Curriculum Guidelines for Undergraduate Programs in Data Science
Richard De Veaux, Mahesh Agarwal, Maia Averett, Benjamin Baumer,, Andrew Bray, Thomas Bressoud, Lance Bryant, Lei Cheng, Amanda Francis, Robert, Gould, Albert Y. Kim, Matt Kretchmar, Qin Lu, Ann Moskol, Deborah Nolan,, Roberto Pelayo, Sean Raleigh, Ricky J. Sethi
TL;DR
This paper presents comprehensive curriculum guidelines designed to assist institutions in developing or revising undergraduate Data Science programs, based on collaborative input from faculty across various disciplines.
Contribution
It offers a structured set of curriculum guidelines specifically tailored for undergraduate Data Science programs, filling a gap in educational resources.
Findings
Guidelines developed through expert consensus
Applicable to diverse institutional contexts
Aims to standardize Data Science undergraduate education
Abstract
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science.
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