This course aims to provide an overview of the methodological toolkit available to empirical researchers who are interested in making causal inference using quasi-experimental data. In marketing, randomized experimentation represents the gold standard for making causal inference using empirical data. In an ideal setting, we would randomly assign participants to different groups to receive varying types or levels of treatment. Nonetheless, there are many marketing-relevant settings where researchers do not have access to experimental data, or where running such experiments is too expensive or infeasible. Additionally, ethical considerations frequently preclude randomly assigning treatments, such as instances where it could lead to harm or deprive participants of necessary care, as in the case of life-saving treatments or medications. When randomization of treatment is not possible, one may have to rely on enhanced "statistical rigor" to compensate for the deficiencies in "design rigor."

This course provides real-world experience for pre-approved MSBA students. Students will transform data into actionable insights for strategic and/or operational decision-making and will refine skills gained in graduate coursework through team-based, real-world projects. Emphasis is placed on interpreting analytical results within organizational contexts and communicating findings effectively to stakeholders. Students will apply advanced tools and methodologies to solve complex business challenges.

Capstone course requiring application of knowledge and techniques derived from previous courses. Course presents strategic management theories and frameworks, uses integrative cases, and leverages empirical observations to guide students in the formulation of successful firm-level strategies.

This course introduces fundamental concepts and computations for statistical analysis of business data and real-world problems with an emphasis on understanding and interpreting statistical information, and using it to form sound judgments in business situations. The course covers basic descriptive statistical methods, sampling methodology, how to draw inferences from samples to larger populations and how to make predictions based upon historical relationships between variables. Theoretical background in descriptive and inferential statistical methods will be used support knowledge of the appropriate statistical test to perform in a given situation. The statistical methods that are covered in the course equip the student with the analytic reasoning and quantitative arguments that applies to all areas of business and management.

Supply chain constitutes a core competency, spanning most business functions required for the delivery of products and services to consumers. Advances in information technology and analytics facilitate continued improvement in supply chain infrastructure and operations efficiency. This course will introduce fundamental concepts in supply chain management, IT-enabled supply chain operations, procurement management, production planning, inventory management, and logistics and transportation. The course involves the use of Excel to support decision making, the study of selected business case studies, and the use of a team-based supply chain simulation game.

Not available at this time

Overview of human resource management theory and practice in organizations. Legal framework; job design; recruitment, selection, performance appraisal, and separation; compensation; benefits; contemporary issues in HRM.

Contact department for description.

This course examines practices and systems utilized in managing and operating today's modern lodging establishments. Topics covered include: front office operations, guest service and accounting, revenue management, security and safety, and housekeeping as well as issues related to coordination with other departments such as food and beverage or sales.

This course develops expertise in Finance, Technology, Innovation, leadership, and decision-making by focusing on real-world challenges in the field of FinTech. We will be actively discussing and learning how to analyze, identify, and manage/innovate FinTech across many functional disciplines, including Financial, Insurance, Banking, Trading, Information Technology, and Budgeting. Students are introduced to the Financial industry and the FinTech ecosystem. The course adopts a decision-maker and leadership perspective (business, operational, functional, and technical leadership) by emphasizing the relationships among financial data, their underlying economic events, risk profiles, challenges/opportunities, and the responses by all stakeholders in a business/corporation.