Computational Life Sciences MS

The MS program in computational life sciences introduces students to a burgeoning new field. Huge leaps in processing technologies have thrown open the doors for new research techniques and exciting opportunities for interdisciplinary collaborations, focusing heavily here on genomics data generation, analysis and interpretation.

Students are introduced to a suite of statistical tools and computational approaches that enable them to uncover correlations, glean new understanding, and help solve scientific problems.

Students examine many different types of data generated from a wide range of fields, including ecology, botany, evolutionary biology, neuroscience, molecular and cellular biology, and animal behavior. Students have the opportunity to investigate topics such as DNA, RNA, protein, imaging, conservation and even historical data from long-term ecological research sites.

Finally, students explore the ethical implications of collecting, analyzing and sharing the results of computational life sciences data.

The current degree requirements are below. Remember you are required to fulfill the requirements from the academic year you were admitted. Please refer to the handbook from your year of admission as needed.

Required Core (1 credit hour)

BIO 511 Big Data in Context: Ethics, Policy, History and Philosophy

OR

BIO 610 Introduction to Responsible Conduct of Research (RCR) in Life Sciences

Restricted Electives Section (18-20 credit hours)

Refer to the CLS MS Course Offering Sheet below for the list of course options that fit into each area.

• Computing in Life Sciences (6-7 credit hours)

• Statistics and Mathematics (6-7 credit hours)

• Biology (6 credit hours)

*We highly recommend that students take a computing in life science restricted elective and a biology restricted elective in their first semester. 

Open Electives Section (6-8 credit hours)

Refer to the CLS MS Course Offering Sheet below for approved open elective options. These can include classes listed under the restricted electives.

Culminating Experience Section (3 credit hours)

 BIO 597 - Capstone (default)

• • • A capstone is best for more open-ended writing tasks; for example, performing a literature review of computational software available for a particular type of analysis and writing a description/evaluation of this literature. 

BIO 593 - Applied Project

• • • An applied project is best when you will apply methods learned during the degree to some well-defined problem; for example, carrying out a certain suite of computational analyses to a dataset. (A formal proposal and Program Director approval is required well ahead of your final semester)