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The master’s program in computational and applied mathematics offered by the Department of Mathematics and Statistics is designed to produce applied mathematicians and statisticians who can meet the growing demand for analytical and computational skills in traditional scientific and multi-disciplinary fields. Students in the program can choose to pursue an option in either applied mathematics, statistics, or biostatistics.
ODU is one of the few institutions around the world that offers a Computational and Applied Mathematics graduate program, rather than a traditional mathematics program.
Coursework for the M.S. degree contains a significant component in Big Data Analytics
Distinguished by its connections to Modeling and Simulation research throughout the campus
Graduates from this major have gone to work at
An introductory course on machine learning. Machine Learning is the science of discovering pattern and structure and making predictions in data sets. It lies at the interface of mathematics, statistics and computer science. The course gives an elementary summary of modern machine learning tools. Topics include regression, decision trees, artificial neural networks, genetic algorithms, clustering, dimension-reduction, learning sets of rules, support vector machines, hidden Markov models, and Bayesian learning. The course will also discuss applications of machine learning that include data mining, bioinformatics, speech recognition, and text and web data processing. Students enrolled are expected to have some ability to write computer programs, some knowledge of probability, statistics and linear algebra. Prerequisites: MATH 312, MATH 316, and STAT 330 or STAT 331.
The statistical perspective of data mining is emphasized for the majority of the course. Both applied aspects |programming, problem solving, and data analysis) and theoretical concepts |learning, understanding, and evaluating methodologies) of data mining will be covered. Topics may include data preparation, data cleaning, exploratory data analysis, statistical estimation and prediction, regression modeling, clustering, classification and regression trees. An elementary knowledge of concepts in probability and statistics and basic computing proficiency are required. Prerequisites: A grade of C or better in STAT 331 and STAT 405.
An introduction to probability. Topics include axiomatic foundations of probability, conditional probability, Bayes formula, random variables, density and mass functions, stochastic independence, expectation, moment generating functions, transformations, common families of distributions, multiple random variables, covariance and correlation, multivariate distributions, convergence concepts, law of large numbers, limit theorems. Prerequisites: A grade of C or better in STAT 531.
Students entering the Master of Science program in Computational & Applied Mathematics should meet the minimum university admission requirements (Graduate Admission)
The M.S. candidate must complete a minimum of 33 normal credit hours of course work designed to fulfill an option in either applied mathematics, statistics or biostatistics. The M.S. candidate will be assigned to a faculty advisor for a master’s project. The master’s project is designed not only to broaden students’ analytical competency but also to enhance students’ writing and reporting skills on a technical subject.
Estimated rates for the 2021-22 academic year. Rates are subject to change. Anyone that is not a current Virginia resident will be charged non-resident rates. That includes international students.
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2107 ENGR & COMP SCI BLDG, NORFOLK, VA, 23529
1000 Rollins Hall, Norfolk, VA 23529
2101 Dragas Hall, Norfolk, VA 23529
With over 120 undergraduate programs, 130 graduate programs at the master's, education specialist and doctoral levels plus a wealth of certificate and professional development programs, Old Dominion University has the program for you. Visit our academic program listing page to see other programs ODU has to offer.