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This course covers the fundamentals of the analytical aspects of marketing research. The course has an applied orientation with application to contemporary issues in marketing.

Teams work on a project developed in consultation with a not for profit organization, and present results to organizations and boards.

Provides project work experience in the field of management.

This course helps business graduate students learn how to prepare and visualize data appropriately, to explore patterns and relations, and to convey these findings effectively.

To introduce participants to a problem-driven approach to assembling, analyzing, and taking action on data. With the Problem-Plan-Data-Analysis-Conclusions (PPDAC) Cycle and Gartner’s Analytics Maturity Model (Descriptive → Diagnostic → Predictive → Prescriptive Analytics) as frameworks, fundamental concepts for the analysis of data will be covered, including descriptive statistics, visualizations, probability and distributions, confidence intervals, hypothesis testing, and regression. Knowledge of the “whys” behind these tools facilitates rigorous data-driven decision-making. The learning objectives of this course are:

1. To understand and execute the process of decision-making based on data;
2. To assemble, summarize, visualize, and analyze data arising in decision making; and,
3. To conceptualize and interpret models for diagnostic, predictive, and prescriptive analytics.

The instructional approach will combine theory and application. A computer with the rights to install software will be needed in each class (a smartphone/tablet will not suffice). Please note that necessary theoretical foundations will need to be covered to ensure appropriate application of these concepts in practice.

This course will expose doctoral students to the design of research studies, quantitative measurement of variables, data analysis, and formulation of mathematical models in audit-related research.

To introduce participants to a problem-driven approach to assembling, analyzing, and taking action on data. With the Problem-Plan-Data-Analysis-Conclusions (PPDAC) Cycle and Gartner’s Analytics Maturity Model (Descriptive → Diagnostic → Predictive → Prescriptive Analytics) as frameworks, fundamental concepts for the analysis of data will be covered, including descriptive statistics, visualizations, probability and distributions, confidence intervals, hypothesis testing, and regression. Knowledge of the “whys” behind these tools facilitates rigorous data-driven decision-making. The learning objectives of this course are:

1. To understand and execute the process of decision-making based on data;
2. To assemble, summarize, visualize, and analyze data arising in decision making; and,
3. To conceptualize and interpret models for diagnostic, predictive, and prescriptive analytics.

The instructional approach will combine theory and application. A computer with the rights to install software will be needed in each class (a smartphone/tablet will not suffice). Please note that necessary theoretical foundations will need to be covered to ensure appropriate application of these concepts in practice.

Students will obtain an understanding of marketing across national borders and cultures. The differences and similarities throughout marketing functions are explored.

This Ph.D. Seminar provides a fundamental understanding of group processes in organizations by analyzing and critiquing cross-level classic and contemporary theories, and research utilizing quantitative models on groups.