  The unprecedented growth in the availability of data of all types and
qualities and the emergence of the field of data science has provided an
impetus to finally realizing the implementation of the full breadth of the
Nolan and Temple Lang proposed integration of computing concepts into
statistics curricula at all levels in statistics and new data science programs
and courses. Moreover, data science, implemented carefully, opens accessible
pathways to stem for students for whom neither mathematics nor computer science
are natural affinities, and who would traditionally be excluded. We discuss a
proposal for the stealth development of computational skills in students' first
exposure to data science through careful, scaffolded exposure to computation
and its power. The intent of this approach is to support students, regardless
of interest and self-efficacy in coding, in becoming data-driven learners, who
are capable of asking complex questions about the world around them, and then
answering those questions through the use of data-driven inquiry. This
discussion is presented in the context of the International Data Science in
Schools Project which recently published computer science and statistics
consensus curriculum frameworks for a two-year secondary school data science
program, designed to make data science accessible to all.
