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Focuses on the use of quantitative tools to interpret and solve important problems in business and finance. Makes extensive use of spreadsheet modeling, analysis, and mini-cases to present material. Targeted at students with an interest in quantitative methods and modeling.

The area of logistics and supply chain management is concerned with one of the oldest set of business activities. Supply chain system activities—communication, inventory management, warehousing, transportation, and facility location—have been performed since the start of commercial activity. It is difficult to think of any product that could reach a customer without the support that these activities provide. Yet it is only over the last few years that firms have started focusing on logistics and supply chain management as a source of competitive advantage. The sudden realization is that no company can do any better than its logistics system. This becomes even more important when we consider that product life cycles are shrinking and competition is intensifying. Logistics and supply chain management today represents a great challenge as well as a tremendous opportunity for most firms.

Good decisions are not the same as good outcomes. Good outcomes may simply be a matter of luck, while bad outcomes can happen even when you make good decisions. So then, what is a good decision? This question is critical for managers, as today’s competitive business environment presents complex decision problems on an on-going basis. Evaluating different alternatives and gaining insight from past performance is the essence of sound decision-making. This course emphasizes a structured approach to modeling decision problems, and considers the extensive use of data, methods and fact-based management to support and improve decision-making. The course explores basic analytical principles that can guide a manager in making complex decisions and introduces a collection of decision support tools that can be useful in a variety of industries and functions.

Covers foundations for statistical reasoning and statistical applications in business. Topics include graduate-level treatment of descriptive statistics, probability, probability distributions, sampling theory, sampling distributions, statistical inference (estimation and hypothesis testing), and regression analysis. This course is designed to familiarize you with fundamental techniques for summarizing and analyzing data found in the business environment. It focuses on practical, hands-on experience with using and communicating your findings in a clear, concise manner. Business graduates need to be adequately trained in quantitative methods as well as have good communication skills and the ability to work in groups. Interpreting results will be a key component of this course in which statistical software will be used to perform all calculations.

The problems faced by decision makers in today’s competitive business environment are often extremely complex and can be addressed by numerous possible courses of action. Evaluating these alternatives and gaining insight from past performance is the essence of business analytics. This course is designed as an introduction to Business Analytics, an area of business administration that considers the extensive use of data, methods and fact-based management to support and improve decision making. While business intelligence focuses on data handling, queries and reports to discover patterns and generate information associated with products, services and customers, business analytics uses data and models to explain the performance of a business and how it can be improved. This course discusses the benefits of employing analytics and a structured approach to problem-solving in management situations.

Optimization is the branch of operations research that uses mathematical models to tackle complex decision problems. Optimization problems consist of minimizing or maximizing an objective function by choosing the best values for a potentially large number of decision variables while meeting a set of constraints. Optimization problems are typically formulated as mathematical programs for which several solution techniques have been developed over more than sixty years of research and experimentation. This course focuses on formulating decision problems as mathematical models and employing computational tools to solve them. Microsoft Excel is used as the main modeling platform, but the course also covers advanced tools, such as modeling problems with Python to be solved with Gurobi. Optimization modeling will be illustrated in problems associated with operations, marketing, management, and finance.