Report Open Access
Cragin, Melissa; Kloefkorn, Tyler
Data science is fast emerging as a new discipline. Data science initiatives across universities and colleges in the US and beyond are emerging at a rapid rate. That data science is as much a practice as it is a discipline, raises challenging organizational questions - for example, whether and how data science should be its own major, department, or division at a university. If data science is siloed into one academic unit, how does one effectively integrate data science skills and best practices into curricula in other departments, particularly those necessary for students in other disciplines to reach the expectations of 21st century employers? This panel was an outgrowth of discussions held during the inaugural Data Science Leadership Summit held in March 2018 at Columbia University, with the goal of furthering understanding of the approaches to data science developing at US institutions. For this Summit, the Program Committee, led by Srinivas Aluru, chose “Data science organizational structures” as one of the current challenge areas for academic departments and cross-departmental initiatives. The session was organized to meet the objectives of the Summit and learn from those leading exemplar programs about success, challenges, and lessons learned. The goal of the panel was to move beyond the structured information already gathered on entities and their institutions (from the Summit report) to questions and decisions encountered along the way. How do we make data science entities work for our institutions? How do we make them sustainable? What approaches seem
to be working well? What are the challenges?
Key topics covered include:
Editorial Note: Some sections have been modified by panelists to update perspective and provide additional context. We are grateful for these additions. (March 12, 2020)