Data Science Track of the PhD Program
Welcome from the BIDS Program Director, Bradley Malin, PhD,
A confluence of technical, analytical, and policy advancements have thrust the biomedical community into the big data science age. Vanderbilt University is right at the center of it all and is the place where the next-generation of biomedical data science investigators and practitioners will be educated and endowed with the skillset to innovate new technologies and analytic strategies to support basic scientific advances. The Vanderbilt Big Biomedical Data Science (BIDS) Training lays a foundation in, and emphasizes the symbiotic relationship between, Biomedical informatics, Computer Science, and Biostatistics. The BIDS program provides PhD students with access to a diverse array of real big biomedical data sets, software tools, and applications at Vanderbilt (and interdisciplinary collaborations). BIDS also integrate courses and faculty from across the institution to ensure that students are well-versed in the foundational competencies of computation, statistics, and biomedical science that are necessary to achieve reproducible success in this field.
The overarching objective of the Vanderbilt BIDS Training Program is to thoroughly prepare the future leaders of the biomedical community focused on infrastructure, software tool development, and big data analytics. The BIDS program aims to achieve this goal through rigorous classroom and research training, as well as career development, for predoctoral trainees who will matriculate into the new Data Science Track of the existing Vanderbilt BMI PhD program. For more information see the Curriculum Overview of the Data Science Track.
The BIDS Faculty Mentors cover the foundational areas of biomedical informatics, biostatistics, and computer science (with a focus on databases and machine learning). Yet, perhaps more important to the success of BIDS is the fact that, the faculty mentors also cover a range of biomedical scientific disciplines (e.g., biochemistry, cancer biology, and genetics) and clinical application domains, (e.g., anesthesiology, internal medicine, and oncology), that will provide clear illustrations of types of data and environments big data technologies need to support. It should further be noted that an overwhelming majority of the faculty mentors are involved in large team-based scientific initiatives (e.g.,eMERGE, PGRN, regional health information exchange implementation and evaluation) and have experience in co-mentoring of students (often across schools of the university).