A. Topics in the application of mathematical models appropriate for teachers of grades P-9.

A. Topics in geometry appropriate for teachers of grades P-9.

A. Topics vary with offering. May be retaken with advisor approval, provided the topics are different. Credit towards degree requirements will depend on the course content.

I. Cross-listed as STA 720. Descriptive statistics, discrete and continuous probability distributions for one and two variables, functions of random variables, sampling distributions, expectations and generating functions. Credit will not be awarded to students who have credit for STA 720. It is strongly recommended that students have completed eight hours of calculus.

A. Algebra and geometry of vectors; vector functions of a single variable; line, surface, and volume integrals; divergence Theorem, Stokes¿ Theorem, Green¿s Theorem; generalized orthogonal coordinates; Fourier Series; solutions to boundary value problems. It is strongly recommended that students have completed twelve hours of calculus.

(3) A. Classical cryptosystems, basic number theory, DES, Advanced Encryption Standard, RSA, discrete logs, digital signatures, elliptic curve cryptosystem, lattice methods. It is strongly recommended that students have completed a course in proof writing.

A. Wave, heat/diffusion and potential/Laplace equations, seperation of variables, orthogonal sets of functions. Fourier series, boundry value problems, Fourier integrals, maximum principles, the Cauchy problem. It is strongly recommended that students have completed a course in differential equations.

A. Continuity, differentiation, integration, series, residues, and applications to the evaluation of real integrals. Applications of conformal mappings to boundary value problems in heat, electrostatic potential, and fluid flow. Emphasis throughout on computational techniques and applications. Credit will not be awarded to students who have credit for MAT 850. It is strongly recommended that students have completed twelve hours of calculus or eight hours of calculus plus a differential equations course.

(3) A. Prerequisites: Senior standing; MAT 301, or both MAT 214 and departmental approval. Introduction to basic concepts of coding theory, linear codes, perfect codes, cyclic codes, BCH codes, and Reed Solomon codes. Additional topics as time permits. It is strongly recommended that students have completed a course in linear algebra and a course in proof writing.

A. Prerequisite: admission to the MAT program or departmental approval. Numeric and geometric concepts; problem solving with numbers, geometry, and data; reasoning; and connections. Credit does not apply toward the M.S. degree offered within this department. Credit will not be awarded to students who have credit for MAT 202.

A. Basic concepts from analytic and algebraic number theory including the Prime Number Theorem, Dirichlet¿s Theorem, the Riemann Hypothesis, algebraic integers, ideals and factorization in algebraic number fields. Additional topics as time permits. It is strongly recommended that students have completed courses in number theory, abstract algebra, and real analysis or differential equations.

A. Prerequisite: departmental approval. May be retaken with approval to a maximum of three credits. Employment with faculty and field supervision in an area related to the student¿s academic interests. A minimum of eighty hours of employment is required for each academic credit.

(3) A. Uniqueness and existence of solutions of initial value problems, maximal intervals of existence, continuous dependence, disconjugacy of boundary value problems, Cauchy functions, Green’s functions, and fixed point theory. Additional topics as time permits. It is strongly recommended that students have completed a course in analysis.

A. Dynamical systems, linear and nonlinear systems theory, transform methods, integral equations, control theory and optimization, calculus of variations, eigenvalue problems, stability theory, bifurcation. It is strongly recommended that students have completed a course in differential equations.

(3) A. Vector spaces, LU decomposition, singular value decomposition, orthogonality, and related theory, with applications to least squares, Markov chains, combinatorics, differential equations, and other topics. It is strongly recommended that students have completed a course in linear algebra.

(3) A. Combinatorial optimization, linear programming, flow and matching theory, traveling salesman problem, and related topics. It is strongly recommended that students have completed a linear algebra course.

A. Computer arithmetic. Analysis of errors and stability of well-posed problems. LaGrange, Hermite and spline interpolation. Newton-Cotes, Romberg, and Gaussian quadrature. Consistency, convergence, and stability of numerical integration methods for ordinary initial value problems. Finite difference and shooting methods for two-point boundary value problems. It is strongly recommended that students have completed a real analysis course and have experience with a programming language.

