General Information

The Master of Science program in INSTORE provides students with the technical skills needed to excel in the new philosophy of data driven decision making that is increasingly becoming standard in areas such as management of business operations, health care delivery, supply chains, to name a few. To prepare students adequately, the degree requirements stress broad familiarity with the quantitative methods. The spectrum includes mathematical optimization, stochastic modeling, probability and statistics, and computer science. The course work emphasizes the solution methodology for mathematical models and their application. The Master of Science program prepares students to pursue managerial careers in business organizations, research labs, retail, health management, etc. The program currently offers three specialized tracks:

1. Machine learning,

2. Business analytics,

3. Computational finance.

Students must choose a track to enroll in at the beginning of their first semester. Students enrolled in a track follow a customized set of courses designed to provide essential understanding of the subject matter of their chosen track. If needed, a student can petition to get a specific set of courses approved as a personalized track. However, this will require approval by the advisor and the faculty of the department. Under special circumstances, a student may be allowed to change tracks midstream. However such a change must be approved by the graduate advisor, and can result in a delay in the completion of degree requirements.


Requirements

The University of North Carolina at Chapel Hill imposes general requirements on all candidates for the Master of Science Degree. These requirements are set forth in the Record of The University of North Carolina at Chapel Hill – the Graduate School. Each track in INSTORE has a specified six courses that each student in the track must take. In addition there are four elective courses that the student must complete to satisfy the course requirements of the degree program. Note that the students must secure permission form the instructor when enrolling in a non-STOR course.


Track I: Machine Learning

Brief Description:

(The earlier track called Applied Statistics and Optimization has evolved into the new Machine Learning track to reflect the recent trends in the field.) Statistics can be viewed as the science of managing uncertainty.  Classical statistical approaches are based on probability theory, and quantification of uncertainty via probability distributions.  Machine learning has arisen as an alternative approach to the same set of data analytic challenges, which was originally much more based purely on the application of optimization methods.  As both fields have developed, there have been fruitful exchanges in both directions, and several of our faculty members are regarded as major players in this process.  The INSTOR Machine Learning Track is aimed at training students in the skills needed to work at this exciting, and rapidly developing, interface between these two fields.  This is done by exposing the students to rigorous courses in both Statistics and Optimization.  Since our department has expert faculty from both these fields, it is uniquely placed to train students in this area.

The students enrolled in this track will take the following six courses in the first semester:

1. STOR 555: Mathematical Statistics
2. STOR 612: Deterministic Models in Operations Research I
3. STOR 664: Applied Statistics I

In addition, the students must take seven electives. At least four of the seven electives courses must be STOR courses at least two of which must be at level 600 and above. The student may select further electives from other departments such as Biostatistics, Computer Science, Bioinformatics, Genomics, Economics, etc. However, all course selections must be approved by the graduate advisor.
Recommended Electives:
• STOR 456: Time Series, Forecasting, Data Mining
• STOR 565: Machine Learning
• STOR 614: Deterministic Models in Operations Research II
• STOR 665: Applied Statistics II
• STOR 641: Stochastic Models in Operations research I
• STOR 642: Stochastic Models in Operations research II
• STOR 743: Stochastic Models in Operations Research III
• STOR 762: Discrete Event Simulation
• STOR 712: Non-linear Programming (when offered)
• STOR 722: Integer Programming (when offered)
• STOR 724: Networks (when offered)
• STOR 756: Design of Experiments (when offered)
• STOR 757: Bayesian Statistics (when offered)
• STOR 705: Practicum
• STOR 992: Masters paper
• ECON 871: Time Series and Forecasting (or an equivalent STOR course when offered)
• COMP 790-90: Data Mining – Concepts, Algorithms, Applications (or an equivalent STOR course when offered

However, all electives must be approved by the student advisor.

Total hours required for degree: 30


Track II: Business Analytics

Brief Description:

Decision making by businesses and organizations is increasingly driven by data. Examples are found in revenue management, marketing, fraud detection, supply chain management, health care management, disease control, traffic congestion control etc. Organizations collect large amounts of data and use it to make intelligent decisions. This requires strong mathematical modeling skills as well as solid background in statistical data analysis and optimization. UNC STOR department offers a unique combination of courses that a student can take to build expertise in these areas. In addition, students can augment these courses by selecting appropriate courses from the Business School, School of Public Health, the Economics department, and the Computer Science department. This allows our students to specialize in a particular area of interest in addition to getting strong methodological training.

