Background on the Data Science Education Program at Berkeley
Berkeley faculty across many disciplines have collaboratively created a model for a comprehensive undergraduate data science curriculum. Starting from the blueprint in the January 2015 report of the Data Sciences Education Rapid Action Team (DSERAT), the curriculum is built around a modular core-and-connections structure that can serve as a platform on which many academic programs can build.
The Data Science curriculum was launched at the entry level in 2015-16 with an innovative introductory course and a suite of connector courses that relate to students’ areas of interest, now ranging from neuroscience to civil engineering to demography to ethics. The entry-level courses are designed to provide the base for later classes in a broad range of departments that will be able to leverage and extend what students have learned. The upper tiers of the program, which are now being developed, will provide additional depth and connect across the campus with a major and integrated minor offerings. As previewed in the DSERAT report, the program engages with societal and ethical issues around data science not only in course content, but also throughout the program design, incorporating best practices around diversity, equity, and inclusion so that the curriculum is welcoming to students of many backgrounds and interests.
The curriculum that is now being created aims at an integrated program. It responds to the experience of faculty of the transformation of their own fields of research and teaching by the cross-cutting possibilities of data science, and to fast-growing student demand for courses in computing, inference, and hands-on work with real data, as reflected in very large numbers of students enrolling in preexisting courses covering parts of this material in separated fashion. The curriculum aims to integrate a full appreciation of the lifecycle of working with data with the computational and mathematical knowledge that underlies it. It follows a modular design that allows it both to leverage common teaching of exceptional quality and shared infrastructure in a highly cost-effective manner, and to create tailored offerings designed and “owned” by departments. In staying strongly coupled to student interests and diverse programs’ needs, it must operate flexibly and responsively even as it scales up fast.
Leadership and staffing
Program leadership has been provided by the Dean of Undergraduate Studies and two additional faculty members involved in the leadership of the Data Science Planning Initiative. A proposal for its institutional regularization in relation to a new decanal division for computation and data is now being considered by campus. Through its start-up phase the data science curriculum has been very lightly staffed. In addition to the dean’s resources and a start-up funding allocation, it has drawn heavily on individual faculty investment, staff commitment, and provision of additional resources by multiple departments and support units.