The need for Data Scientists at all degree levels is growing rapidly. As is the case in boom times, there is a rush to respond to the immediate needs of students and the industry that sometimes can underestimate the complexity of developing a successful program. In particular, the interdisciplinary nature of Data Science and need for a non-traditional complex set of mathematical and statistical skills has made for great variation in curricular designs across institutions and a lack of coherence between gateway courses and those of the major. Earlier this year the Charles A. Dana Center launched an initiative to develop a set of recommendations for mathematics and statistics foundations that are integral to the preparation of undergraduate Data Scientists. Based on a scan of successful programs and the findings from several national initiatives these recommendations span two- and four-year college degree programs. They include recommendations for student learning outcomes, support for underprepared students, placement, and sample course sequences. Come join us for an exploration of these recommendations and their applicability to your program.