The Mastery Rubric for Statistics and Data Science for coherence and consistency in data science education

Recently published guidance for undergraduate data science may not result in consistency in learning outcomes across or within institutions. To promote this consistency, the Mastery Rubric for Statistics and Data Science (MR-SDS) was developed prioritizing learning and the development of independence in the knowledge, skills, and abilities for professional practice in statistics and data science (SDS). A MR-SDS -driven curriculum can emphasize computation, statistics, or a third discipline in which the other(s) would be deployed; or, all three. The MR-SDS promotes consistency with recommendations for SDS education, and allows "statistics", "data science", and "statistics and data science" curricula to reliably, but flexibly, educate with a focus on increasing learners' independence. The MR-SDS supports self-directed learning, training, and tertiary education, accommodating the interests of business, government, and academic work force development. The MR-SDS can be used for development or revision of an evaluable curriculum for undergraduates, upskilling and training, and doctoral level learning.

Mastery Rubric

higher education

training

statistics and data science education

curriculum development 

Mid-Level

Education

Symposium on Data Science and Statistics (SDSS) 2023