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A minor in Computer Science is required, so that the student will develop strong programming skills for data analysis
The combination of Applied Mathematics and Statistics develops the ability to analyze data and generate predictive modelin
A senior project is required
Use of SAS and R to handle data sets. Topics for SAS include data input, creating permanent data sets, merging data sets, creating new variables, sorting, printing, charting, formatting, IML programming, macro programming, and an overview of proc SQL and other statistical procedures. Topics for R include data structure, control structure, writing functions, and graphics. Prerequisites: grade of C or better in STAT 130M or equivalent and a grade of C or better in MATH 316 or equivalent or permission of instructor.
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.
This course introduces students to practical applications of big data analytics. Lecture topics include an overview of the various topics in business, engineering, and government currently using big data analytics. Students will choose a project involving a real world application to explore techniques learned during other course work. Course involves written and oral presentations for students to improve communication and teamwork skills. Prerequisites: A grade of C or better in STAT 331 and STAT 405. Pre- or corequisite: BDA 431.
Students entering the Bachelor of Science program in Mathematics, Big Data Analytics should meet the minimum university admission requirements (Undergraduate Admission)
Math Core courses: 34 credits (GPA of 2.3)
Big Data specific courses: 24 credits
Minor in Computer Science
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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2003H STROME ENTREPRENEURIAL CTR, NORFOLK, VA, 23529
1004 Rollins Hall, Norfolk, VA 23529
2101 Dragas Hall, Norfolk, VA 23529
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