MS - SCG Concentration

The MS in Biostatistics with area of concentration in Statistical and Computational Genomics is designed for students with a background in math, some experience with programming, and a strong interest in public health and genomics. The SCG concentration emphasizes biostatistical theory and statistical computational methods for analyzing and interpreting ‘omics data so that students are prepared to be effective statistical collaborators in interdisciplinary studies; and lead the design and execution of studies.

Biostatistics and SCG Careers

Statistical and computational genetics is an exciting, rapidly changing, and interdisciplinary profession. Trained statistical and computational geneticists are in high demand.In fact, the U.S. Department of Labor’s O*NET OnLine projects the job growth for bioinformatics scientists nationwide to be 5-9%.

Here are just a few employers with open positions for statistical geneticists, genomic data scientists or bioinformaticians in a recent search on Indeed, Glassdoor, and ZipRecruiter:

  • 23andMe
  • Regeneron Pharmacueticals
  • University of Pittsburgh Medical Center
  • GeneDx
  • New York Genome Center
  • Signature Science
  • AgBiome
  • NIH Bioinformatics and Microbiome Center

Program Information

MS-SCG Schedule (PDF, 2023-24)
MS-SCG Degree Requirements Worksheet (PDF, 2023-24)
Student Handbook (PDF, 2023-24)


SCG Concentration-specific Competencies
  • 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 R software for processing and analysis of ‘omics-type data
  • Apply specialized statistical, bioinformatics, and computational methods for analysis of 'omics data and interpret the results
  • Apply methods of statistical learning, including dimension reductions, clustering, and subgroup analysis to visualize and analyze 'omics-type data
  • Apply advanced methods in at least three major areas of genomics