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The MS program in Data Science and Analytics offers students a rigorous program of training in the areas of statistics, optimization, stochastic modeling, and probability. The program is designed to be flexible enough to accommodate students with different technical backgrounds and subject matter interests, and it allows students to pursue a variety of coursework in theory, methodology, computation, and applications.

The MS program  in Data Science and Analytics (formally an MS degree in Statistics and Operations Research) can function as a stand-alone (terminal) degree for individuals seeking jobs in the private or public sector.  However, it also provides a valuable complement to a number of PhD programs in the natural and social sciences, enhancing the credentials of students in these programs seeking academic or industry jobs. In the past, students have completed MS degrees in STOR concurrently with a Ph.D. in areas such as Economics, Sociology, Psychology, Mathematics, and Physics.  Terminal masters students, who seek employment immediately after completion of the degree, have readily found jobs in industry and government.

The Data Science and Analytics MS program requires 30 credit hours of graduate coursework, and the completion of a Master’s Essay.  Students can choose from a wide variety of courses within the STOR Department, as well as a limited number of courses from outside the Department.


Applicants to the MS program should have a strong record of undergraduate coursework in statistics and mathematics.  Statistics coursework should include an introductory course in statistics (similar to STOR 155), an intermediate level course in inference and regression (similar to STOR 455), and a calculus- based probability course (similar to STOR 435). Mathematical coursework should include single and multivariable calculus, as well as an intermediate or advanced level course in linear and matrix algebra. Students interested in theoretical statistics or probability should have prior coursework in advanced calculus or real analysis.

Summary of degree requirements

Completion of the MS degree requires 30 credit hours of graduate coursework and a Master’s essay. The Graduate School requires that at least 24 credit hours must be taken after admission to the MS program: at most six credit hours may be transferred from another accredited institution, or from within UNC-CH, for courses taken before admission to the MS program.  This policy on transferring credits applies to courses taken in the STOR Department as well.  Doctoral students in STOR may receive an MS degree upon the successful completion of their Preliminary Oral Examination.

Time Limit:  In keeping with University regulations, all work credited toward MS degrees, except transferred course work, must be completed within a period of five years from the first date of registration.

*Please note: the official MS degree after successful completion of the program will say “MS in Statistics and Operations Research”.


Degree requirements in detail

A.   Core courses

Students must take 5 out of the following 7 core courses:

  • Data Science (STOR 520 or BIOS 611)
  • Machine Learning (STOR 565) or Advanced Machine Learning (STOR 767)
  • Applied Statistics (STOR 664)
  • Optimization (STOR 612 or STOR 614)
  • Stochastic Modeling (STOR 641)
  • Theoretical Statistics (STOR 555 or STOR 654)
  • Probability (STOR 634)

Furthermore the 5 courses chosen should be such that after completion of these 5 courses, students have taken at least one course from the STAT side (520,565/767,634,555/654,664) and one course from the OR side (612,614,641).

B.   Other course requirements

  • Students must take and pass 12 additional credit hours of STOR-related coursework either inside or outside the STOR Department; outside courses need to be approved by the STOR program’s Graduate Studies Committee.
  • Students must take at least 3 credit hours of STOR 992 (MS essay). A maximum of 3 credit hours of 992 registration may be counted as part of the 30 credit hour minimum.
  • Students must pass all courses and have no more than three credit hours of low pass (L).
  • Students must complete an MS essay (see details below).

C.   Masters Essay and Examination Committee

In addition to their coursework, MS students must also complete a Master’s Essay.  In many cases the Master’s Essay contains the careful modeling and analysis of a data set using ideas and methods from statistics, optimization, or stochastic modeling, including a detailed description of the data set, and a review of the relevant literature.  In other cases, a Master’s essay may present new theoretical results, computational methods, or simulations.  Masters essays are typically 20-30 pages in length.

The Masters essay is completed under the supervision of a Faculty Adviser, who should be an adjunct or regular member of the STOR Department.  With approval of the Graduate Studies Committee, a faculty member outside STOR may serve as a student’s Faculty Adviser.

