This course covers the foundations of probability and inferential statistics as well as foundations of linear algebra and matrices. After completion of this course, students should be comfortable with performing basic analysis of data including descriptive statistics, data visualization and appropriate statistical tests. Different probability distributions will be introduced along with hypothesis testing, confidence intervals, linear regression, and ANOVA.

This course covers using Python as a programming language to write, implement, and design programs that are relevant to various aspects of programming in a health setting. After completion of this course, students should be comfortable with the basic data structures in Python and R (including arrays, dictionaries, and dataframes), conditional logic and iterators, writing Python and R functions, and using Python libraries to read external data and perform data manipulations and data analysis.

This course examines the evidence developed through the lens of outcomes research relative to clinical care and public/population health initiatives. It explores the development of study design, developing a workable research question and associated proposed study methods. The course explores frequently used study designs, techniques for evaluating/selecting health outcomes measures, and analytical approaches appropriate to conducting health outcomes research. Students will construct an independent research protocol, which will be developed in increments as course evolves.

In this course, students will work to develop a data science thesis project with both an outside mentor and a faculty advisor. This course essentially bridges the entire curriculum, but builds over the coursework to the final presentation at the students' second residency prior to completing the program. Each student on a team will be required to justify the completion of practicum hours.