Choose at least 2 courses from the following recommended list with Graduate Advisor's approval:
CS 5334 Parallel and Concurrent Programming
The study of software and hardware architectures for parallel and concurrent systems, including multiple processes executing in parallel and the programming of distributed systems. Prerequisite: instructor approval.
CS 5350 Advanced Algorithms
A review of mathematical techniques for analysis of computer algorithms, techniques for design of efficient algorithms, description and analysis of both well established and recently developed algorithms. Prerequisite: CS 2402 or instructor approval.
MATH 5330 Computational Methods of Linear Algebra
Numerical methods for large systems of linear algebraic equations, preconditioned iterative methods, sparse direct methods, eigenvalue and generalized eigenvalue problems, and error analysis. Prerequisites: MATH 3323 and a working knowledge of a high-level programming language.
MATH 5343 Numerical Solution of Partial Differential Equations
Introduction to modern numerical methods for the solution of elliptic, parabolic, and hyperbolic partial differential equations. Emphasis is given to finite element methods. Prerequisites: MATH 4329 or its equivalent, and a working knowledge of a high-level programming language.
MATH 5345 Numerical Optimization
A study of numerical algorithms for solving systems of nonlinear equations, unconstrained optimization, and nonlinear least squares problems. Derivation of necessary and sufficient conditions for constrained optimization, and an introduction to interior-point methodology. Prerequisites: MATH 2313, MATH 3323, and a working knowledge of a high-level computer language
STAT 5329 Statistical Programming
Statistical Programming (1-2). Introduction to statistical programming using statistical software packages such as SAS, Python, and R. Emphasis on methods of data entry, data management, and creation of statistical reports. Topics covered include data manipulation, creation of user-defined functions, simulation methods, random variable generation, permutation methods, the bootstrap, the jackknife and methods of increasing computational efficiency.
STAT 5385 Statistics in Research
An introduction to statistical modeling of a univariate response conditional on a test of explanatory variables. Classical formulation of multiple linear regression and analysis of variance. Some discussion of experimental design from power considerations. Selected topics from generalized linear models, nonparametric regression, and quasi-likelihood estimation. Emphasis is on model building, fitting, validation, and subsequent inferences. Analysis of real data using major statistical software packages. Prerequisites: MATH 3323, STAT 4380, or instructor approval.