Mathematical Sciences (MA)
MA 610 Optimization and Simulation for Business Decisions (3 credits)
Pre-Req: GR 521 or PPF 501.
Optimization and simulation methods are being used as effective tools in many environments that involve decision-making. This course covers classical and modern optimization techniques used today in a business environment. Specifically, the focus will be on linear and nonlinear programming techniques with applications, as well as elective topics selected from game theory, agent-based modeling, and modern simulation and optimization techniques. Examples of application areas of optimization include portfolio selection in finance, airline crew scheduling in the transportation industry, resource allocation in healthcare industry, and minimizing the cost of an advertising campaign in marketing.
MA 611 Time Series Analysis (3 credits)
This course examines methods for analyzing time series. In many data modeling situations, observations are collected at different points in time and are correlated. Such time series data cannot typically be modeled using traditional regression analysis methods. This course provides a survey of various time series modeling approaches, including regression, smoothing and decomposition models, Box-Jenkins analysis and its extensions, and other modeling techniques commonly used, such as quantile estimation and value at risk. It makes use of statistical packages such as SAS, JMP, R andor SPSS.
MA 700 Dir Study in Mathematics (3 credits)
A Directed Study is designed for highly qualified students who, under the direction of a member of the sponsoring academic department, engage in an agreed-upon in-depth independent examination, investigation or analysis of a specialized topic.
MA 705 Data Science (3 credits)
Pre req or Co req: GR 521.
Working with and finding value in data has become essential to many enterprises, and individuals with the skills to do so are in great demand in industry. The required skill set includes the technical programming skills to access, process and analyze a large variety of data sets, including very large (big data) data sets, and the ability to interpret and communicate these results to others. Anyone with these abilities will provide benefit to their organization regardless of their position. This course presents the essentials of this skill set.
MA 706 Design of Experiments for Business (3 credits)
Pre Req: ST 625
This class is planned for those interested in the design, conduct, and analysis of experiments, with an emphasis on business applications. The course will examine how to design experiments, carry them out, and analyze the data they yield. Various designs are discussed and their respective differences, advantages, and disadvantages are noted. In particular, factorial and fractional-factorial designs are discussed in great detail. It has been found to allow cost savings, while revealing the essential nature of the impact of the factors studied, in a manner readily understood by those conducting the experiment as well as those to whom the results will be reported.
MA 707 Introduction to Machine Learning (3 credits)
This course provides analytics students an introduction to machine learning field. Students will be introduced the mathematics and statistics ideas behind the foundation of the machine learning. Particularly, students will be involved in hand on experience to practice the machine learning methods through advanced tools, and work on real-world business questions to look for business solutions. Advanced analytics topics, such as resampling methods, support vector machines (SVM), Bayesian inference, Kernel methods, and simulations, deep learning will be covered in this class.
MA 710 Data Mining (3 credits)
Pre-Req: ST 635
This course introduces participants to the most recent data-mining techniques, with an emphasis on: (1) getting a general understanding of how the method works, (2) understanding how to perform the analysis using suitable available software, (3) understanding how to interpret the results in a business research context, and (4) developing the capacity to critically read published research articles which make use of the technique. Contents may vary according to the interest of participants. Topics will include decision trees, an introduction to neural nets and to self-organizing (Kohonen) maps, multiple adaptive regression splines (MARS), genetic algorithms, association (also known as market basket) analysis, web mining and text mining, and social networks.
MA 755 Special Topics in Mathematical Science (3 credits)
This course offers an in-depth exploration of a selected advanced or emerging topic in mathematics, statistics or data science, based on student and faculty interests. Students may be required to participate in a seminar format, requiring active participation in developing and presenting course materials.
MA 799 Experimental Course in MA (3 credits)
Experimental courses explore curriculum development, with specific content intended for evolution into a permanent course. Topics may be offered twice before becoming a permanent course. Students may repeat experimental courses with a different topic for credit.