Statistical Science PhD and MS Courses

Typical First Year PhD Courses:
213 Introduction to Statistical Methods	205 Probability & Measure Theory
253 Applied Stochastic Processes	215 Statistical Inference
290 Modern Statistical Data Analysis	244 Linear Models
Typical Second Year PhD Courses:
214 Probability and Statistical Models	376 Advanced Modeling & Scientific Computing
216 Generalized Linear Models	395 Readings in Statistical Science
291/2 Independent Study	293/4 Special Topics in Statistics
390 Statistical Consulting Workshop
Other Courses Taught Regularly (or as Topics):
218 Statistical Data Mining	226 Statistical Decision Theory
246 Experimental Design	270 Statistical Methods in Computational Biology
280 Spatial Statistics	281 Modern Nonparametric Theory & Methods
345 Multivariate Analysis	356 Time Series and Forecasting
357 Stochastic Processes

Synopses of many of these courses are available in the Course Descriptions and also under the listings and web pages of current and recent semesters course offerings. Other and more advanced topics are occasionally offered as full semester courses. "Special Topics" courses STA 293 and STA 294 have included in recent semesters, topics such as

Bayesian networks & graphical models	Topics in bioinformatics	Statistical genetics
Statistical learning theory & algorithms	Statistical shape analysis	Information theory
Geometry and random matrices	Environmental modelling	Model selection
Nonparametrics and regularization	Categorical data analysis	Asymptotic theory
Advanced theory of statistics	Foundations of statistics	Objective Bayes
Advanced statistical computation	Ordinal data analysis	Nonparametric methods
Monte Carlo methods in finance	Wavelets and statistics	Empirical processes
Survival and reliability analysis	Industrial statistics	Meta-analysis

Courses outside Duke Statistics

Duke Statistics graduate students frequently take courses from other departments. Some of these courses are cross-listed with Duke Statistics. Of these, a number of primary cross-listings are already noted above, and others include:

BUS 510/STA 221   Bayesian Inference and Decision
BUS 513/STA 234   Choice Theory
BUS 525/STA 231   Behavioural Decision Theory
CBB 221/STA 278   Gene Expression Analysis
CBB 241/STA 271   Statistical Genetics
CBB 250/STA 277   Computational Structural Biology
CPS 250/STA 250   Numerical Analysis
MTH 287/STA 207   Probability
MTH 288/STA 297   Topics in Probability Theory

Additional non-STA courses from the following programs (among others) are often of interest to Duke Statistics students:

• The university-wide Computational Biology and Bioinformatics graduate program
• The Department of Computer Science
• The Department of Mathematics
• The Department of Electrical and Computer Engineering
• The Department of Economics
• The Fuqua School of Business
• The Department of Sociology
• The Nicholas School of the Environment

Finally, Duke University has an agreement with the University of North Carolina system that graduate students may take courses on other campuses. This permits a student to take at most one course in a semester at another university, on condition that the student is registered for the normal course load (at least 2 other courses) at Duke and subject to the approval of the advisor and DGS.Full details are here.

Graduate Service Courses (non PhD/MS)

Additional Duke Statistics courses, cross-listed with other programs for which they are primary service courses, include:

• STA 210A: Statistics and Data Analysis for Policymakers
• STA 240: Applied Data Analysis for the Environmental Sciences
• STA 242: Applied Regression Analysis
  

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