Program Requirements

All new students are required to attend MS Bootcamp

2021 Bootcamp will be on August 2nd - 9th 
2021 Graduate School Orientation will be Tuesday, August 17th

Requirements

Coursework

  • 36 credits: 24 graded STA; 6 graded/ungraded STA; 6 STA or non-STA
  • MSS Core is a set of six required 3-credit courses on models & methods, theory, computing and practice, plus a 1-credit pro-seminar. A student having substantial prior courses in one or more of these may be permitted to substitute an alternate, more advanced course with approval of her/his advisor and the MSD prior to registration.
  • MSS Electives is a set of courses offering a diverse range of advanced and special topics, including advanced courses specific to the STA MSS program.
  • External Electives are graduate level courses related to a student’s track offered by other academic departments at Duke University. If students need to take more than 6 credits external to the department (and have it count toward the degree), they should get approval from the Master's Director prior to registration.

Prerequisites: Some courses, including required first-year courses, have formal prerequisite courses. A student whose grade on a prerequisite course is lower than C+ may be required to undertake additional assignments to enroll in the required course, following discussion with the course instructor and the MSD.

Progress Toward Completion: Each first-year MSS student must complete the Progress Report Form and submit it to the DGS assistant by the last week of April.

Completion Exercise: Either Portfolio of Work or Master's Thesis. Students planning on writing a thesis must begin early work on their research to meet the thesis deadline.

Required Core

The requirements are somewhat flexible depending on student background and interest. Any changes to the requirements must be approved by the Master's Director prior to registration.  

First Year

STA 521L  Predictive Modeling and Statistical Learning (Fall)
STA 523L  Programming for Statistical Science (Fall)
STA 581    ProSeminar: Becoming a Professional Statistician (Fall)
STA 602L  Bayesian and Modern Statistical Data Analysis (Fall)
STA 532    Theory of Statistical Inference (Spring)
STA 663L  Statistical Computing and Computation (Spring)

Second Year

STA 610L   Multilevel and Hierarchical Models (Fall)

Elective Courses

The list of offered elective courses will vary each 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 613     Statistical Methods in Computational Biology
STA 621     Applied Stochastic Processes
STA 623     Statistical Decision Theory
STA 640     Causal Inference
STA 642     Time Series and Dynamic Models
STA 643     Modern Design of Experiments
STA 650L   Social Network Analysis
STA 665     Statistical Programming for Big Data
STA 671D  Advanced Machine Learning
STA 690     Special Topics in Statistics
STA 693     Research Independent Study*
STA 841     Categorical Data
STA 863     Advanced Statistical Computing
STA 995     Internship

* Note that no more than 6 credits of Independent Study will count towards the completion of the Master’s Degree.