Biostatistics PhD

Major: Biostatistics
Degree Awarded: Doctor of Philosophy (PhD)
Calendar Type: Quarter
Minimum Required Credits: 90.0 (post-bachelor's) or 57.0 (post-master's)

Co-op Option: None
Classification of Instructional Programs (CIP) code: 26.1102
Standard Occupational Classification (SOC) code: 15-2041

About the Program

The Doctor of Philosophy in Biostatistics trains highly functional statistical researchers with the breadth of knowledge to contribute to many applied domains or to specialize in a single theoretical domain. Students will 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 the program is deliberate exposure to more contemporary quantitative concepts in data science.

The program 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. The program will prepare students for successful careers in academia (both teaching and research), government agencies, private health-related organizations/industries, and many other data-driven industries. 

Students will take a comprehensive exam that requires them to synthesize coursework and demonstrate mastery of public health competencies. Students will then develop and defend a dissertation proposal and complete a dissertation of publishable quality and final defense.

Post-Baccalaureate Requirements and Post-Master's Requirements

Both post-baccalaureate and post-master's students are admitted into the doctoral program in Biostatistics.

For post-master’s students in a related field, students must complete a minimum of 57.0 credits.

For post-baccalaureate students, students must complete a minimum of 90.0 credits.

Major Competencies

  • Master fundamental concepts in probability and statistical theory
  • Construct an analysis plan to evaluate a scientific question of interest
  • Critically appraise statistical methods literature
  • Communicate statistical findings effectively and succinctly using data visualization tools and appropriate summaries
  • Direct quantitative methods of collaborative investigations with researchers in public health, medicine, and other health science disciplines

Additional Information

For more information about this program, please contact:

DSPH Academic Advising Team
Office of Education

Additional information can be found on the Dornsife School of Public Health website, including admissions criteria and how to apply.

Degree Requirements

Required Courses
BST 701Advanced Statistical Computing *3.0
BST 751Statistical Inference I *3.0
BST 804Applied Bayesian Analysis3.0
BST 819Statistical Machine Learning for Biostatistics3.0
BST 823Theory of Generalized Linear and Mixed Models3.0
BST 825Probability Models and Stochastic Processes3.0
BST 826Research Skills in Biostatistics I 1.0
BST 827Research Skills in Biostatistics II 1.0
BST 828Research Skills in Biostatistics III 1.0
BST 851Statistical Inference II *4.0
BST 867Statistical Consulting3.0
BST 869Linear Statistical Models *4.0
BST 870Generalized Linear Models *4.0
BST 875Statistical Consulting Lab (taken 3 times )3.0
MATH 510Applied Probability and Statistics I *3.0
EPI 570Introduction to Epidemiology *3.0
RCRG 600An Introduction to the Responsible Conduct of Research0.0
PBHL 501Introduction to Public Health **0.0
Biostatistics Thesis Research
Electives (Choose Two)6.0
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: Theory
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
Data Science Using R
Introduction to GIS for Public Health
Topics in Computer Simulation
Total Credits90.0


Students entering the PhD program with a master's degree in a related field may be waived from these classes, as determined by the PhD Biostatistics Admissions Committee.


Foundational Public Health Learning Objectives: PBHL 501 is only required for students who do not have a degree from a CEPH accredited school.

Sample Plan of Study

First Year
BST 8694.0BST 7513.0BST 8043.0Written Qualifying Exam
EPI 5703.0BST 8253.0BST 8233.0 
MATH 5103.0BST 8704.0BST 8514.0 
PBHL 501*0.0   
 10 10 10 0
Second Year
BST 8261.0BST 8193.0BST 7013.0Oral Candidacy Exam
BST 8673.0BST 8271.0BST 8281.0 
BST 8751.0BST 8751.0BST 8751.0 
BST 9993.0RCRG 6000.0BST 9993.0 
PBHL 5010.0Elective or Selective3.0Elective or Selective3.0 
Elective or Selective3.0Elective or Selective3.0  
 11 11 11 0
Third Year
BST 9999.0BST 9999.0BST 9999.0 
 9 9 9 
Total Credits 90

PBHL 501 is only required for students who do not have a degree from a CEPH accredited school.

Alternative Plan of Study

(Students entering the PhD program with a master's degree in a related field, as determined by the PhD Biostatistics Admissions Committee)

First Year
BST 8261.0BST 8193.0BST 8043.0Written Qualifying Exam and Oral Candidacy Exam
BST 8673.0BST 8253.0BST 8233.0 
PBHL 501*0.0BST 8271.0BST 8281.0 
Elective or Selective3.0Elective or Selective3.0Elective/Selective3.0 
Elective or Selective3.0   
 10 10 10 0
Second Year
BST 8751.0BST 8751.0BST 8751.0 
BST 9998.0BST 9998.0BST 9998.0 
 RCRG 6000.0  
 9 9 9 
Total Credits 57

PBHL 501 is only required for students who do not have a degree from a CEPH accredited school.

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