Master of Science in Biostatistics

The MS in Biostatistics is an academic degree program for students with a background in math and a strong interest in public health. The program emphasizes statistical theory and methods so that students are prepared to be effective statistical collaborators in interdisciplinary studies; and lead the design and execution of studies. 

Program Information 2024-25

(PDFs) 
MS Two-year Schedule 
MS Eighteen-month Schedule 
MS Degree Requirements Worksheet
Student Handbook

Admissions

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MS Competencies

Graduates will be able to:

  • Identify appropriate problem definitions, study designs and data collection methods to address public health problems

     
  • Utilize fundamental theoretical concepts and relationships to effectively apply and interpret common statistical inference techniques

     
  • Use common biostatistical inference techniques and regression models to analyze data and interpret the results for public health practice
  • Recognize strengths and weaknesses of approaches, including alternative designs, data sources and analytic methods

     
  • Communicate the meaning, potential and results of biostatistical analyses to potential collaborators with varying degrees of statistical knowledge

     
  • Effectively use SAS software for advanced data management and in-depth statistical analysis
  • Appropriately utilize generalized linear models to analyze clustered and longitudinal data (binary/continuous/count) applicable to health sciences

     
  • Derive and interpret fundamental quantities and statistics from various survival analysis models; perform the analysis and interpret the results from nonparametric, parametric, and semiparametric survival models

     
  • Apply advanced methods or theories in at least three major areas of classical biostatistics

Degree Requirements

40 credits of coursework in:

  • Fundamentals of statistical theory, applications, epidemiology and public health
  • Software for data management and statistical analysis (SAS and electives)
  • Analysis of correlated and longitudinal data
  • Survival analysis
  • Advanced methods in several major areas of classical biostatistics

Finalized with a statistical consulting practicum and MS thesis project.

Jeanine Buchanich lecturing students

Careers

Recent graduates hold the following positions:

  • Opioid compliance specialist, Georgia Dept. of Public Health
  • Biostatistician, University of Pittsburgh Dept. of Critical Care Medicine
  • Data-analyst, DeFiner
  • Biostatistician, Ichan School of Medicine at Mt. Sinai
  • Statistician, Mayo Clinic
  • Business analyst, PNC Financial Services
  • Data analyst, University of Pittsburgh School of Pharmacy
  • Biostatistician, Cleveland Clinic
  • Statistician, University of Pittsburgh School of Social Work
  • Actuarial analyst, Highmark Health
  • Biostatistician, University of Pittsburgh Dept. of Orthopedic Surgery