MS - Biostatistics and Data Science

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MS - Biostatistics and Data Science

The Master of Science (MS) program in Biostatistics & Data Science will prepare graduates to extract, analyze, and translate vast amounts of data into actionable evidence and communicate findings to collaborators from other disciplines. This program synergizes competencies in statistics, computer science, and epidemiology, an important combination of skills for analyzing increasingly complex health-related data. The target audiences include college graduates and professionals from mathematics, statistics, biology, computer science, or math-intensive fields (e.g. engineering) who have completed training in calculus (through multivariable integration and differentiation) and linear algebra. Enrolled students can complete the MS degree program in 2 years, earning a total of 42 credit hours.

The primary objective of the program is to educate students on statistical theory, practical data analysis, big data management and manipulation, and communication to the scientific and general community. All biostatisticians and data scientists must master these competencies in order to support basic science, clinical, and population health studies. Students will gain knowledge in three emphasis areas, namely: 1) Biostatistics; 2) Bioinformatics & Genomics; and 3) Data Science. 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.

Program goals

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.

Admission criteria

Admission into the Bower School of Population Health is based upon a baccalaureate degree in a relevant scientific discipline, past academic performance in undergraduate and graduate (if applicable) degree programs (prefer a grade point average ≥ 3.0 on a 4.0 scale), official scores on the Graduate Record Examination (GRE; prefer ≥ 295 on the combined verbal and quantitative sections), three letters of recommendation, and a personal statement. Applicants whose native language is not English and/or who have completed their tertiary education primarily outside of the USA must demonstrate proficiency in written and spoken English through the Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS), or Pearson Test of English-Academic (PTE-A). This requirement may be waived for students who are currently enrolled at a college or university in the United States and/or who demonstrate a proficiency in written and spoken English following a personal interview. Students matriculating into the MS program in Biostatistics & Data Science must have documented training in calculus (covering multiple integration and differentiation) and linear algebra. Additional training in statistical or computer programming languages is preferred.