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 health care industry, minimizing the cost of an advertising campaign in marketing.
Pre- or corequisite(s): Pre or Co-Req: ST 625
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.
Prerequisite(s): ST 625 or ST 635 or Instructor Permission
This course will introduce 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), an introduction to genetic algorithms, to association (also known as market basket) analysis, to web mining and text mining, and to social networks.
Prerequisite(s): GR 521 or PPF501
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. Makes use of statistical packages such as SAS, JMP, R or SPSS.
Prerequisite(s): ST 625 or Instructor Permission
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. Makes use of statistical packages such as SAS, JMP, R or SPSS, along with more specialized software.
Prerequisite(s): ST 625 and at least one other Business Analytics concentration 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.