May 2, 2018


Academic Institutions Should Prepare Undergraduates for a Data-Driven Workplace, New Report Recommends

WASHINGTON – All U.S. undergraduate students should develop a basic understanding of data science to prepare them adequately for the workforce, says a new report from the National Academies of Sciences, Engineering, and Medicine. The report examines the importance and benefits of postsecondary data science education and recommends offering a range of educational pathways, attracting students with varied backgrounds to the discipline, and embedding ethics and privacy into the curriculum.

“We live in a data-driven era in which people constantly generate data about their whereabouts, habits, and preferences. The evolution and spread of data science creates a demand for more, well-trained data science talent,” said Laura Haas, co-chair of the committee that wrote the report, and dean of the College of Information and Computer Sciences at the University of Massachusetts, Amherst. “The undergraduate setting is a critical link in offering more data science exposure to students and expanding the supply of data science talent.”

Data science draws on skills and concepts from a wide array of disciplines that may not always overlap, making it a truly interdisciplinary field. Students in many fields need to learn about data collection, storage, integration, analysis, inference, communication, and ethics, the report says. The growing demand for data scientists across many sectors illustrates that data science instruction needs to be broader and include a wide range of students from varied programs. Both data science specialists and professionals with a range of data science knowledge and skills will be in demand.

Many skills that often are not fully developed in traditional computer science, statistics, and mathematics courses are crucial in the education of future data scientists. This collection of abilities -- data acumen -- combines concepts from these disciplines along with insight about communications and ethics plus key skills such as data modeling and visualization. Including this wide range of topics in a data science undergraduate curriculum will prepare future students for success in data-driven careers.

Academic institutions should ensure that ethics is woven throughout data science education, the report says. As data and tools for analyzing them have proliferated, data scientists increasingly face decisions about whether they should take certain actions just because they can. A code of ethics for the data science discipline should be approved and affirmed by members of professional societies and included in professional development programs and curricula.

“Starting a broad undergraduate data science program is a challenging task, but it will greatly benefit both students and academic institutions,” said Alfred O. Hero III, co-chair of the committee that wrote the report, and John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. “Inclusion, broad participation, and engagement are key to forming a cross-cutting data science curriculum. During these early days for undergraduate data science education, colleges and universities should be prepared to evaluate and evolve programs over time.”

The study was sponsored by the National Science Foundation. The National Academies of Sciences, Engineering, and Medicine are private, nonprofit institutions that provide independent, objective analysis and advice to the nation to solve complex problems and inform public policy decisions related to science, technology, and medicine. The National Academies operate under an 1863 congressional charter to the National Academy of Sciences, signed by President Lincoln. For more information, visit

Jennifer Olson, Media Consultant
Andrew Robinson, Media Relations Assistant
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Copies of Data Science for Undergraduates: Opportunities and Options are available at or by calling 202-334-3313 or 1-800-624-6242. Reporters may obtain a copy from the Office of News and Public Information (contacts listed above).


Division on Engineering and Physical Sciences
Computer Science and Telecommunications Board
Board on Mathematical Sciences and Analytics

Division of Behavioral and Social Sciences and Education
Board on Science Education

Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective

Laura M. Haas1 (co-chair)
College of Information and Computer Sciences
University of Massachusetts

Alfred O. Hero III (co-chair)
John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science;
R. Jamison and Betty Williams Professor of Engineering; and
Co-Director, Michigan Institute for Data Science
University of Michigan
Ann Arbor

Ani Adhikari
Teaching Professor
Department of Statistics
University of California

David E. Culler1
Founding Director
Intel Research
University of California

David L. Donoho2
Anne T. and Robert M. Bass Professor of Humanities and Sciences, and
Professor of Statistics
Department of Statistics
Stanford University
Stanford, Calif.

E. Thomas Ewing
Associate Dean of Graduate Studies and Research
College of Liberal Arts and Human Sciences, and
Department of History
Virginia Polytechnic Institute and State University

Louis J. Gross
James R. Cox and Alvin and Sally Beaman Distinguished Professor of Ecology and Evolutionary Biology and Mathematics;
Director, National Institute for Mathematical and Biological Synthesis; and
Director, The Institute for Environmental Modeling
University of Tennessee

Nicholas J. Horton
Professor of Statistics
Amherst College
Amherst, Mass.

Julia I. Lane
Center for Urban Science and Policy;
Wagner Graduate School of Public Policy; and
Provostial Fellow in Innovation Analytics
New York University
New York City

Andrew McCallum
Professor, and
Director, Center for Data Science
College of Information and Computer Sciences
University of Massachusetts

Richard D. McCullough
Vice Provost for Research
Harvard University

Rebecca Nugent
Teaching Professor
Department of Statistics and Data Science
Carnegie Mellon University

Lee Rainie
Internet and Technology Research
Pew Research Center
Washington, D.C.

Rob A. Rutenbar
Senior Vice Chancellor for Research
University of Pittsburgh

Kristin M. Tolle
Data Science Initiative
Microsoft Research Outreach
Redmond, Wash.

Talithia Williams
Associate Professor of Mathematics
Harvey Mudd College
Claremont, Calif.

Andrew Zieffler
Senior Lecturer and Researcher
Quantitative Methods in Education Program
Department of Educational Psychology
University of Minnesota


Jon Eisenberg
Co-Study Director

Michelle Schwalbe
Co-Study Director

1Member, National Academy of Engineering
2Member, National Academy of Sciences