The following requirements must be fulfilled:
The general requirements stated under Columbian College of Arts and Sciences, Graduate Programs.
33 credits, including 27 credits in required courses and 6 credits in elective courses, and successful completion of a master's comprehensive examination.
The following requirements must be fulfilled: 33 credits, including 18 credits in statistics courses, 7 credits in public health courses, 6 credits in elective courses, 2 credits in consulting, and successful completion of a master's comprehensive examination.
|The courses listed below (or equivalents) are prerequisites for admission consideration and must appear on the student's transcript. Students may apply to the program only after they have fulfilled this requirement:
|Single-Variable Calculus I
|Single-Variable Calculus II
Applicants lacking the courses listed below (or equivalents) are considered for admission; however, if admitted, the student is required to complete these courses within two semesters of matriculation in the program. Credit earned in these courses does not count toward the 33 credits required for the degree and grades earned are not reflected in the overall grade-point average.
|Linear Algebra I
|One of the following:
|Use of Statistical Packages for Data Management and Data Analysis *
|Intermediate Statistics Lab/Packages
|Required for the degree
|or PUBH 8877
|Generalized Linear Models in Biostatistics
|Mathematical Statistics I
|Mathematical Statistics II
|or PUBH 6866
|Principles of Clinical Trials
|Public health courses
|Principles and Practices of Epidemiology
|And two courses (2 credits) selected from the following:
|Introduction to Geographic Information Systems
|Introduction to SAS for Public Health Research
|Introduction to R for Public Health Research
|Introduction to Python for Public Health Research
|Advanced SAS for Public Health Research
|And 2 credits in any PUBH course(s) in the 6800 range.
|6 credits in elective courses selected from the following:
|Applied Computing in Health Data Science
|High Performance and Cloud Computing
|Principles of Bioinformatics
|Public Health Genomics
|Applied Linear Regression Analysis for Public Health Research
|Applied Categorical Data Analysis for Public Health Research
|Propensity Score Methods for Causal Inference in Observational Studies
|Bioinformatics Algorithms and Data Structures
|Statistical and Machine Learning for Public Health Research
|Applied Longitudinal Data Analysis for Public Health Research
|Introduction to Sampling
|Applied Time Series Analysis
|Nonparametric Statistics Inference
|Fundamentals of SAS Programming for Data Management
|Applied Linear Models
|Applied Multivariate Analysis I
|Applied Multivariate Analysis II
|Design of Experiments
|Bayesian Statistics: Theory and Applications
|Longitudinal Data Analysis
|Categorical Data Analysis
|Statistical Data Mining
|Modern Regression Analysis
|Statistical Methods in Bioinformatics and Computational Biology
|Topics in Statistics
|Advanced Biostatistical Methods
|Stochastic Processes I
|Advanced Time Series Analysis
|Topics in Sample Surveys
|Biostatistics Consulting Practicum
|Principles of Biostatistical Consulting
|Master's comprehensive examination
|Students must successfully complete a master's comprehensive examination, a written examination in the field of biostatistics based on the material covered in PUBH 6266 or PUBH 8877. The examination is administered by the faculty of the Department of Biostatistics and Bioinformatics in the Milken Institute School of Public Health.
Visit the program website for additional information.
Admission to this program is not being offered at this time. Related programs in the field are offered by the Milken Institute School of Public Health.