Add your name to our mailing list now and apply this fall to join the first class of the new Master of Science (SM) in Computational Biology and Quantitative Genetics program at Harvard School of Public Health.
Candidates for admission to the SM in Computational Biology and Quantitative Genetics program should have successfully completed the following:
- An undergraduate degree in mathematical sciences or allied fields (e.g. biology, psychology, economics)
- Calculus through partial differentiation and multivariable integration
- One semester of linear algebra or matrix methods
- Either a two-semester sequence in probability and statistics or a two-semester sequence in applied statistics
- At least one semester of training in biology, with some familiarity with molecular biology and genetics.
- Applicants are also encouraged to have completed other courses in quantitative areas and in areas of application in the biological sciences.
- Practical knowledge of computer scripting and programming as well as experience with a statistical computing package such as R is highly desirable.
- Additional research or work experience is beneficial, but not required.
- Applicants should show excellence in written and spoken English.
Evidence that these requirements have been fulfilled should form part of the application.
Applying to the Program in 2013 (see update below)
For administrative purposes, applicants who seek to matriculate in the fall of 2014 must apply to the two-year 80-credit SM (SM2) program in either the Department of Biostatistics or Epidemiology, and select the “interdisciplinary concentration in computational biology and quantitative genetics.” Admissions will contact anyone who selects that interdisciplinary concentration to inquire whether they would like to be switched to the 80-credit SM.
Be sure to address the admissions requirements outlined in the section on Prerequisites for the new degree.
Updated on November 8, 2013:
Applicants may now apply directly to the Master of Science (SM) 80-credits – Computational Biology and Quantitative Genetics.