The Master of Science in Bioinformatics seeks to provide students with core knowledge in bioinformatics programming, integrating knowledge from the biological, computational, and mathematical disciplines. Upon completion, students are equipped to apply bioinformatics and computational methods to biological problems. Students in the MS program have the opportunity to gain professional work experience via co-op.
The program consists of core coursework 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
Course List | Code | Title | Hours |
| BINF 6200 | Bioinformatics Programming | 4 |
| BINF 6310 | Introduction to Bioinformatics | 4 |
| MATH 7340 | Statistics for Bioinformatics | 4 |
| 1-2 |
| Ethics in Biological Research | |
| Bioethics in the Age of Artificial Intelligence | |
| EESC 6500 | Pathways to Professional Success | 1 |
Concentration or Electives Option
A concentration is not required. Students who choose not to declare a concentration will complete the electives option.
Optional Co-op Experience
Course List | Code | Title | Hours |
| BINF 6964 | Co-op Work Experience | 0 |
| or BINF 6965 | Co-op Work Experience Abroad |
Program Credit/GPA Requirements
32 total semester hours required
Minimum 3.000 GPA required
Concentration in Bioinformatics Enterprise
Course List | Code | Title | Hours |
| BIOT 5219 | The Biotechnology Enterprise | 2 |
| BIOT 5225 | Managing and Leading a Biotechnology Company | 3 |
| BIOT 5228 | Planning and Executing Biotechnology Projects | 3 |
| 6 |
| 4 |
Concentration in Biotechnology
Course List | Code | Title | Hours |
| BIOT 5120 | Foundations in Biotechnology | 3 |
| BIOT 5621 | Protein Principles in Biotechnology | 3 |
| BIOT 5750 | Molecular Approaches in Biotechnology | 3 |
| 5 |
| 4 |
Concentration in Data Analytics
Course List | Code | Title | Hours |
| DA 5020 | Collecting, Storing, and Retrieving Data | 4 |
| DA 5030 | Introduction to Data Mining/Machine Learning (or Elective) | 4 |
| 4 |
| Introduction to Computational Statistics | |
| Information Design and Visual Analytics | |
| 3 |
| 3 |
Concentration in Health Informatics
Course List | Code | Title | Hours |
| 9 |
| Introduction to Health Informatics and Health Information Systems | |
| Data Management in Healthcare | |
| The American Healthcare System | |
| Global Health Information Management | |
| Theoretical Foundations in Personal Health Informatics | |
| Database Design, Access, Modeling, and Security | |
| 5 |
| 4 |
Concentration in Omics
Course List | Code | Title | Hours |
| BINF 6400 | Genomics in Bioinformatics | 4 |
| BINF 6420 | Omics in Bioinformatics | 4 |
| BINF 6430 | Transcriptomics in Bioinformatics | 4 |
| 6 |
Electives Option
Course List | Code | Title | Hours |
| 10 |
| 8 |
Restricted Electives
Course List | Code | Title | Hours |
| Algorithmic Foundations in Bioinformatics | |
| Genomics in Bioinformatics | |
| Omics in Bioinformatics | |
| Transcriptomics in Bioinformatics | |
| Biology Colloquium | |
| Stem Cells and Regeneration | |
| Evolution | |
| Cell and Molecular Biology of Aging | |
| Molecular Cell Biology | |
| Foundations in Biotechnology | |
| Biotechnology Lab Skills | |
| The Biotechnology Enterprise | |
| Protein Principles in Biotechnology | |
| Molecular Approaches in Biotechnology | |
| Higher-Order Structure Analytics | |
| Protein Chemistry | |
| Molecular Modeling | |
| Object-Oriented Design | |
| Data Structures, Algorithms, and Their Applications within Computer Systems | |
| Programming Design Paradigm | |
| Foundations of Artificial Intelligence | |
| Database Management Systems | |
| Principles of Programming Language | |
| Foundations of Software Engineering | |
| Computer Systems | |
| Algorithms | |
| Natural Language Processing | |
| Machine Learning | |
| Information Retrieval | |
| Data Mining Techniques | |
| Collecting, Storing, and Retrieving Data | |
| Introduction to Data Mining/Machine Learning | |
| Data Science Engineering with Python | |
| Introduction to Programming for Data Science | |
| Introduction to Linear Algebra and Probability for Data Science | |
| Supervised Machine Learning and Learning Theory | |
| Unsupervised Machine Learning and Data Mining | |
| Population Dynamics | |
| Database Design, Access, Modeling, and Security | |
| Data Science Engineering Methods and Tools | |
| Introduction to Computational Statistics | |
| Information Design and Visual Analytics | |
| Introduction to Mathematical Methods and Modeling | |
| Algorithms for Optimization | |
| Machine Learning and Statistical Learning Theory 1 | |
| Machine Learning and Statistical Learning Theory 2 | |
| Mathematical Statistics | |
| Regression, ANOVA, and Design | |
| Bioethics in the Age of Artificial Intelligence | |
| Experimental Design and Biostatistics | |
Additional Electives
Course List | Code | Title | Hours |
| |
| Biomedical Imaging | |
| Cellular Engineering | |
| Medical Physiology | |
| Inventions in Microbial Biotechnology | |
| Medical Microbiology | |
| Biological Imaging | |
| Immunology | |
| Evolution | |
| Comparative Neurobiology | |
| Advanced Genomics | |
| Cell and Gene Therapies | |
| Biochemistry | |
| Neurobiology and Behavior | |
| Managing and Leading a Biotechnology Company | |
| Planning and Executing Biotechnology Projects | |
| Bioprocess Fundamentals | |
| Downstream Processes for Biopharmaceutical Production | |
| Drug Product Processes for Biopharmaceuticals | |
| Molecular Interactions of Proteins in Biopharmaceutical Formulations | |
| Cutting-Edge Applications in Molecular Biotechnology | |
| Cellular Therapies | |
| Cell and Gene Therapy Lab | |
| Biotechnology Applications Laboratory | |
| Special Topics in Biotechnology | |
| Introduction to Glycobiology and Glycoprotein Analysis | |
| Protein Mass Spectrometry Laboratory | |
| Web Development | |
| Fundamentals of Computer Networking | |
| Fundamentals of Cloud Computing | |
| Deep Learning | |
| Introduction to Health Informatics and Health Information Systems | |
| Data Management in Healthcare | |
| The American Healthcare System | |
| Global Health Information Management | |
| Theoretical Foundations in Personal Health Informatics | |
| Introduction to Computational Statistics | |
| Numerical Analysis 1 | |
| Numerical Analysis 2 | |
| Graph Theory | |
| Probability 1 | |
| Probability 2 | |
| Nanomedicine Research Techniques | |
| Repurposing Drugs for Cancer Immunotherapies | |
| Network Science 1 | |
| Network Science Data 2 | |