A. Advanced topics in Mathematics. May be retaken to a maximum of six hours, provided the topics are different. Credit towards degree requirements will depend on the course content.

A. Prerequisites: An 800-level course and departmental approval. Independent study on a problem chosen by the student and instructor. Student must have the independent study proposal form approved by faculty supervisor and department chair prior to enrollment. May be retaken to a maximum of nine hours, provided the topics are different.

(3) A. Prerequisite: completion of at least 15 hours toward the M.A. in Applied Mathematics degree. Preparation for mathematical and statistical study. Guided one-on-one study of a mathematical or statistical concept. Use of mathematical typesetting software, presentation software, and research databases.

A.

A. Topics vary with offering. May be retaken with advisor approval, provided the topics are different. Credit towards degree requirements will depend on the course content.

A. Cross-listed as MAT 720. Descriptive statistics, discrete and continuous probability distributions for one and two variables, functions of random variables, sampling distributions, expectations and generating functions. Credit will not be awarded to students who have credit for MAT 720. It is strongly recommended that students have completed eight hours of calculus.

heA. Prerequisite: MAT 520, 720, STA 520 or STA 720. A continuation of STA 720. Estimation theory, hypothesis testing, linear regression, analysis of variance, and allied topics. It is strongly recommended that students have completed a course in linear algebra.

(3) A. Analysis of six sigma techniques, statistical analysis of process capability, statistical process control using control charts, quality improvement, acceptance sampling, and an introduction to product reliability. It is strongly recommended that students have completed a course in calculus and STA 700, 721, or two courses in applied statistics.

(3) A. Data set manipulation, application of statistical techniques in SAS, data visualization, and statistical programming. It is strongly recommended that students have completed a course in applied statistics.

A. Cross-listed as DSC 780. Data set manipulation, application, of statistical techniques in R, statistical programming, data visualization, and data mining skills. It is strongly recommended that students have completed a course in applied statistics and an introductory course in computer programming. Credit will not be awarded to students who have credit for DSC 780.

A. Completely randomized designs, factorial experiments, multiple comparisons, model diagnosis, randomized blocks, Latin squares, fixed and random models,nested-factorial experiments, 2f factorial experiments, and split-plot designs. Emphasis on applications and use of statistical software. It is strongly recommended that students have completed a course in applied statistics.

A. Data collection, descriptive statistics, basic probability, confidence intervals, hypothesis testing, linear regression, chi-square tests, analysis of variance, and use of statistical software. Credit does not apply toward the Concentration in Applied Mathematics and Statistics or the Concentration in Data Science and Statistics under the M.A. in Applied Mathematics. Credit will not be awarded for STA 700 and STA 800.

(3) A. Prerequisite: Use of matrix algebra to develop theory of linear models. General linear models, estimability, multivariate normal distribution, estimation, testing, prediction, restricted models, models with general covariance structure, reparameterization, multi-part model, and random and mixed models. It is strongly recommended that students have completed a course in applied statistics and a course in linear algebra.

A. Prerequisite: departmental approval. May be retaken with approval to a maximum of three credits. Employment with faculty and field supervision in an area related to the student's academic interests. A minimum of eighty hours of employment is required for each academic credit.

(3) A. Prerequisite: Analysis of variance and simple linear regression review, multiple linear regression, multivariate analysis of variance, multivariate analysis of covariance, repeated measures ANOVA, discriminant analysis, factor analysis, principal component analysis, and use of statistical software. It is strongly recommended that students have completed courses in applied statistics.

A. Advanced topics in Statistics. May be retaken to a maximum of six hours provided the topics are different. Credit towards degree requirements will depend on the course content.

A. Prerequisite: departmental approval. Independent study on a problem chosen by the student and instructor. Student must have the independent study proposal form and course syllabus approved by faculty supervisor and department chair prior to enrollment. May be retaken to a maximum of nine hours, provided the topics are different.