Complete all courses and requirements listed below unless otherwise indicated.
Core Requirements
| Code | Title | Hours |
|---|---|---|
| BINF 6200 | Bioinformatics Programming | 4 |
| BINF 6310 | Introduction to Bioinformatics | 4 |
| BINF 6400 | Genomics in Bioinformatics | 4 |
| or BINF 6430 | Transcriptomics in Bioinformatics | |
Elective
| Code | Title | Hours |
|---|---|---|
| Complete one of the following (electives outside this list may be chosen in consultation with faculty advisor): | 3-4 | |
| Algorithmic Foundations in Bioinformatics | ||
| Genomics in Bioinformatics | ||
| Omics in Bioinformatics | ||
| Transcriptomics in Bioinformatics | ||
| Biomedical Imaging | ||
| Cellular Engineering | ||
| Principles of Bioengineering | ||
| Medical Physiology | ||
| Biology Colloquium | ||
| Stem Cells and Regeneration | ||
| Inventions in Microbial Biotechnology | ||
| Medical Microbiology | ||
| Biological Imaging | ||
| Immunology | ||
| Evolution | ||
| Advanced Genomics | ||
| Cell and Molecular Biology of Aging | ||
| Biochemistry | ||
| Molecular Cell Biology | ||
| Neurobiology and Behavior | ||
| Prokaryotic Cell and Molecular Biology | ||
| Biochemistry for Molecular Biologists | ||
| Foundations in Biotechnology | ||
| Biotechnology Lab Skills | ||
| The Biotechnology Enterprise | ||
| 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 | ||
| Regulatory Landscape of Cell and Gene Therapies | ||
| Cell and Gene Therapy Lab | ||
| Higher-Order Structure Analytics | ||
| Biotechnology Applications Laboratory | ||
| Special Topics in Biotechnology | ||
| Introduction to Glycobiology and Glycoprotein Analysis | ||
| Protein Mass Spectrometry Laboratory | ||
| Protein Chemistry | ||
| Molecular Modeling | ||
| Object-Oriented Design | ||
| Data Structures, Algorithms, and Their Applications within Computer Systems | ||
| Programming Design Paradigm | ||
| Foundations of Artificial Intelligence | ||
| Applied Programming and Data Processing for AI | ||
| Database Management Systems | ||
| Principles of Programming Language | ||
| Foundations of Software Engineering | ||
| Computer Systems | ||
| Web Development | ||
| Fundamentals of Computer Networking | ||
| Algorithms | ||
| Machine Learning | ||
| Information Retrieval | ||
| Data Mining Techniques | ||
| Fundamentals of Cloud Computing | ||
| Deep Learning | ||
| 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 | ||
| 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 | ||
| Data Science Engineering Methods and Tools | ||
| Introduction to Computational Statistics | ||
| Information Design and Visual Analytics | ||
| Introduction to Mathematical Methods and Modeling | ||
| Numerical Analysis 1 | ||
| Numerical Analysis 2 | ||
| Algorithms for Optimization | ||
| Graph Theory | ||
| Probability 1 | ||
| Statistics for Bioinformatics | ||
| Probability 2 | ||
| Mathematical Statistics | ||
| Regression, ANOVA, and Design | ||
| Nanomedicine Seminar | ||
| Bioethics in the Age of Artificial Intelligence | ||
| Nanomedicine Research Techniques | ||
| Repurposing Drugs for Cancer Immunotherapies | ||
| Artificial Intelligence and Machine Learning in Drug Discovery: Concepts and Applications | ||
| Experimental Design and Biostatistics | ||
| Network Science 1 | ||
| Network Science Data 2 | ||
Program Credit/GPA Requirements
15–16 total semester hours required
Minimum 3.000 GPA required