The Center for Bioinformatics & Computational Biology (CBCB) offers these graduate level online (section 194) and hybrid (section 010) courses.

BINF601:Introduction to Data Science (Data Science I)
Instructors: Cecilia Arighi, Shawn Polson
Credits: 3

Survey of data science topics focusing on developing skills and principles required to extract, clean, and manage big data. Apply data analytics to real-world clinical and/or healthcare data to achieve best outcomes in the field of data science.

BINF644: Bioinformatics
Instructor: Cecilia Arighi
Credits: 3

Description: Principles of molecular sequence analysis and genome annotation with practical guides to bioinformatics resources, databases and tools.


BINF694: Systems Biology I
Instructor: Shawn Polson, Karen Ross
Credits: 3

This course couples lectures and hands-on exercises to introduce students to experimental methods and bioinformatics analysis in systems biology, showing how global analysis of omics data improves understanding of biological systems. This course has three units: (i) experimental techniques; (ii) genomics and transcriptomics data analysis; and (iii) proteomics and pathway/network data analysis

BINF690: Programming for Bioinformatics
Instructor: Jaysheel Bhavsar
Credits: 3

This course examines principles of computer programming using Python language. Explores basic technique, syntax, best practices, advance programming concepts and basic algorithm designs through series of lectures, assignments and projects framed within the context of bioinformatics. Designed to teach Python to all levels, from beginner to experienced programmer.

BINF640: Database for Bioinformatics
Instructor: Serdar Kuyuk
Credits: 3

Principles and techniques of bioinformatics database development cycle from data modeling to relational database management and web hosting.

BINF610: Applied Machine Learning
Instructor: Li Liao
Credits: 3

Description: This course introduces students to the basic concepts to understand machine learning principles and paradigms and equips them with knowledge of machine learning pragmatics that are involved in a complete learning process from formulating a problem to choosing the appropriate techniques/tools to evaluating the results.

BINF630: Big Data Analytics in Healthcare
Instructors: Zugui Zhang
Credits: 3

Big data analytics has the potential to transform the way healthcare providers use sophisticated technologies to gain insight from their clinical and other data repositories and make informed decisions. This course will introduce students to detect risk factors, find patterns and reason about data, make causal inference and decision about health care and precision medicine.

BINF695: Computational Systems Biology
Instructors: Abhi Singh
Credits: 3

Computational/mathematical techniques for modeling & analysis of biological systems. Includes properties of gene-regulatory and signaling networks; network reconstruction from data; stochastic modeling to study cellular variation & physiological modeling.

BINF666: Special Problems
Credits: 1-3

BINF864: Internship
Credits: 3

Supervised, on-the-job experience on specialized topics in bioinformatics data science or industry research and analysis related to bioinformatics data science. Topics range from bioinformatics and data science methods, tool and database development to application of biomedical informatic approaches to biotechnology and medicine. Industry research and analysis include topics on product development, project or operations management, and ethical, legal and regulatory affairs. The practical learning experience will require two written reports 1) a plan of work outlining the background of the project and the learning objectives for the internship and 2) a scholarly report outlining the objectives of internship, what was accomplished on each objective, recommendations for future work, and literature citation. (Prior written approval from Primary Research Advisor for MS Thesis and PhD students.)

BINF815: Ethics, Business and Communication

Provides professional development in ethics, business and communication related to systems biology research applications in stem cell, tissue engineering, and drug delivery technologies. Intended for PhD students in interdisciplinary life science and engineering programs.

BINF865: Bioinformatics Seminar
Instructors: Karen Hoober
Credits: 0-1

Lectures and discussions by guest speakers, faculty, and students on specialized topics and cutting-edge developments in bioinformatics, computational biology, biomedical informatics, and data science.

PHIL655: Ethics in Data Science and Artificial Intelligence
Instructor: Thomas Powers
Credits: 3 (UAPP655 1 Credit)

Seminar on societal impacts of data gathering and analysis, with applications in health sciences, disaster science, policing, and e-commerce. Participation-based format. Topics include privacy, algorithmic biases and data incompleteness, profiling, safety, and informed consent.

BINF667-011 Data Science with Online Competitions
Credits: 1

Introduce students to online data science competitions on Kaggle. Students will participate in various online competitions on Kaggle and get hands-on experience with applied machine learning.

BINF667-016: Big Data in Social, Behavioral and Health Sciences
Instructor: Fang Fang Chen
Credits: 1

Big Data such as social media and electronic health records, present unprecedented opportunities for social, behavioral, and health sciences. This emerging field has generated innovative ways of collecting data, developed cutting edge analytical and statistical techniques, and provided novel approaches of visualizing and presenting information. The objective of the course is to familiarize students with big data analysis as a tool for addressing substantive research questions.

BINF667-014: Introduction to Biostatistics- biomedical applications
Instructor: Zugui Zhang
Credits: 3

This course introduces the basic principles and methods of biostatistics, providing students a sound methodological foundation for biomedical and healthcare research. Students will learn the fundamentals of descriptive and inferential statistics, study design, regression, and survival analysis; present results in graphical display; and summarize conclusions in the biomedical context.

BINF869: Master’s Thesis
Credits: 1-6

Independent research leading to the Master’s Thesis.

BINF868: Research
Credits: 1-6


BINF964: Pre-Candidacy Study
Credits: 1-12

Pre-candidacy Study.

BINF969: Pre-Candidacy Study
Credits: 1-12

Doctoral Dissertation.