Program Requirements



36 Credits:

  • 24 Graded STA Credits
  • 3 to 6 Credits, Graded or Ungraded, in STA
  • 6 to 9 Graded Credits in STA or Non-STA Courses

Out of the total required 36 credits for the program, 18 credits are dedicated to core courses. The remaining 18 credits offer various choices, encompassing internal and external electives to provide students with a comprehensive and flexible educational experience.

For MSS Electives, we offer a broad selection of in-house STA courses and provide opportunities for cross-listed courses with other departments at Duke University. These collaborative offerings span departments such as MATH, ECE, CS, B&B, and MIDS (Master's of Interdisciplinary Science.)

External Electives are graduate-level courses that align with a student's chosen track and are provided by various academic departments at Duke University. These courses can be taken by students with the approval of the respective course instructors. Some of the most sought-after external courses include those related to Natural Language Processing and Deep Learning, which are offered by the ECE and CS departments. Please refer to our focus tracks for a comprehensive list of available external courses.

Completion Exercise:

The MSS program provides exceptional flexibility in completing exercises tailored to individual student interests. As part of the MSS completion requirements, students can choose from three options to showcase their comprehension and mastery of statistical methods, practical application, and computational skills.

Other Departmental Requirements:

  • Attendance at the one-week in-person summer boot camp and one-week orientation, usually scheduled during the second and third week of August before the start of the semester.
  • Submission of the First-Year Progress Report
  • Attendance at departmental events, including Orientation, the Alumni Dinner (held each Spring), and the Departmental Career Fair (held each Fall)
  • Participation in the exit interview/exit survey process

Graduate School Requirements: 

  • All master’s students are required to complete four hours of RCR training during their orientation and an additional two-hour RCR forum before graduation. 
  • International students who are required to take English for International Students (EIS) courses must receive grades of “CR” (credit) for those courses to be certified to graduate.

Required Core:

The MSS Core consists of six mandatory 3-credit courses covering models, methods, theory, computing, practice, and a 1-credit pro-seminar. If a student possesses substantial prior coursework in one or more of these areas, they may request approval from their advisor and the Master's Director to substitute an alternative, more advanced course prior to registration.

STA 521L  Predictive Modeling and Statistical Learning (Fall, 1st year)
STA 523L  Programming for Statistical Science (Fall, 1st year)
STA 581    ProSeminar: Becoming a Professional Statistician (Fall, 1st year)
STA 532    Theory of Statistical Inference (Fall, 1st year)
STA 602L  Bayesian and Modern Statistical Data Analysis (Spring, 1st year) 
STA 663L  Statistical Computing and Computation (Spring, 1st year)
STA 610L   Multilevel and Hierarchical Models (Fall, 2nd year)

Elective Courses

The selection of available elective courses may vary from semester to semester.

STA 522     Study Design: Design of Surveys and Causal Studies
STA 540L   Case Studies in Statistical and Data Science
STA 561D  Probabilistic Machine Learning
STA 571     Advanced Stochastic Models and Machine Learning
STA 583     Communicating Statistics (offered in Spring and required for second-year portfolio students)
STA 613     Statistical Methods in Computational Biology
STA 621     Applied Stochastic Processes
STA 623     Statistics and Decision Analytics
STA 640     Causal Inference
STA 642     Time Series and Dynamic Models
STA 643     Modern Design of Experiments
STA 650L   Social Network Analysis
STA 671D  Advanced Machine Learning
STA 690     Special Topics in Statistics
STA 693     Research Independent Study*
STA 798     Capstone Project
STA 841     Categorical Data
STA 863     Advanced Statistical Computing
STA 995     Internship (Summer)
STA 996     Internship (Spring of the second year)

* Note that no more than six credits of Independent Study will count towards the Master’s Degree.