Honors Thesis expectations are high. The intended end-product is a traditional research manuscript with accompanying artifact(s), all theses: - are 6 credits or more of sustained research on a single topic, typically conducted over two semesters. - begin with creative inquiry and systematic research. - include documentation of substantive scholarly endeavor. - culminate in an oral defense or other form of public presentation.

Honors Project expectations are high. The intended end-product is a traditional project manuscript with accompanying artifact(s), all projects: - are 6 credits or more of sustained research on a single topic, typically conducted over two semesters. - begin with creative inquiry and systematic research. - include documentation of substantive scholarly endeavor. - culminate in an oral defense or other form of public presentation.

This is a stand-alone independent study designed by the student and faculty sponsor that involves frequent interaction between instructor and student. Qualitative and quantitative enrichment must be evident on the proposed contract before consent is given to undertake the study.

This is a stand-alone independent study designed by the student and faculty sponsor that involves frequent interaction between instructor and student. Qualitative and quantitative enrichment must be evident on the proposed contract before consent is given to undertake the study.

This course is designed to provide Informatics students with a broad, practical introduction to the field of computational biology and bioinformatics. The course will discuss at a conceptual level the models and algorithms used to analyze biological sequence data, as well as practical applications and data analysis. Background in biology is not assumed.

The modern world is awash with data, and making sense of it requires specialized skills. This course will expose students to commonly used data analytics techniques. Topics include the acquisition, manipulation, and transformation of structured data, exploratory data analysis, data visualization, and predictive modeling. Students in this course will learn and use the Python programming language and tools for working with data. Analysis will be performed using real data sets. Does not count as a CS Elective (BA or BS).

This course aims to introduce the fundamentals of Clinical Health Informatics to prepare students as forerunners of the future of digital health care systems. More specifically, this course aims to teach students the fundamentals of and tools for quantitative analysis of clinical health data and the practical application of the tools on various health data. The detailed components of the course are as follows.

This course aims to introduce the fundamentals of Clinical Health Informatics to prepare students as forerunners of the future of digital health care systems. More specifically, this course aims to teach students the fundamentals of and tools for quantitative analysis of clinical health data and the practical application of the tools on various health data. The detailed components of the course are as follows.