G-STA-MS - Statistical Science - Master's
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Coursework
36 credits: 24 graded STA; 6 graded/ungraded STA; 6 STA or non-STA
The 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 with substantial prior coursework in one or more of these may be permitted to substitute an alternate, more advanced course with approval of their advisor and the Master's Director (MSD) prior to registration.
MSS Electives offer 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 (MSD) 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 Director of Graduate Studies Assistant (DGSA) by the last week of April.
Completion Exercise
Students must complete either a Portfolio of Work presentation or a Master's Thesis. Students planning on writing a thesis must begin early work on their research to meet the thesis deadline. (stat.duke.edu/ms/mss-completion-exercise)
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 798. Capstone Project
STA 841. Categorical Data
STA 863. Advanced Statistical Computing
STA 995. Internship