MS - Biostatistics and Data Science

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Master of Science in Biostatistics and Data Science

Earn your Masters in Biostatistics and Data Science with our synergetic program. We prepare you to extract, analyze, and translate vast amounts of data into actionable evidence and communicate findings to collaborators from other disciplines by gaining knowledge in three emphasis areas, namely:

  • Biostatistics
  • Bioinformatics & Genomics
  • Data Science

Target audiences

Data Science students and Assistant Professor, Dr. Lirette

  • Mathematicians
  • Statisticians
  • Biologists
  • Computer scientists
  • Engineers
  • Bioinformaticians
  • Math-intensive field professionals who have completed training in calculus (through multivariable integration and differentiation) and linear algebra.

 

Primary objective

To graduate leaders in Statistical theory, Practical data analysis, Big data management and manipulation, and Communication skills
All biostatisticians and data scientists must master these competencies in order to support basic science, clinical, and population health studies.

Through supervised consulting sessions, an internship, and directed research, students will develop the technical and collaborative skills necessary to excel in clinical, academic, industrial, government, and population health work organizations. Students will have ample opportunities to work with high-quality data and reputable researchers from two epidemiologic studies supported by the National Institutes of Health. The Jackson Heart Study (JHS) is the largest ever single-site study of cardiovascular disease and its causes in African-Americans. The Atherosclerosis Risk in Communities study (ARIC) is designed to investigate the causes of atherosclerosis and its clinical outcomes, as well as the variation in cardiovascular risk factors and disease by race, gender, and location.

You can complete your Master's in 2 years (42 credit hours)

 

Graduates of the program will be able to:

  • Efficiently collect, clean, organize, and appropriately analyze biomedical, clinical, and population health data;
  • Use standard statistical (R, SAS, and Stata) and computer (Python) programming languages to reproducibly explore and visualize data, fit models, conduct inference, and translate analysis results;
  • Conduct all facets of big data analysis, including the extraction, storage, manipulation, and analysis of massive genetic and bioinformatics datasets;
  • Convert information contained in databases and data warehouses into actionable findings using machine learning and other data science techniques;
  • Adhere to rigorous ethical and methodological standards when analyzing real-world data;
  • Collaborate with non-statisticians and communicate findings to the scientific and general community to improve health care and prevent disease.