Master of Science in Quantitative Management: Business Analytics
Program Code: F-MQM-MSQM
Degree Designation: Master of Science in Quantitative Management
Department: Fuqua School of Business
Website: MQM: Business Analytics | Duke's Fuqua School of Business
Program Summary
The Master of Science in Quantitative Management: Business Analytics program is a pre-experience master’s degree program intended to provide a foundation in data analytics with a focus on specific issues in one of four industry domains. In addition to learning data science tools, the program stresses critical thinking and communication skills to help students ask the right questions, generate insights, and present solutions effectively. Designed for students with zero to three years of work experience, MQM: Business Analytics is a STEM-designated degree program giving international students meeting certain requirements the opportunity to work in the United States for three years.
Students interested in the program should contact the Office of Admissions, The Fuqua School of Business, Duke University, Box 90120, Durham, NC 27708-0120; email: admissions-info@fuqua.duke.edu; website: MQM: Business Analytics | Duke's Fuqua School of Business; phone: (919) 660-7700; fax: (919) 684-2818.
Academic Requirements
Curriculum
The curriculum for the Fuqua MQM: Business Analytics Program involves sixteen required courses totaling at least 50 graduate credit hours and a minimum cumulative GPA of 3.0 or higher. The Finance track requires an additional course, totaling at least 53 graduate credit hours. These courses are offered over five terms (three–four courses per term). Most courses meet twice a week for 2.25 hours. Classes generally meet Monday and Thursday or Tuesday and Friday, with occasional classes falling on Wednesdays. When classes are not being held, Wednesdays are reserved for MQM: Business Analytics programming, such as Career Management Center activities, team-building sessions, and professional development seminars.
Modern analytics requires the ability to not only perform deep quantitative analysis, but also to communicate insights throughout an organization. As such, applicants with a strong quantitative background are preferred, especially from STEM fields in which quantitative tools are used in applied environments. While not required, applicants are encouraged to have some familiarity with statistics, mathematics, and computer programming.
Courses
The MQM: Business Analytics Program consists of two types of courses. Core courses consist of technical courses, which develop the data science tools necessary to perform deep quantitative analysis, and critical thinking and communication courses, which develop the ability to present insights effectively. Track courses are focused on topics specific to one of four tracks: Finance, Marketing, Risk, and Strategy.
Each student is admitted into a track and must complete all required track courses to graduate. In addition, students are able to select elective courses subject to availability, generally from track courses offered outside their admitted track. There are no course exemptions or course substitutions in the program.
The planned schedule, which is subject to change, for each track is as follows:
Finance
Summer
Applied Probability & Statistics (Core-518Q)
Business Communications (Core-507Q)
Business Fundamentals (Core-531Q)
Programming for Analytics (Core-563Q)
Fall 1
Data Science for Business (Core-520Q)
Data Infrastructure (Core-519Q)
Introductory Finance (Track-526Q)
Fall 2
Critical Thinking & Collaboration (Core-542Q)
Derivatives (Track-528Q)
Intermediate Finance (Track-527Q)
Modern Analytics (Core-546Q)
Spring 1
Data Visualization (Core- 522Q)
Decision Analytics and Modeling (Core-521Q)
Fixed Income Securities (Track-529Q)
Spring 2
Capstone Project (Core-532Q)
Financial Risk Management (Track-530Q)
Navigating Organizations (Core-543Q)
Marketing
Summer
Applied Probability & Statistics (Core-518Q)
Business Communications (Core-507Q)
Business Fundamentals (Core-531Q)
Programming for Analytics (Core-563Q)
Fall 1
Data Infrastructure (Core-519Q)
Digital Marketing (Track-549Q)
Data Science for Business (Core-520Q)
Fall 2
Critical Thinking & Collaboration (Core-542Q)
Customer Relationship Management (Track-553Q)
Modern Analytics (Core-546Q)
Spring 1
Decision Analytics and Modeling (Core-521Q)
Data Visualization (Core- 522Q)
Market Intelligence (Track-552Q)
Spring 2
Capstone Project (Core-532Q)
Navigating Organizations (Core-543Q)
Pricing (Track-555Q)
Information Risk Management
Summer
Applied Probability & Statistics (Core-518Q)
Business Communications (Core-507Q)
Business Fundamentals (Core-531Q)
Programming for Analytics (Core-563Q)
Fall 1
Data Infrastructure (Core-519Q)
Data Science for Business (Core-520Q)
Managing Operational and Informational Risks (Track-515Q)
Fall 2
Critical Thinking & Collaboration (Core-542Q)
Fraud Analytics (Track-523Q)
Modern Analytics (Core-546Q)
Spring 1
Data Visualization (Core- 522Q)
Decision Analytics and Modeling (Core-521Q)
Empirical Economic Analysis (Track-548Q)
Spring 2
Capstone Project (Core-532Q)
Managing Cybersecurity Risk (Track-534Q)
Navigating Organizations (Core-543Q)
Strategy
Summer
Applied Probability & Statistics (Core-518Q)
Business Communications (Core-507Q)
Business Fundamentals (Core-531Q)
Programming for Analytics (Core-563Q)
Fall 1
Data Infrastructure (Core-519Q)
Data Science for Business (Core-520Q)
Strategic Management (Track-558Q)
Fall 2
Critical Thinking & Collaboration (Core-542Q)
Operations Analytics (Track-556Q)
Modern Analytics (Core-546Q)
Spring 1
Data Visualization (Core- 522Q)
Decision Analytics and Modeling (Core-521Q)
Empirical Economic Analysis (Track-548Q)
Spring 2
Capstone Project (Core-532Q)
Navigating Organizations (Core-543Q)
People Analytics (Track-559Q)
Capstone
The MQM: Business Analytics Program culminates in the Capstone Project (Core-532Q), a six-week intensive project in which teams of four to five students partner with a faculty advisor to solve a specific industry problem utilizing analytics techniques. The capstone is designed to give students the opportunity to utilize the domain-specific skills developed throughout the program in a real-world environment in which they are exposed to the challenges inherent to the modern data environment.