Comparative Data Science Education Policy and Management: United States and Europe

This study examines what “good” curricula and administration for undergraduate data science are by surveying and interviewing U.S. and European data science educators and administrators. Many U.S. and European higher education institutions have embraced data science education with a kind of inertial sensibility, i.e., it is inevitable that data science education needs to be offered to students. Information and data affect so many areas of knowledge that higher education administrators feel they must assure students are “ready” for the world they are inheriting. Academic capitalism and isomorphism theories help elucidate social and market forces influencing higher education decision-making. Topics to which I contribute using a comparative perspective include knowledge management across higher education institutions; data science education practice; transdisciplinarity; and the designing and implementation of public policies at the federal and local levels.