Mathematical Sciences (MA)

MA 610 Optimization and Simulation for Business Decisions     (3 credits)

PREQ: GR 521 or PPF 501.

Optimization and simulation methods are being used as effective tools in many environments that involve decision making. This is a course that 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 transportation industry, resource allocation in healthcare industry, minimizing the cost of an advertising campaign in marketing.

MA 611 Time Series Analysis     (3 credits)

PREQ: ST 625 Not open to students who have completed EC 621.

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. 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: GR 521. Cross listed with UG course MA 402A.

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 710 Data Mining     (3 credits)

PREQ: 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)

PREQ: MA 710 or MA 799: Data Science or instructor approval.

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.