The students enrolled in this track are expected to take the following three courses in the first  semester:
1. STOR 555: Mathematical Statistics
2. STOR 612: Deterministic Models in Operations Research I
3. STOR 641: Stochastic Models in Operations Research I

In addition, the students must take seven electives. At least four of the seven electives courses must be STOR courses at least two of which must be at level 600 and above. The student may select further electives from other departments such as Business School at UNC, Business School at Duke, Computer Science, Economics, etc. However, all course selections must be approved by the graduate advisor.

 

Recommended Electives:
• STOR 456: Time Series, Forecasting, Data mining
• STOR 565: Machine Learning
• STOR 614: Deterministic Models in Operations Research II
• STOR 642: Stochastic Models in Operations Research II
• STOR 664: Applied Statistics I
• STOR 665: Applied Statistics II
• STOR 743: Stochastic Models in Operations Research III
• STOR 762: Discrete Event Simulation
• STOR 712: Non-linear Programming (when offered)
• STOR 722: Integer Programming (when offered)
• STOR 724: Networks (when offered)
• STOR 705: Practicum
• MBA 748A: Marketing Analytics
• BA 830: Operations Management I
• BA 832: Operations management II
• BA 837: Decision Making with Spreadsheets (when offered)
• ECON 710: Advanced Microeconomic Theory I
• Quantitative Methods in Marketing (when offered)
• STOR 992: Masters Paper
• COMP 790-90: Data Mining – Concepts, Algorithms, Applications (or an equivalent STOR course when offered
• ECON 871: Time Series and Forecasting (or an equivalent STOR course when offered)

Total hours required for degree: 30


Track III: Computational Finance

Brief Description: 

Computational finance aims at solving problems in financial economics, such as asset pricing, risk analysis, interest rate modeling, market microstructure, etc., via stochastic modeling, statistical inference and scientific computation. These tasks require a rigorous training in probability and statistics. UNC STOR Department has superb expertise in those areas. Students can further augment their training by taking courses in Economics and Finance Departments of Business School.

The students enrolled in this track will take the following three courses in the first semester:
1. STOR 555: Mathematical Statistics
2. STOR 641: Stochastic Models in Operations research I
3. STOR 664: Applied Statistics I

In addition, the students must take seven electives. At least four of the seven electives courses must be STOR courses at least two of which must be at level 600 and above. The student may select further electives from other departments such as Business School at UNC, Business School at Duke, Economics, etc. However, all course selections must be approved by the graduate advisor.

Recommended Electives:
• STOR 456: Time Series, Forecasting, Data Mining
• STOR 565: Machine Learning
• STOR 612: Deterministic Models in Operations Research I
• STOR 614: Deterministic Models in Operations Research II
• STOR 743: Stochastic Models in Operations Research III
• STOR 762: Discrete Event Simulation
• STOR 712: Non-linear Programming (when offered)
• STOR 722: Integer Programming (when offered)
• STOR 724: Networks (when offered)
• STOR 756: Design of Experiments (when offered)
• STOR 757: Bayesian Statistics (when offered)
• STOR 705: Practicum
• ECON 710: Advanced Microeconomic Theory I
• ECON 711: Advanced Microeconomic Theory II
• ECON 871: Time Series and Forecasting (or an equivalent STOR course when offered)
• Busi 880: Financial Economics
• Busi 886: Quantitative Methods in Finance (or an equivalent STOR course when offered)
• Busi 899: Market Microstructure (or an equivalent STOR course when offered)
• Busi 881: Corporate Finance
• STOR 992: Masters paper

Total hours required for degree: 30


Master’s Paper

Every MS student in INSTORE must complete a master’s paper. The student must register in STOR 992 and a STOR faculty member must be either the advisor or co-advisor. The topic of the paper should be broadly within the areas the student’s track. The MS paper can be counted as an elective.