Abstract
Undergraduate data science students need more math, not less, but the typical sequence of mathematics courses does not meet their needs. Core courses provide concepts. that are crucial to data scientists: differential calculus, integral calculus, multivariable calculus, discrete mathematics, linear algebra, and probability. We need to find ways to successfully move students through the core faster. What is needed is a revision of the applied math curriculum that is accessible and useful to students majoring in the cognate disciplines of statistics, computer science, data science, physics, and engineering, as well as economics, environmental science, psychology, and political science. A revision is possible if the curriculum is adapted to leverage (rather than ignore) the power of computing.
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