Interdisciplinarity requires the collaboration of two or more disciplines to combine their expertise to jointly develop and deliver learning and teaching outcomes appropriate for a subject area. Curricula and assessment mapping are critical components to foster and enhance interdisciplinary learning environments. Emerging careers in data science and machine learning coupled with the necessary graduate outcomes mandate the need for a truly interdisciplinary pedagogical approach. The challenges for emerging academic disciplines such as data science and machine learning center on the need for multiple fields to coherently develop university-level curricula. Using text mining, we empirically analyze the breadth and depth of existing tertiary-level curricula to quantify patterns in curricula through the use of surface and deep cluster analysis. This approach helps educators validate the breadth and depth of a proposed curriculum relative to the broad evolution of data science as a discipline.