The Center for Bioinformatics & Computational Biology (CBCB) offers these graduate level courses. Courses may be offered online, in-person, or as hybrid sessions.
Please use UD’s Courses Search tool to determine instruction mode and when courses are offered.
BINF601: Introduction to Data Sciences (Data Science I)
Instructors: Cecilia Arighi, Ryan Moore
Credits: 3
Description: The course introduces topics and fundamental skills needed for application of bioinformatics and data science across life science disciplines using biomedically-relevant examples. Topics will include fundamental computational skills, data preparation, FAIR data practices, basic concepts and applications of Omics and AI/ML in life sciences, and effective communication of results.
Note: This course is designed for non-majors or first-year Bioinformatics students
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.
BINF620: Big Data Analytics in Healthcare
Instructor: Zugui Zhang
Credits: 3
Description: 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.
BINF640: Databases for Bioinformatics
Instructor: Serdar Kuyuk
Credits: 3
Description: Introduces the basic concepts, components, and principles underlying the design, implementation, and use of database management systems. Coupling lectures and a modular, half-semester-long term project, students (i) Collect data from the designated websites and design a relational data model using MySQL Workbench; (ii) Implement a relational database and write SQL queries; and (iii) Understand and use NoSQL database. Upon completion of this course, students will have basic data modeling techniques, database management concepts, and SQL skills to develop a database supporting real-world applications.
BINF644: Bioinformatics
Instructor: Cecilia Arighi
Credits: 3
Description: This course will introduce the principles of bioinformatics analysis of genes and proteins and provide a practical introduction to a variety of bioinformatics resources and tools. Course consists of lectures, tutorials, hands–on exercises, quizzes and a term project.
BINF690: Programming for Bioinformatics
Instructor: Jaysheel Bhavsar
Credits: 3
Description: 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.
BINF694: Systems Biology I
Instructors: Shawn Polson, Cecilia Arighi
Credits: 3
Description: 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.
BINF695: Computational Systems Biology
Instructor: Abhyudai Singh
Credits: 3
Description: 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.
BINF667-014: Introduction to Biostatistics with Biomedical Applications
Instructor: Zugui Zhang
Credits: 3
Description: This course introduces the basic principles and methods of biostatistics, providing students a sound methodological foundation for biomedical and health care research. Students are exposed to basic statistics, including descriptive statistics, inference for means and proportions, and regression methods; study design, including sample size estimation and power calculation; biostatistical techniques, focusing on logistic regression, and survival analysis; along with advanced topics, such as statistical methods in observational studies.
BINF667-017: Team Science Experiential Learning
Instructor: Thomas Powers, Abhyudai Singh, Ryan Zurakowski,
Credits: 3
Ethics in Data Science and AI* – Dr. Powers
Description: This seminar will provide participation- and case-based ethics education to evaluate the ethical, social and policy impacts of data gathering, automated analysis, and applications in fields ranging from healthcare, public safety, and agriculture to homeland security, e-commerce, and the biological and environmental sciences.
*Students in Team Science Experiential Learning take first 3rd of Ethics in Data Science and AI (PHIL655)
Responsible Conduct of Research – Dr. Singh
Description: Covers the topics of responsible conduct of research, scientific rigor and reproducibility, ethics, diversity and equity. The course addresses: authorship and ownership, the sanctity of data (fabrication and falsification), data sharing, peer review, conflicts of interest, whistle-blowing: benefits and risks, mentor-trainee responsibilities, and collaborative science.
Interdisciplinary Research Practice – Dr. Zurakowski
Description: In this portion of the course, students will work as a group, and will develop an interdisciplinary mock research plan to address a topic proposed by either a program-affiliated faculty member or an industry partner. We will use this mock research plan to develop and discuss “soft” materials including cold e-mails to collaborators, Publication and Authorship Agreements, Communication and Collaboration Scheduling, Consortium and Contractual Agreements, Multiple PI/PD leadership plans, and Conflict Resolution. Lectures and discussions will focus on how to approach potential new collaborators, how to successfully communicate across different disciplines, how to manage communication, budgeting, and scheduling for multi-site projects, potential sources of conflict and how to preemptively avoid them when possible, and how to handle conflict effectively when it arises.
BINF667-018: Electronic Health Records (EHR) Data Science
Instructor: Arighi,Cecilia Noemi; Hossain,Md. Jobayer
Credits: 3
Description: This course is designed to empower students with the knowledge and skills to harness the potential of Electronic Health Records (EHR) data for data science applications within the healthcare domain. Electronic Health Records have become a critical source of information for healthcare providers and researchers, offering vast potential for improving patient care, healthcare management, and medical research. Throughout this course, students will acquire essential competencies in EHR data extraction, preprocessing, visualization, exploration, and modeling essentials. These skills are integral in extracting valuable insights to facilitate predictions and evidence-based decision-making in the healthcare sector, enabling students to uncover critical findings, make data-driven predictions, and facilitate evidence-based decision-making in healthcare.
BINF666: Special Problem
Instructor: Varies
Credits: 1-3
BINF864: Internship
Instructor: Varies
Credits: 3
Description: 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.)
BINF865: Bioinformatics Seminar
Instructor: Cecilia Arighi
Credits: 0-1
Description: 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.
Research
Open only to students in CBCB programs. Departmental permission required to register.
BINF868: Research
Instructor: Research Advisor
Credits: 1-6
Description: Research.
BINF887: Special Session Research
Instructor: Research Advisor
Credits: 1-6
Description: Research (Summer and Winter Sessions).
BINF869: Master’s Thesis
Instructor: Thesis Advisor
Credits: 1-6
Description: Independent research leading to the Master’s Thesis.
BINF964: Pre-Candidacy Study
Instructor: Doctoral Advisor
Credits: 1-12
Description: Pre-candidacy Study.
BINF969: Doctoral Dissertation
Instructor: Doctoral Advisor
Credits: 1-12
Description: Independent research leading to the Doctoral Dissertation.