Frequently Asked Questions


How does this new program differ from the two-year 80-credit masters programs (SM2) offered in Biostatistics and Epidemiology?


All three programs are complementary and each is designed to provide students with a great degree of flexibility in choosing courses to develop specific competencies. The new program, however, has a strong focus on the analysis and interpretation of complex genomic data. Working with their advisor, students design an individual program drawing from a broad and often overlapping selection of required and elective courses.

Strengths and weaknesses of the undergraduate record, work experience, career goals, and future educational plans will normally inform an applicant’s choice in selecting the degree program that is right for them. And as a student’s interests evolve, in some cases he or she may petition to transfer between programs in their second year.

One key difference is that the new SM2 in Computational Biology and Quantitative Genetics requires Collaborative Research Thesis experience. Consequently, there are fewer course requirements for this program — 60 course credits vs. 80 for the SM2 in Biostatistics and 75-80 for the SM2 in Epidemiology. Instead, the focus here is on giving students practical experience that is likely to be of interest to potential employers.


Explain the specific differences between:

  • The SM2 in Computational Biology and Quantitative Genomics
  • The SM2 in Epidemiology with a Specialization in Genetic Epidemiology and Statistical Genetics
  • The SM2 in Biostatistics with an Area of Interest in Bioinformatics


Again, there is a good deal of potential overlap between these programs, so specific interests and future plans will guide the student to a particular program. The following table summarizes degree requirements for the three programs.

Entrance Requirements Required Coursework Thesis

SM2 in Computational Biology and Quantitative Genetics

Bachelor’s degree in the mathematical sciences or allied fields (biology, psychology, economics, etc.). Successful completion of calculus through partial differentiation and multivariable integration, one semester of linear algebra or matrix methods, and either a two-semester sequence in probability and statistics or a two-semester sequence in applied statistics.

Applicants should have at least one semester of training in biology, with some familiarity with molecular biology and genetics. Practical knowledge of computer scripting and programming as well as experience with a statistical computing package such as R are highly desirable. Applicants are encouraged to have completed other courses in quantitative areas and in areas of application in the biological sciences. Additional research or work experience is considered beneficial, but not required.

55 credits total
15-22.5 credits in biostatistics, depending on track selected

5-12.5 credits in epidemiology, depending on track selected

20-35 credits in elective courses

10-20 credit
Collaborative Research Thesis

SM2 in Biostatistics
Area of Interest in Bioinformatics

Bachelor’s degree. Successful completion of calculus through multivariable integration and one semester of linear algebra. Knowledge of a programming language is required. In addition, all applicants are strongly encouraged to have completed courses in probability, statistics, advanced calculus or real analysis, and numerical analysis.

80 credits total
30 required credits in Biostatistics

5 required credits in Epidemiology

45 credits in elective courses

No thesis required

SM2 in Epidemiology
Specialization in Genetic Epidemiology and Statistical Genetics

Bachelor’s degree and have a strong background in relevant disciplines (e.g., biology, chemistry, genetics, physiology, bioengineering, and related social and computational sciences) and mathematics, excellent quantitative GRE scores and clear research goals.

75 credits total
30 required credits in Epidemiology

15 required credits in Biostatistics

30 credits in elective courses

5 credit thesis required



How does this program differ from masters programs in Computational Biology and/or Bioinformatics programs offered at other schools?


This program is designed to provide balanced training in genomics and bioinformatics, but with a strong emphasis on developing a rigorous understanding of the quantitative methods used in genomic data analysis. The Collaborative Research Thesis requirement is also unique, allowing our students to undertake high-profile research in the Harvard-affiliated hospitals and other premier institutions with which our world-renowned faculty collaborate.  Listen to Program Director John Quackenbush discuss the focus of this program.