In consultation with their Faculty Adviser, students should assemble a Master’s Committee consisting of the Faculty Advisor and one other UNC faculty member with interest or expertise in the student’s essay topic.  With approval, individuals outside UNC can serve as second committee members.  Satisfactory completion of the Master’s Essay is based in part on an oral presentation and defense of the essay before the student’s Masters Committee. Presentations are typically 40-60 minutes in length and are usually closed to the public.

Masters students have the option of completing their Master essay and presentation as part of the consulting course (STOR 765).  In this case the written report of the final consulting project (including the description of the project and associated data set, methods of analysis, results, and conclusion) will constitute the Masters essay, the instructor of the course and the client (or another appropriate faculty member) will constitute the Masters Committee, and the in-class presentation of the report (with Committee members present) will constitute the essay defense.


Suggested core courses in the first year

Students seeking broad training in Data Science and Analytics

  • Fall: STOR 520, STOR 664, STOR 641
  • Spring: STOR 614, STOR 565/767, STOR 672

Students wishing to concentrate in Statistics

  • Fall: STOR 634, STOR 654, STOR 664
  • Spring: STOR 635, STOR 655, STOR 665

Students wishing to concentrate in Operations Research

  • Fall: STOR 612, STOR 641, STOR 654
  • Spring: STOR 614, STOR 642, STOR 672


Examples of other courses taken by students


The following serves as examples of various courses taken by MS students in the recent past and is not meant to be comprehensive. Students in the MS program meet with the graduate program director to discuss their plan for courses a few days before registration opens (typically end of October for courses in Spring or beginning March for courses in Fall). Course offerings as well as instructors change regularly.


Example of Fall courses


The following are examples of courses (taken from those offered in the Fall of 2017). There is no guarantee that the same courses will be offered every Fall and only serve as examples of courses taken by students in the program. They are also not meant to be a comprehensive list of all possible courses available for MS students.



743: Simulation, Argon

765: Stat consulting, Marron

831: Advanced Probability, Bhamidi

856: Multivariate Analysis, Sen

890: Object-oriented Data Analysis, Marron

892: Convex Analysis and Optimization, Lu

893: Stat of Climate Research, SAMSI

894: SAMSI

UNC Business School

MBA courses: Supply Chain, Retail Operations, Project management

Special Topics Courses: TBD

Comp Sci:

550: Algorithms and Analysis, Sanjoy Baurah

562: Machine Learning, Vladimir Jojic


511: Stat Computing and Data Management (Fall)

664: Sample Survey Methodology (Spring)


523: Intro to Database Concepts and Analysis (Fall)

Duke Business School:

Supply Chain Models, Robert Swinney

Stochastic Comparisons, Jeannette Song (Spring)



Examples of Spring courses


The following are examples of courses (taken from those offered in the Spring of 2019). There is no guarantee that the same courses will be offered every Spring and only serve as examples of courses taken by students in the program. They are also not meant to be a comprehensive list of all possible courses available for MS students.




672: Simulation, Argon

754: Graduate level time series, Smith

765: Stat consulting, Marron

890: high dimensional time series, Pipiras

891: Probability on Trees and Networks, Fraiman

892: Stochastic Models in Healthcare Operations, Ziya

893: Methods for precision medicine, Forrest, SAMSI

UNC Business School (From 2018)

Empirical Research in Operations (Prof. Kesavan)

MBA-898: Healthcare Operations (Second Half, Deshpande)

MBA-748A: Marketing Analytics (First Half)

MBA-706: Data Analytics (First half, Emadi)

MBA 705: Operations Research Models (First Half, Gilland)

899-030: Asset Pricing (Half term)

899-030: Decision Theory (Half term)

886: Empirical Asset Pricing,  (Full term)

899-038: Advanced Psychometric Modeling in Marketing

Comp Sci:

550: Algorithms and Analysis

555: Bioalgorithms

790-139: Advanced topics in NLP

790-142: Generative methods in machine learning


511: Stat Computing and Data Management (Fall)

664: Sample Survey Methodology (Spring)



523: Database Systems

613: Text Mining,

690-270: data mining: methods and applications