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Graduate Courses

The Course Catalog includes course descriptions of all courses offered by F. W. Olin Graduate School of Business. For descriptions of the courses offered in the current or upcoming semesters, please see our Course Listing

 Graduate Course Catalog





Data Exploration (Quantitative Methods)


QTM7200 Data, Models and Decisions Data, Models and Decisions (DMD) - This course is concerned with identifying variation, measuring it, and managing it to make informed decisions. Topics include: numerical and graphical description of data, confidence intervals, hypothesis testing, regression, decision analysis, and simulation. Applications to Economics, Finance, Marketing, and Operations illustrate the use of these quantitative tools in applied contexts. The course utilizes spreadsheet, statistical, and simulation software.


QTM7540 Business Forecasting with Predictive Analytics 1.5 credit graduate elective Predictive analytics encompasses a variety of statistical techniques that analyze current and historical facts to make predictions about the future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify possible risks and opportunities. Predictive models capture relationships among many factors to allow the assessment of risk or the potential associated with a particular set of conditions. This course introduces advanced forecasting methods in the context of real business analytics data and decision-making situations. The objectives of the course are: to provide experience in using time series data (e.g., sales, profits, financial and economic indicators, and industry sector indicators) to explain the impact of various internal and external factors and to predict future trends; to provide a framework for comparing alternative forecasting models for validity, accuracy, and feasibility; to enhance an appreciation for the limitations of forecasting models; to provide exposure and experience in using analytics software to develop forecasting models; and to develop skills at communicating statistical concepts, methods, results, and inferences effectively in a managerial context. Teamwork and professional presentation of analysis and recommendations will be required during this course. It emphasizes the use of the latest technological methods (Minitab and RStudio) for prediction. Prerequisites: QTM7200


QTM7571 Business Intelligence, Analytics & Visualization Formerly Business Intelligence & Data Mining This course will examine the methods and challenges faced in turning data into insightful analytics in business. With data sizes significantly increasing in the last decade, extracting meaningful information to compete successfully is essential. You will accomplish this by learning techniques for data gathering, data analysis, and visualization as well as in discussion on companies currently trying to turn the information they gather into business opportunities. We will learn a variety of methods and software for finding patterns(such as regression, neural networks, association rules, CART, forecasting etc.), building models, and ultimately making decisions using large data sets. We will address questions such as: Guest speakers who are executives and consultants in the field of analytics and visualization will discuss how they address these challenges in their companies. This is a hands-on course with in-class exercises and group projects to help students learn and apply data analysis techniques preparing them for the practical challenges analysts face in the real world. - How does Amazon recommend products based on your past purchases ? - How to forecast energy consumption based on historical weather and consumption data? - How do credit-card companies detect fraud? - What challenges does Big Data pose to companies and how to handle these challenges? Prerequisite: QTM7200


QTM7575 Financial Modeling using Simulation and Optimization The focus of this course is on developing spreadsheet models for a wide variety of financial concepts including, but not limited to portfolio optimization, option pricing, asset allocation, value at risk, asset prices, etc. Students will gain familiarity with the financial instruments through the construction of the models, and will gain greater insights by analyzing and solving the models. Simulation and optimization are used extensively to analyze the models. Particular attention is paid to modeling uncertainty via random variables and the mathematics of stochastic variables. Prerequisite: QTM7200


XXX7580 Independent Research ******Independent research is available for all academic divisions.Registration is manual for students through Graduate Programs and Student Affairs****** Independent Research provides an opportunity to conduct in-depth research in areas of a student's own specific interest. Students may undertake Independent Research for academic credit with the approval of a student-selected faculty advisor, the appropriate division chair, and Graduate Programs and Student Affairs. Please note that a student is responsible for recruiting a faculty advisor through the student's own initiative and obtain the advisor's prior consent/commitment before applying for an independent research project. Authorization for such a project requires submission of a formal proposal written in accordance with standards set forth by the Graduate School. The research project normally carries 1.5 or 3 credits. For more information and a proposal outline please visit:


QTM9515 Introduction to Data Science and Business Analytics 1.5 credit Intensive Elective This course is an introduction to data science – the science of iterative exploration of data that can be used to gain insights and optimize business processes. The course discusses the data analytics lifecycle, and introduces predictive analytics techniques in the context of real-world applications from diverse business areas such as database marketing, financial forecasting, and operations. The focus is on framing business problems as analytics problems. A brief map of applications and an overview is provided for advanced methods for data visualization, logistic regression, decision tree learning methods, clustering, and association rules. Students will gain exposure to different software packages for data visualization as well as R, the most popular open-source package used by data scientists around the world. Since R is freely available, students will be able to apply the skills acquired in this course regardless of where they work after graduation. Students can pursue these topics in more depth in QTM7571 Business Intelligence, Analytics, and Visualization. Prerequisites: QTM7200