Domain Emphasis

What are Domain Emphases?

Domain Emphases give students a grounded understanding of a particular domain of data-intensive research, relevant theory, or an integrative intellectual thread. A Domain Emphasis is comprised of three courses chosen from a list. Each Domain Emphasis is rooted in a lower division course, which is typically also a prerequisite for the upper division courses. 

A Domain Emphasis is not limited to courses that are intended to be specifically for data science. Rather, they should bring the data science student into the context of a domain. That may involve understanding the vocabulary, methods of study, theoretical foundations, or cultural outlook of the domain. The student needs to become able to build the bridges with data science in carrying out the emphasis, rather than expecting each course to do it for them.

Students will select one course from a short list of lower-division prerequisites, and two to three courses comprising 7 or more units from a list of upper-division courses. The lower division course is a required element of the Domain Emphasis.

What to think about when selecting a domain emphasis:

  • Courses you take for the 7-course L&S Breadth requirement may fulfill the lower-division course for a domain. Even if you don’t yet know which domain to choose, your breadths may work for you for the major as well.

  • Allow yourself some flexibilitychoose three or four upper-division courses from the domain list that you’d like to take, rather than two, so you can make sure you continue making progress if you are unable to take a particular course.

  • Be advised that it is important to examine whether seats are typically available in the courses for the domain you selectmany courses in other departments have priority enrollment groups.

Approved course lists are available for a partial set of Domain Emphases below. Additional Domain Emphases are currently under consideration and are expected to be approved in the near future.

applied mathematics and modeling
Data arts and humanities
evolution and biodiversity
human behavior and psychology
neurosciences
robotics
urban science
business and industrial analytics
ecology and the environment
geospatial information and technology
inequalities in society
organizations and the economy
social welfare, health, and poverty
cognition
economics
human biology
linguistic sciences
physical science analytics
social policy and law
Computational biology methods
environment, resource management, and society
human and population health
molecular biology and genomics
quantitative social science
sustainable development and engineering