Biostatistics PhD

Major: Public Health
Degree Awarded: Doctor of Philosophy (PhD)
Calendar Type: Quarter
Total Credit Hours: 86.0
Co-op Option: None
Classification of Instructional Programs (CIP) code: 26.1102
Standard Occupational Classification (SOC) code: 15-2041

About the Program

The PhD in biostatistics will train highly functional statistical researchers with the breadth of knowledge to contribute to many applied domains or to specialize in a single theoretical domain. Students should expect to receive a strong, quantitative foundational core in statistical theory while simultaneously having opportunities to apply these concepts to a wide range of practical applications. An important feature of our new program is deliberate exposure to more contemporary quantitative concepts in data science.

The doctoral program in biostatistics will offer interdisciplinary instruction and research opportunities that are designed to provide a solid training in both statistical theory and in applications of biostatistical methods to a variety of relevant contexts that are central to modern interdisciplinary research. This program will prepare doctoral students for successful careers in academia (both teaching and research), government agencies, private health-related organizations/industries and many other data-driven industries. 

Additional Information

For additional information about the program, visit the Dornsife School of Public Health web site.

Admission Requirements

Applicants must have a Bachelor’s or a Master’s degree from an accredited institution and must satisfy general Drexel University School of Public Health requirements for admission. Admission to the PhD program in biostatistics is based on:

1. A strong academic background in mathematics, statistics, and related sciences, and practical, professional, or research experience. Background in mathematics should include one year of calculus including multivariate calculus, linear algebra, and a calculus-based course in probability or statistics. Additional background in biological, social, or health sciences is desirable.

2. Three letters of recommendation.

3. GRE scores for the General Test.

4. TOEFL/IELTS scores are required for all applicants whose native language is not English. If an international applicant has attended a US institution of higher education for two or more years of consecutive enrollment, this TOEFL requirement will be waived.

5. Statement of purpose.

6. Official transcripts of all post-secondary school course work completed or attempted.

PhD applicants in biostatistics traditionally have an undergraduate degree in mathematics or statistics, but students with degrees in other fields, in particular data science, computer science or engineering, will be considered with the requirement that necessary mathematical course work must be completed. Applicants must have at least a B+ average in courses required as prerequisites for the program. We will require that PhD students should have strong mathematical background, which includes having completed courses such as multivariate calculus, real analysis, and linear algebra, familiarity with probability and statistics. Additionally, exposure to statistical programming is highly desirable. 

Degree Requirements

Required Courses
BST 551Statistical Inference I3.0
BST 567Statistical Consulting3.0
BST 569Linear Statistical Models4.0
BST 570Generalized Linear Models3.0
BST 604Applied Bayesian Analysis3.0
BST 623Theory of Generalized Linear and Mixed Models3.0
BST 625Probability Models and Stochastic Processes3.0
BST 626Research Skills in Biostatistics I1.0
BST 627Research Skills in Biostatistics II1.0
BST 628Research Skills in Biostatistics III1.0
BST 651Statistical Inference II4.0
BST 701Advanced Statistical Computing3.0
Non-credit Consulting Lab
BMES 547Machine Learning in Biomedical Applications3.0
BST 999Biostatistics Thesis Research30.0
EPI 570Introduction to Epidemiology3.0
MATH 510Applied Probability and Statistics I3.0
PBHL 501Introduction to Public Health0.0
Electives (Choose Three)9.0
Longitudinal Data Analysis
Survival Data Analysis
Design & Analysis of Clinical Trials
Nonparametric and Semiparametric Models
Advanced Clinical Trials
Advanced Bayesian Analysis
Causal Inference in Epidemiology
Probability Theory I
Probability Theory II
Topics in Probability Theory
Selectives (Choose two)6.0
Applied Multivariate Analysis
Making Sense of Data
Data Science Using R
Introduction to GIS for Public Health
Topics in Computer Simulation
Total Credits86.0

Sample Plan of Study

Term 1Credits
PBHL 501Introduction to Public Health0.0
 Term Credits0.0
Term 2
BST 569Linear Statistical Models4.0
EPI 570Introduction to Epidemiology3.0
MATH 510Applied Probability and Statistics I3.0
 Term Credits10.0
Term 3
BST 551Statistical Inference I3.0
BST 570Generalized Linear Models4.0
Elective or Selective3.0
 Term Credits10.0
Term 4
BST 604Applied Bayesian Analysis3.0
BST 651Statistical Inference II4.0
BST 701Advanced Statistical Computing3.0
 Term Credits10.0
Term 5
BST 567Statistical Consulting3.0
BST 625Probability Models and Stochastic Processes3.0
BST 626Research Skills in Biostatistics I1.0
Elective or Selective3.0
 Term Credits10.0
Term 6
BMES 547Machine Learning in Biomedical Applications3.0
BST 627Research Skills in Biostatistics II1.0
Electives or Selectives6.0
 Term Credits10.0
Term 7
BST 623Theory of Generalized Linear and Mixed Models3.0
BST 628Research Skills in Biostatistics III1.0
BST 999Biostatistics Thesis Research3.0
Elective or Selective3.0
 Term Credits10.0
Term 8
BST 999Biostatistics Thesis Research9.0
 Term Credits9.0
Term 9
BST 999Biostatistics Thesis Research9.0
 Term Credits9.0
Term 10
BST 999Biostatistics Thesis Research9.0
 Term Credits9.0
Total Credit: 87.0
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