Bioinformatics, MS

The Master of Science (MS) in Bioinformatics seeks to provide students with core knowledge in bioinformatics programming, integrating knowledge from the biological, computational, and mathematical disciplines.  Students in the MS program gain professional work experience via co-op.  The program offers students an opportunity to become equipped to apply bioinformatics and computational methods to biological problems.

The program entails a required core of course work in computational methods, programming, and statistics, enhanced by electives in molecular biology, biochemistry, molecular modeling, web development, database design and management, data mining, and other related topics.

Complete all courses and requirements listed below unless otherwise indicated.

Core Requirements

Computational Methods
BINF 6308Bioinformatics Computational Methods 14
BINF 6309Bioinformatics Computational Methods 24
Research and Seminar
BIOL 6381Ethics in Biological Research2
BINF 7385Bioinformatics Seminar2
Statistics and Programming
BINF 6200Bioinformatics Programming4
MATH 7340Statistics for Bioinformatics4
Co-op
BINF 6964Co-op Work Experience0

Electives

Complete 12 semester hours from the following. Electives outside this list may be chosen in consultation with faculty advisor. 12
Stem Cells and Regeneration
Microbial Biotechnology
Advanced Microbiology
Microbial Ecology
Medical Microbiology
Biological Imaging
Immunology
Evolution
Comparative Neurobiology
Advanced Genomics
Cell and Molecular Biology of Aging
Molecular Cell Biology for Biotechnology
Biochemistry
Molecular Cell Biology
Neurobiology and Behavior
Dynamics of Microbial Ecology
Biochemistry for Molecular Biologists
The Biotechnology Enterprise
Molecular Modeling
Programming Design Paradigm
Foundations of Artificial Intelligence
Database Management Systems
Principles of Programming Language
Managing Software Development
Computer Systems
Web Development
Fundamentals of Computer Networking
Algorithms
Machine Learning
Information Retrieval
Data Mining Techniques
Collecting, Storing, and Retrieving Data
Introduction to Data Mining/Machine Learning
Ecological Dynamics
Basics and Probability and Statistics
Topology 1
Geometry 1
Introduction to Mathematical Methods and Modeling
Complex Networks and Applications
Network Science Data
Introduction to Computational Statistics
Information Design and Visual Analytics

Program Credit/GPA Requirements

32 total semester hours required
Minimum 3.000 GPA required