This course offers students a broad overview of health informatics as a foundation for further study of the topic. Designed as a survey of informatics domains as well as primer of informatics methods, this course serves as an introduction for anyone interested to ask good informatics questions, employ informatics tools, or design informatics systems across a broad range of biomedical applications.

Data are messy, come from different sources, and are seldom complete. This course provides an in-depth study of how to prepare data coming from various sources, in different formats, with noisy information, in order to perform meaningful and sound analyses. Contents include grouping data, visualizing data, and aggregating data from the web and other sources.

This course familiarizes students with the concepts of misinformation, disinformation, and communicating for impact. Content covered includes how to fact check and how to combat misinformation and disinformation in practice.

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 fundamentals of bioinformatics and how they apply to translational and clinical problems. The purpose of this course is to give students a broad overview of the field of bioinformatics, and the tools commonly used, as well as the applications to practical biomedical issues, diseases, population health, drug discovery, etc. The students will also be exposed to a variety of open-source software for sequence alignment, SNPs discovery, and on how to access and analyze data from large biological databases, for translational and clinical research.

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.

This course is a comprehensive introduction to spatial data analysis and visualization using programming languages (R or Python). Modules will cover core concepts and practical applications of spatial data handling within these tools. The content is structured to progressively build knowledge, starting from basic principles and advancing to more complex analytical techniques.

This is a survey course of AI user-facing systems for healthcare, that demonstrates the practical use of seminal algorithms in machine learning and AI, as well as principles of interaction design and user center design. In addition, the course focuses on how these design principles and algorithms can be blended to generate impactful systems that help bridge health disparities.

This is a directed study course in health informatics for approved students, supervised by a member of the faculty. Students must have an assigned professor, written objectives, planned outcomes and timelines.

This course covers a specific topic in health informatics.

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