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Biostatistics Program
Ph.D. Program in Statistics with an Emphasis in Biostatistics

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Course Requirements

Background Courses expected at entrance
Basic Required Courses
The Biostatistics emphasis degree requires the six core courses for the Department of Statistics Ph.D.:

Course Topic # of Courses Example
Advanced Calculus 1 Math 521 - Advanced Calculus
Linear Algebra 1 Math 340 - Elementary Matrix and Linear Algebra
Programming (e.g., C++) 1 Comp Sci 302 - Introduction to Programming
Introduction to Mathematical Statistics 2 Stat 309-310 - Introduction to Mathematical Statistics

Basic Required Courses

Course Number Credits Course Title
Six Core Courses Required for Ph.D. in Statistics
Statistics 709 3 Mathematical Statistics I
Statistics 710 3 Mathematical Statistics II
Statistics 831 3 Theory of Probability
Statistics 849 3 Theory and Application of Analysis of Variance I
Statistics 850 3 Theory and Application of Analysis of Variance II
Statistics 998 3 Statistical Consulting
To satisfy the emphasis in biostatistics students are also required to take
Statistics 641 3 Statistical Methods for Clinical Trials
Statistics 642 3 Statistical Methods for Epidemiology
Statistics 741 3 Survival Analysis Theory and Methods

Students may select one of 3 required courses:

Statistics 732 Large Sample Theory of Statistical Inference
Statistics 775 Bayesian Analysis
Statistics 851 Theory of Linear Models

Electives- totaling at least 21 credits.

At least 15 elective credits from the following list must be taken in the Statistics Department. At least three of these 15 credits (one course) must be from 732, 775, or 750.

All courses counted toward the required elective course credits must be passed with a grade of B or higher. By earning these required and elective course credits, the student meets the course requirements for the Department of Statistics PhD, and has specialized in biostatistical topics (641, 642, 741). See the Department of Statistics PhD regulations for further course details. A suitable course sequence for each student will be established by consultation with faculty advisors.

 

Category Course Number Course Title
Inference (I) Statistics 611 Sample Survey Theory and Methods
Statistics 809 Nonparametric Statistics
Statistics 829 Decision Theory
Statistics 842 Hypothesis Testing
Statistics 853 Bayesian Inference
Statistics 832 Theory of Probability
Stochastic Processes and Time Series (II) Statistics 701-702 Applied Time Series Analysis, Forecasting and Control I and II
Statistics 840 Statistical Model Building and Learning
Statistics 841 Nonparametric Statistics and Machine Learning Methods
Statistics 860 Estimation of Functions from Data
Design and Analysis (III) Statistics 611 Sample Survey Theory and Methods
Statistics 749 Mathematical Models and Response Surface Methodology
Statistics 803 Experimental Design I
Statistics 824 Nonlinear Regression Analysis with Engineering Applications
Statistics 851 Generalized Linear Models
Special Techniques (IV) Statistics 751 Sequential Analysis
Statistics 760 Multivariate Analysis I
Statistics 761 Decision Trees for Multivariate Analysis
Statistics 771 Statistical Computing
Statistics 826 Theory of Life Testing and Reliability
Other Statistics 992 Seminar (Special Topics)
  Statistics/BMI 877 Statistical Methods for Molecular Biology

It is required that at least one course in the Health Sciences be audited unless a previous biological background exists.

* Please see Timetable under enrollment for instructions on how to audit a course. To satisfy the Biostatistics requirement, the audited course must appear on the student's transcript.


 

Suggested Health Science Course Course Title
Genetics 466 General Genetics
Medical Genetics 565 Human Genetics
Medical Genetics 629 Population Genetics
Physiology 603 Human Physiology and Biophysics
Oncology 401 Introduction to Experimental Oncology
Preventive Medicine 797 Introduction to the Strategy and Methods of Epidemiology

Minor

The prospective candidate for the Ph.D. in Statistics must obtain a minor requirement, either by course work in another department, or as a distributed minor. The wide applicability of statistical techniques makes possible fulfillment of the minor requirement in such diverse areas as mathematics, biology, engineering, natural sciences, agriculture, and the social sciences. For current regulations, the student should consult the Minor Requirements & Procedures for PhD Students in Statistics in Section 6 of the Graduate School Handbook.

For an example of how these courses can be applied to an entire Ph.D. program, see the sample program page.

 

 

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