Northeastern University

Overview

The Master of Science in Bioinformatics at Northeastern University’s Portland Campus offers a unique blend of life sciences and advanced computational techniques. This program is designed to equip students with the skills necessary to analyze extensive biological data, preparing them for successful careers in both research and industry. With a hybrid study mode, students can benefit from both in-person and online learning experiences over a typical duration of 24 months.

What you'll study

The curriculum encompasses a variety of core topics in bioinformatics, data analytics, and the biological interpretation of computational findings. Students have the option to specialize in specific areas or choose electives that align with their career aspirations.

Core learning areas

• Biomedical Imaging
• Cellular Engineering
• Medical Physiology
• Stem Cells and Regeneration
• Innovations in Microbial Biotechnology

Additional subjects typically included

• Genomics and transcriptomics
• Proteomics and omics data analysis
• Data management, mining, and statistical analysis
• Machine learning applications for biological datasets
• Visualization and communication of scientific results

Capstone / project: The final assessment format may vary; students should confirm whether it involves a capstone project, thesis, or project-based coursework with the program team.

Entry requirements

While specific GRE, GMAT, or GPA requirements are not stated, applicants should possess a relevant bachelor’s degree, ideally in life sciences or quantitative fields such as biology, biotechnology, computer science, mathematics, or statistics. Candidates with different academic backgrounds may be required to demonstrate their readiness through prior coursework or supplementary documentation.

Career prospects

Graduates of the Bioinformatics program are well-prepared for various roles in the biotechnology and healthcare sectors. They may pursue careers in data analysis, research, and development, among other opportunities, leveraging their expertise in both biological sciences and computational methods. The program’s experiential learning model also provides practical experience, further enhancing employability in a competitive job market.