Development of an informatics project, including research question, literature review, and assessment.

Health Informatics / Biomedical Informatics is the information as studied in or applied to biomedical science, healthcare, and public health. Thus, its focus is on the use of informatics methods to transform data into actionable knowledge within the broad context of health, from basic science, to public health.

Clinical Data Science provides students with an introduction to a broad range of concepts and methods in data science, as they pertain to biomedical research. The focus of the class is on introducing key methods ranging from data collection and storage, to probabilistic methods, etc.

This course covers the fundamental issues of bioinformatics and how they apply to translational and clinical problems. The course is organized in 4 parts: sequence analysis, databases and ontologies, genome-wide association and linkage analysis, and networks.

This course provides students with a broad exposure to concepts, policies, and methodologies in security and privacy, as they pertain to healthcare research and practice. Information security and data privacy are essential components of biomedical and clinical research, and therefore, it is critical for students to understand security guidelines.

This course provides students with essential concepts of ontologies, building ontologies, and knowledge representation as they pertain to health care, and biomedical research. With the ubiquitous nature of computer systems, and information-based systems in health care (and everywhere!) there is a critical need to be able to represent information.

The objective of this course is to present a broad overview of methodologies to automatically analyze and mine biomedical text automatically. Students will be exposed to some of the common and state-of-the-art software, algorithms and techniques to extract content and knowledge from biomedical texts.