Bioinformatics and Computational Biology - Master of Professional Studies | College of Computer, Mathematical, and Natural Sciences

Engage in cutting-edge learning with the Master of Professional Studies in Bioinformatics and Computational Biology.

The MPS in Bioinformatics and Computational Biology provides education in the theory and practice of the major current areas in the field including problem contexts, mathematical and statistical foundations, computational approaches, communication, and ethical, privacy and legal considerations. In addition to the fundamentals of bioinformatics and computational biology, the program covers relevant probability and statistics, data structures and algorithms, and machine learning. Students who successfully complete the MPS in Bioinformatics and Computational Biology will be able to identify, choose, describe, explain, and apply bioinformatics and computational biology methods to problems in biology and biomedical research. The program is offered through the Science Academy in the College of Computer, Mathematical, and Natural Sciences.

The MPS in Bioinformatics and Computational Biology is a 30-credit graduate program designed for working professionals and can be completed in less than two years. Instruction is provided by UMD faculty and experts in the field. The program features instructional delivery through face-to-face instruction at the UMD College Park campus.

Application Deadlines

Fall 2023:
Domestic Students: June 30, 2023

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Admission Requirements

Any student applying for admission to a graduate program at the University of Maryland must meet the following minimum admission criteria as established by the Graduate School.

Earned a four-year baccalaureate degree from a regionally accredited U.S. institution, or an equivalent degree from a non-U.S. institution.
Earned a 3.0 GPA (on a 4.0 scale) in all prior undergraduate and graduate coursework.

General Requirements:

Statement of Purpose
Transcript(s)
TOEFL/IELTS/PTE (international graduate students)

Program-Specific Requirements:

Graduate Record Examination (GRE) (optional)
CV/Resume
Description of research/work experience
Prior coursework establishing quantitative ability (including calculus II, linear algebra, statistics, etc.)
Education or work experience in biological/biomedical sciences, mathematics, statistics and/or computer science fields
Proficiency in programming languages, demonstrated either through prior programming coursework or substantial software development experience

Plan of Study & Courses

The MPS in Bioinformatics and Computational Biology is a 30-credit, 10-course, non-thesis graduate program. The program provides education in the theory and practice of the major current areas in the bioinformatics and computational biology fields. Elective courses in the Data Science and Machine Learning programs may also be available.

Sample Plan of Study (Part-time, two 3-credit courses per semester)

Semester 1 (fall)	BIOI601 Probability & Statistics (Core) BIOI604 Principles of Molecular Biology, Genetics, and Genomics (Core)
Semester 2 (spring)	BIOI605 Data Sources and Data Management in Bioinformatics (Core) BIOI606 Sequence Alignment (Core)
Semester 3 (summer)	BIOI610 Genome Annotation (Elective)
Semester 4 (fall)	BIOI602 Principles of Data Science (Core) BIOI603 Principles of Machine Learning (Core)
Semester 5 (spring)	BIOI607 Data Structures and Algorithms for Bioinformatics (Core) BIOI621 Genome Assembly and Annotation (Elective)
Semester 6 (summer)	No classes
Semester 7 (fall)	BIOI611 Analysis of Gene Expression Data (Core)

Sample Plan of Study (Full-time, three 3-credit courses per semester)

Semester 1 (fall)	BIOI601 Probability & Statistics (Core) BIOI602 Principles of Data Science (Core) BIOI604 Principles of Molecular Biology, Genetics, and Genomics (Core)
Semester 2 (spring)	BIOI605 Data Sources and Data Management in Bioinformatics (Core) BIOI606 Sequence Alignment (Core) BIOI607 Data Structures and Algorithms for Bioinformatics(Core)
Semester 3 (summer)	BIOI610 Genome Annotation (Elective)
Semester 4 (fall)	BIOI603 Principles of Machine Learning (Core) BIOI611 Analysis of Gene Expression Data (Core) BIOI621 Genome Assembly and Annotation (Elective)