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The rapid expansion of data science programs across a wide range of academic disciplines - including computer science, engineering, business, and other applied data domains - presents a challenge for standardizing curricula in line with established competencies. This paper critically examines whether university data science programs are aligned with the ACM Competencies for Undergraduate Data Science Curricula. Using a systematic review of 788 data science program offerings and 9,322 course titles, we assess levels of alignment with ACM's eleven competency areas. Additionally, we evaluate the inclusion of additional common skills course offerings, such as math/statistics, data analytics, and capstone courses. Our findings highlight significant variability in programs' adherence to the ACM competencies. This underscores the need for greater interdisciplinary collaboration towards integrating computing, statistics, and domain-specific coursework into the broad range of data science curricula, ensuring that data science graduates have a well-rounded, interdisciplinary skill set suited to the diverse applications of data science.