This course discusses issues associated with data capture, organization, storage, extraction, and modeling for planned and ad hoc reporting. Enables student to model data by developing conceptual and semantic data models. Techniques taught for managing the design and development of large database systems including logical data models, concurrent processing, data distributions, database administration, data warehousing, data cleansing, and data mining.

This course provides an overview of marketing analytics in the context of marketing research, product portfolios, social media monitoring, sentiment analysis, customer retention, clustering techniques, and customer lifetime value calculation. Students will be introduced to, mathematical and statistical models used in these applications and their implementation using statistical tools and programming languages such as SAS, SPSS, Python and R. Multiple data sources will be used ranging from structured data from company databases, scanner data, social media data, text data in the form of customer reviews, and research databases. Students will complete guided projects using real time data and make effective use of visualization to add impact to their reports. There are no listed pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.

This course examines the impact that the internet has on traditional and contemporary business-to-consumer marketing activities. It explores these implications in both strategic and tactical terms to enhance organizations' levels of competitiveness. The course identifies the use of the internet in enhancing value for consumers and considers the leverage of the latest technologies, trends, e-culture and innovation through the medium of the internet.

This course is designed introduce doctoral students to statistical methodology as it pertains to the study of multivariate techniques used in the behavioral sciences. The course will cover a range of statistical procedures and programs for multivariate data analysis. The focus is on practical issues such as selecting the appropriate analysis, preparing data for analysis, interpreting output, and presenting results of a complex nature. Topics covered include multivariate data screening, analysis of variance, multi-dimensional scaling, factor analysis, OLS regression, mediation and moderation among others.

This course provides a detailed look at structural equation modeling (SEM) for doctoral students. SEM is a technique for modeling the relationships among multiple latent variables. It includes models that have multiple indicators of constructs (latent variables; confirmatory factor analysis) that have directional relationships among constructs (path analysis; structural equations). The course will cover both conceptual and practical aspects of SEM, with the goal of preparing the student to use SEM in original research and to critically evaluate its use in scholarly work. Further, it introduces the student to partial least squares modeling and to Bayesian approaches in structural equations modeling.

A general management perspective on the critical impact of marketing in organizations. Topics include an overview of the marketing process, market research, segmentation, and target markets. The focus is on the process of creating, communicating, and delivering customer value through the marketing mix. The course is structured around the managerially controllable elements of product, price, promotion and distribution, plus the interrelationships of these elements.