The UC Berkeley Institute for Data Science (BIDS) and its partners have received one award from the National Science Foundation to deepen the theoretical foundations of data science in a new transdisciplinary institute, and another to strengthen educational strategies through national workshops led by the faculty and staff guiding Berkeley’s broad-ranging data science curriculum.

The first award will support the creation of the Foundations of Data Analysis Institute, which will bring together core research communities in theoretical statistics, applied mathematics and theoretical computer science. It will also be supported by NSF’s TRIPODS Program (Transdisciplinary Research in Principles of Data Science), which was launched to address fundamental open questions in data science’s theoretical underpinnings.

The institute will initially address four deep theoretical challenges: the possibility of a general complexity theory of inference in the context of optimization, the power of stability as a computational-inferential principle, the value of randomness as a statistical and algorithmic resource in data-driven computational mathematics, and the principled combination of science-based with data-driven models.

Cathryn Carson, a professor of the history of science and faculty lead of Berkeley’s new Data Science Education Program, is the principal investigator for the second NSF award. She and colleagues will lead two national workshops to develop curricular materials anchored in data science work.

The effort will assist integrating insights from social scientific and educational research into teaching and learning in data science. The workshops’ goal will be to develop implementable curricular materials with practical resources such as exercises and course modules that can be made publicly available.

The grants recognize the campus’s leadership in data science education. Berkeley’s undergraduate curriculum in data science has been built from the freshman level up and is teaching more than 2,000 students per semester.