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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, Models and Decisions (QTM7200) Boston Location 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. QTM7200 will meet for 7 face-to-face sessions, plus a 3 hour online session, and an integrated session with ECN7200 and MKT7200 held on a Saturday April 23rd 9:00 AM - Noon - Boston location. QTM7200 is part of Cluster B, but can be taken prior to ECN7200 and MKT7200 as a prerequisite or students can take QTM7200 as a co-requisite while enrolled in ECN7200 and MKT7200. All of the courses in Cluster B must be taken at the same location. QTM7200 is a prerequisite for Cluster F MOB7202 and MBA7201.


Meeting Dates: Friday, January 22nd 6:30 PM - 9:30 PM Saturday, January 30th 8:30 AM - 4:30 PM Saturday, February 6th 8:30 AM - 4:30 PM DROP DEADLINE: January 22nd by 11:59 PM 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.