A core data science discipline, Biostatistics creates new and innovative theory and methods for study design, data analysis, and statistical inference, and promotes the principled use of these methods to advance scientific inquiry in research fields that concern human health and the life sciences. Launched in 2011, the Department of Biostatistics at the Brown University School of Public Health has a highly interdisciplinary faculty of internationally recognized leaders in studies of diagnosis and prediction, causal inference, and research synthesis, and a growing number of students at all levels. The faculty have developed a broad portfolio of research, with major strengths in the analysis of large health care databases, causal inference, diagnostic imaging evaluation and radiomics, computational biology and bioinformatics, Bayesian methodology, research synthesis, and neuroscience. As such, Biostatistics represents a vital connection to a central domain discipline in data science, informing our methodological innovation.
Carole and Lawrence Sirovich Professor of Public Health
The Computer Science Department is known for its ground-breaking research, its distinctive educational programs at both the undergraduate and graduate levels, and its strong industry connections that offer students unique opportunities to collaborate with and intern at top technology companies. The award-winning faculty conduct research and offer courses on a wide array of data science-related topics including data management and engineering, machine learning and artificial intelligence, interactive analytics and data mining, information privacy and security, big data algorithms and visualization. This year’s launch of the new CS course Data Fluency for All marked a signature effort toward our integrative approach to data science instruction at all levels.
The Department of Mathematics enjoys a rich historical tradition of research and education in many fields of pure mathematics, with particular strengths in algebra and number theory, geometry and topology, probability, and analysis. The Department, which counts many internationally recognized researchers among its faculty ranks, nurtures an informal environment for students that emphasizes creative models for scholarship and learning. As data science challenges require increasingly complex methodologies and algorithms, the Department’s expertise in tools from cryptography, harmonic analysis, probability, and even topology has become central to developing our understanding of data science’s foundational questions, and the Department’s courses in these areas serve as a theoretical foundation to our methodological research and our curricular offerings in data science.
The Division of Applied Mathematics is a distinguished unit counting many internationally recognized scholars in its faculty ranks: the research conducted in the Division ranges from applied and algorithmic problems to the study of fundamental mathematical questions. Its core research group in Pattern Theory has set much of the groundwork for modern data science’s role in computer vision and neuroscience. The Applied Mathematics Faculty’s work in statistical inference, in Bayesian statistics, and in data assimilation in neuroscience and climate plays a vital role in the research mission of the Initiative, as does its strength in large-scale high-performance computing. The Division offers courses in data science that range from first-year seminars to advanced upper-level courses.