  Consensus based publications of both competencies and undergraduate
curriculum guidance documents targeting data science instruction for higher
education have recently been published. Recommendations for curriculum features
from diverse sources may not result in consistent training across programs. A
Mastery Rubric was developed that prioritizes the promotion and documentation
of formal growth as well as the development of independence needed for the 13
requisite knowledge, skills, and abilities for professional practice in
statistics and data science, SDS. The Mastery Rubric, MR, driven curriculum can
emphasize computation, statistics, or a third discipline in which the other
would be deployed or, all three can be featured. The MR SDS supports each of
these program structures while promoting consistency with international,
consensus based, curricular recommendations for statistics and data science,
and allows 'statistics', 'data science', and 'statistics and data science'
curricula to consistently educate students with a focus on increasing learners
independence. The Mastery Rubric construct integrates findings from the
learning sciences, cognitive and educational psychology, to support teachers
and students through the learning enterprise. The MR SDS will support higher
education as well as the interests of business, government, and academic work
force development, bringing a consistent framework to address challenges that
exist for a domain that is claimed to be both an independent discipline and
part of other disciplines, including computer science, engineering, and
statistics. The MR-SDS can be used for development or revision of an evaluable
curriculum that will reliably support the preparation of early e.g.,
undergraduate degree programs, middle e.g., upskilling and training programs,
and late e.g., doctoral level training practitioners.
