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Mathematical Sciences Graduate Courses
Prerequisite(s): This course counts toward the foundation requirements for the MSF Quantitative Finance track.
The course presents differential and integral calculus in a single variable, with connections to further study in continuous probability, multivariate calculus and differential equations. Specific emphasis is placed on tools relevant to later study of computational finance. Topics include limits, continuity, differentiation of single-variable and multivariate functions, implicit differentiation, optimization, integration by substitution and by parts, numerical integration, and introductions to ordinary differential equations, continuous probability, and Taylor series.
MA610 Optimization and Simulation for Business Decisions
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.
MA611 Time Series Analysis
Prerequisite(s): ST 625. Not open to students who have completed EC 621.
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.
MA705 Data Science
Pre or Corequisite(s): 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.
MA706 Design of Experiments for Business
Prerequisite(s): 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.
MA707 Introduction to Machine Learning
Prerequisite(s): Pre req: ST 635 and MA 705
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.
MA710 Data Mining
Prerequisite(s): 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.
MA755 Special Topics in Mathematical Science
Prerequisite(s): Varies each semester with topic
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.
ST590 Internship in Statistics
This one-credit course is a unique opportunity that allows students to explore a certain career path, learn valuable workplace skills, broaden their knowledge about a particular area of business analytics, and gives students the chance to add value to their internship by applying their business analytics knowledge. The essence of the internship for Credit Program is that students continue to learn as they work. To a large degree, the education they receive from the internship is up to them. It is based on the quality of the position they have found and the decisions they make once on the job (e.g., asking for certain opportunities). The Business Analytics internship for credit course grants one credit for successfully fulfilling this field-based learning experience, which includes working a minimum of 120 hours at an organization suitable for the individual student’s field learning experience, and completing the specific requirements outlined below during the internship.
ST625 Quantitative Analysis for Business
Prerequisite(s): GR 521 or PPF 501.
This course provides students with an in-depth coverage of simple and multiple linear regression methods and, as time permits, an introduction to the analysis of time series data. Simple and multiple linear regression techniques are covered, including the use of transformations such as squares and logarithms, the modeling of interactions, and how to handle problems resulting from heteroscedasticy and multicollinearity. Issues surrounding outlying and influential observations are also covered. The art and science of model building are demonstrated with the help of cases. Autocorrelation is then considered, and an introduction to the ARIMA modeling of times series is provided. The course makes use of statistical packages such as SAS, JMP, R or SPSS.
ST635 Intermediate Statistical Modeling for Business
Prerequisite(s): ST 625 or Instructor Permission
This course focuses on statistical modeling situations dependent on multiple variables, as commonly found in many business applications. Typical topics covered are logistic regression, cluster analysis, factor analysis, decision trees, and other multivariate topics as time permits. Applications of these methodologies range from market analytics (e.g., direct mail response and customer segmentation) to finance and health informatics. A central objective of the course is for participants to be able to determine the appropriate multivariate methodology based on the research objectives and available data, carry out the analysis and interpret the results. This course makes use of statistical packages such as SAS, JMP, R or SPSS, along with more specialized software.
ST701 Internship in Business Data Analysis
Prerequisite(s): PREQ: ST 635.
This course provides an opportunity for students to apply quantitative and data analysis skills in a live employment environment, serving as a quantitative analyst. With help from the internship coordinator, students identify a suitable internship and meet regularly with the internship coordinator. Students prepare a paper that discusses the internship experience and demonstrates at least one specific case analyzed during the internship period. The course can be used either as a Business Analytics concentration elective with permission of the Business Analytics coordinator, or as a Distribution elective.