This course will introduce various aspects of data science in digital health applications in the form of state-of-the-art projects. Students will build a foundation for performing applied data science, machine learning, and artificial intelligence (AI). The course will employ a combination of high-level theory with practical experience. Each team will work on a unique topic in biomedical engineering and will present their results in the form of a journal publication through merging all assignments and presentations during the course work. Therefore, students will achieve hands-on experience in how to perform literature review, data visualization, AI analysis and results interpretation as well as how to prepare a manuscript to publish their results and get reviewers' feedback on their work. Expert guest lecturers from researchers and scientists will be invited to present sample previous or ongoing projects in digital health. Teams of four to five students will choose their projects from the provided themes. This course will provide insights to help students apply theory to real world health examples targeting vulnerable population such as patients and older adults. This course will also provide students with lots of opportunities to use recent innovative sensing and vision technology for projects on prevention, detection, and treatment. We will discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into healthcare safely and ethically.