS degree without thesis May be repeated for credit for a total of 3 hours.
A course of independent study directed by a faculty adviser. By prearrangement. May be repeated for credit.
Individual, external internship in a professional setting, completed under the supervision of a faculty member. The student is responsible for identifying and obtaining the internship. May be repeated for credit. Pass/Fail only. Permission of Instructor required.
This course includes the axiomatic foundations of probability theory and an introduction to stochastic processes. Applications may include Markov chains, Markov chain Monte Carlo with Metropolis-Hastings, Gibbs sampling, Brownian motion, and related topics, with an emphasis on modern developments. This course is cross-listed as MTH 757. P-STA 610 or MTH 657 and MTH 611 or POI.
Advanced mathematical treatment of point estimators, hypothesis testings, and confidence intervals, using both frequentist and Bayesian approaches. P-STA 610 or MTH 657, or POI.
Extensions of the classical linear model to cover models for binary and count data, ordinal and nominal categorical data, and time-to-event data, along with numerical maximization techniques needed to fit such models. Additional topics may include longitudinal data, the Expectation- Maximization algorithm, non-linear models, or related topics. P-STA 612 and STA 711 or POI.
Fundamental concepts, theory, and computational methods for Bayesian inference. Topics may include decision theory, evaluating Bayesian estimators, Bayesian testing and credible intervals, Markov chain Monte Carlo methods, and hierarchical models. P-STA 610 or MTH 657, or POI.
An introduction to clinical trials from a statistician’s perspective. Materials will cover the design of clinical trial including power calculations and randomization procedures, the analysis of study outcomes, and the communication of results. Assumes knowledge of R and general linear models. P-STA 612.
Analysis of time-to-event outcomes subject to censoring and truncation. al Topics include nonparametric, semiparametric, and parametric approaches to modeling survival quantities and conducting inference. P- STA 610.
This course provides students with essential skills for conducting causal inference analyses. Students will learn how to implement causal inference methods, evaluate critical assumptions, and perform sensitivity analyses to rigorously assess causal relationships. P-STA 610.
Topics and prerequisites vary by instructor. May be repeated for credit ifthe topic varies.
Research performed under the supervision of the thesis advisor. A research proposal is written and approved by the thesis committee, and an oral presentation is given on the research plan. May berepeated for credit. Satisfactory/Unsatisfactory.
Research performed under the supervision of the thesis advisor, in preparation for a thesis defense. May be repeated for credit. Satisfactory/Unsatisfactory P-satisfactory completion of STA 791.
Source: Wake Forest University's catalog, linked per course · table learning_unit · CourseShelf publish 61
