Course Selection
These are the primary STA courses that can be taken to satisfy the 18 credit requirement:
| Theory & Methods | Modelling & Applications |
| 205 Probability & Measure Theory | 216 Generalized Linear Models |
| 213 Introduction to Statistical Methods | 218 Statistical Data Mining |
| 214 Probability and Statistical Models | 246 Experimental Design |
| 215 Statistical Inference | 290 Modern Statistical Data Analysis |
| 253 Applied Stochastic Processes | 270 Statistical Methods in Computational Biology |
| 226 Statistical Decision Theory | 280 Spatial Statistics |
| 244 Linear Models | 345 Multivariate Analysis |
| 281 Modern Nonparametric Theory & Methods | 356 Time Series and Forecasting |
| 357 Stochastic Processes | 376 Advanced Modeling & Scientific Computing |
Each MS candidate in Duke Statistics must demonstrate proficiency in both "theoretical" and "applied" statistics. This normally involves taking at least two courses from each of the two lists above, though may be accomplished in other ways subject to the approval of the student's committee and the DGS. Independent Study and Special Topics courses STA 291, 292, 293 and 294 may be included on either list, depending on their content and assessment format in a particular semester.
The MS qualifying exam (the Duke Statistics Ph.D. First Year Exam) is heavily based around "core" material covered in STA 213, 215, 244 and 290, so that each MS student must be proficient in this material; most MS students take some or all of these four core STA courses.
Non-STA Course Substitutes
Subject to approval by the student's Statistical Science MS committee and DGS, up to 6 units (i.e., 2 courses) of graduate credit from non-STA primary courses may be applied to the 18 unit (i.e., 6 courses) Duke Statistics course requirement. Students may petition for any course to be considered. The courses below are pre-approved as substitutes, and apply towards the Modelling & Application proficiency unless indicated as (T&M). Those marked * have STA secondary cross-listings, but will still count towards no more than the maximum of 6 substitute units.
| BUS 510* | Bayesian Inference and Decision | |
| BUS 513* | Choice Theory | |
| BUS 525* | Behavioural Decision Theory | |
| BUS 564 | Experimental Design and Analysis Seminar | |
| CBB 221* | Gene Expression Analysis | |
| CBB 241* | Statistical Genetics | |
| CBB 250* | Computational Structural Biology | |
| CPS 237 | Randomized Algorithms | |
| CPS 250* | Numerical Analysis | |
| CPS 271 | Machine Learning | |
| ECE 281 | Random Signals and Noise | |
| ECE 282 | Digital Signal Processing | |
| ECE 289 | Adaptive Filters | |
| ECON 341, 342, 343 | Econometrics I, II, III (any one or more) | |
| ECON 327 | Empirical Methods in Macroeconomics | |
| ECON 345 | Applied Econometrics | |
| ECON 349 | Empirical Methods in Finance | |
| ECON 350 | Econometrics of Macroeconomic Time Series | |
| ENV 236 | Water Quality Management | |
| ENV 385 | Environmental Decision Analysis | |
| MTH 215 | Mathematical Finance | (T&M) |
| MTH 287* | Probability | (T&M) |
| MTH 288* | Topics in Probability | (T&M) |
| SOC 212 | Soc Statist I: Lin Mod, Path Anal, Struct Eqns | |
| SOC 213 | Soc Statist II: Discrete Multivar Models | |
| SOC 216 | Adv Methods of Demographic Analysis |