This course will cover the design of personal health and wellness technologies. Using the personal health informatics model, we will learn various challenges in designing technologies for personal health data collection (e.g., step count, heart rate, or food intake etc.), integration, self-reflection, and behavior change. Going further, students will understand design issues in sharing personal health data and discuss design guidelines for collaborative data collection, reflection, and care.

This course will cover the design of personal health and wellness technologies. Using the personal health informatics model, we will learn various challenges in designing technologies for personal health data collection (e.g., step count, heart rate, or food intake etc.), integration, self-reflection, and behavior change. Going further, students will understand design issues in sharing personal health data and discuss design guidelines for collaborative data collection, reflection, and care.

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 an introduction to the concepts and skills involved with the collection, management, analysis, and presentation of data sets and the data products that result from the work of data scientists. Privacy, algorithmic bias and ethical issues are also discussed. Students will work with data from the financial, epidemiological, educational, and other domains. The course provides many examples of real-world data that students work with using various software tools.

This course is an introduction to the concepts and skills involved with the collection, management, analysis, and presentation of data sets and the data products that result from the work of data scientists. Privacy, algorithmic bias and ethical issues are also discussed. Students will work with data from the financial, epidemiological, educational, and other domains. The course provides many examples of real-world data that students work with using various software tools.

The course will cover the technical foundations and use of today's communication networks, particularly the internet. It will also address key social, policy, economic, and legal aspects of these networks, their use (and abuse) and their regulation. This course covers computer science topics, but all material will be presented in a way that is accessible to an educated audience with or without a strong technical background. This course is not intended for Computer Science majors or minors; students interested with a major or minor-level treatment of this material should see COMPSCI 453.

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.