The Park City Math Institute 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 United States, 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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Much has been written recently about 'Big Data' and the new possibilities that mining this vast amount of data brings. It promises to help us understand or predict everything from the Higgs boson to what a customer might purchase next from Amazon. As with most new phenomena, it is hard to sift through the hype and promotion to understand what is actually true and what is actually useful. One implicit or even explicitly stated assumption in much of the Big Data literature is that statistical thinking fundamentals are no longer relevant in the petabyte age. However, we believe just the opposite. Fundamentals of good modeling and statistical thinking are crucial for the success of Big Data projects. Sound statistical practices, such as ensuring high-quality data, incorporating sound domain (subject matter) knowledge, and developing an overall strategy or plan of attack for large modeling problems, are even more important for Big Data problems than small data problems.