Data Science: Featured Courses

B.S. in Data Science

CS 3345: Data Structures and Introduction to Algorithmic Analysis	Students will study the Analysis of algorithms including time complexity and Big-O notation. They will also learn about stacks, queues, and trees, including B-trees.
CS 4347: Database Systems	This course emphasizes the concepts and structures necessary for the design and implementation of database management systems. Topics include data models, data normalization, data description languages, query facilities, file organization, index organization, file security, data integrity, and reliability.

M.S. in Mathematics

STAT 6340: Statistical and Machine Learning	Students will learn about Statistical models, including linear models, generalized linear models, spline models, and additive models.
MATH 6340: Numerical Linear Algebra	Topics include direct and iterative methods for solving linear systems, vector and matrix norms, condition numbers, least squares problems, orthogonalization, and more. Students will be supplemented with programming assignments.

M.S. in Statistics

STAT 7330: Bayesian Data Analysis	Students will study Bayesian modeling fundamentals, prior distributions, large-sample theory and connection with classical inference, model checking and evaluation, Markov chain Monte Carlo methods (including Gibbs), Metropolis, and related algorithms.
STAT 7334: Nonparametric and Robust Statistical Methods	Students will learn about order statistics, ranks, and related distribution theory. They will also explore concepts of sign, signed rank, and permutation statistics. Students will be solving problems involving U-statistics, L-statistics, M-statistics, R-statistics, One- and multi-sample location and scale problems.