The Biostatistics data science pathway combines rigorous statistical training with the development of advanced computational skills to solve the public health challenges of tomorrow. Required courses cover epidemiology, regression models, databases, machine learning and more. Students will become familiar with data science programming tools (e.g. R, Python, SQL and NoSQL databases). Data science pathway graduates can find careers analyzing large volumes of health data in government (e.g. public health agencies), hospitals, industry (e.g. pharmaceutical companies) or research.
Students pursuing this pathway will graduate with the key skills of any Biostatistician. Unlike the traditional pathway, data science pathway students will have more experience using computational techniques to store, manipulate and analyze large volumes and varieties of data. This pathway trains biostatisticians; as such, it emphasizes the development and application of rigorous statistical theory to extensive health data sets, as opposed to application of the latest computational techniques that are prioritized in the health informatics masters. The focus on health applications differentiates this pathway from the MS in Data Science and Statistics.
Students must choose this pathway at the start of their two-year program.
Students should take 4 courses for credit each semester (BIS 525/526, EPH 600, EPH 100/101 are not for credit). Course schedules with more than 5 courses for credit will not be approved. Courses listed without a notation in the “term taken” column can be taken in either year of the program if prerequisites are met and with advisor approval.
* Students entering the program with an MPH or relevant graduate degree may be exempt from this requirement.
** These courses can only be counted to fulfill the requirement of one category (they cannot be counted twice in fulfillment of requirements)