• Concentrations and course offerings may vary by campus and/or by program modality.  Please consult with your advisor or admissions coach for the course availability each term at your campus or within your program modality. 
  • Certain options within the program may be required at certain campuses or for certain program modalities.  Please consult with your advisor or admissions coach for requirements at your campus or for your program modality. 

Complete all courses and requirements listed below unless otherwise indicated.

Required Courses 

MATH 5010Foundations of Statistical Theory and Probability4
MATH 6241Stochastic Processes2
MATH 6243Statistical Learning4
PHTH 6800Causal Inference in Public Health Research3-4
or PHTH 6801 Causal Inference 1
PHTH 6830Generalized Linear Models4

Concentrations

Complete one of the following concentrations:

Experiential Courses

Complete 2 semester hours from the following (courses may be repeated):2
Master's Project
Statistical Consultancy

Co-op (Optional)

Co-op Preparation
MATH 6000Professional Development for Co-op0
Co-op Work Experience
Statistical Machine Learning Concentration Students
Statistical machine learning students may take either course.
Biostatistics Concentration Students
HLTH 6964Co-op Work Experience0
Statistical Theory and Modeling Concentration Students
MATH 6964Co-op Work Experience0

Program Credit/GPA Requirements

31–32 total semester hours required 

Minimum 3.000 GPA required


Biostatistics Concentration (Bouvé College of Health Sciences)

Complete 12 semester hours from the following:12
Using SAS in Public Health Research
Advanced Methods in Biostatistics
Causal Inference 2
Survival Analysis
Design and Analysis of Clinical Trials

 Statistical Machine Learning Concentration (Khoury College of Computer Sciences)

CS 5100Foundations of Artificial Intelligence4
CS 6140Machine Learning4
DS 5110Introduction to Data Management and Processing4

Statistical Theory and Modeling Concentration (College of Science)

Complete 12 semester hours from the following:12
Machine Learning and Statistical Learning Theory 2
Statistics for Bioinformatics
Probability 2
Mathematical Statistics
Applied Statistics
Regression, ANOVA, and Design