Pathways to Introductory Data Science

With Nicholas J. Horton, Roxy Peck, & Rebecca Hartzler (Amherst College)


The need for Data Scientists at all degree levels is growing rapidly, with a rapid expansion of undergraduate degree programs. The interdisciplinary nature of Data Science and need for a non-traditional complex and integrated set of mathematical, statistical, and computational skills has made for great variation in curricular designs across both two- and four-year institutions and a lack of coherence between gateway courses and courses needed for degree programs.

Some programs have developed an introductory (no prereq besides pre-calculus) data science course to introduce the discipline to students. Models for such a course include the curriculum and the Advanced Placement Computer Science Principles course. Others have proposed that the introduction to Data Science should build on prior coursework in statistics (e.g., STAT101 or AP Statistics) and computer science (e.g., CS0 or CS1).

This bird of a feather discussion will focus on the relative merits and drawbacks of these two approaches. The discussion will be seeded by a lightning overview of four introductory slides laying out key skills and capacities as well as some of the advantages and disadvantages of no prereq and prereq introductory courses. The birds of a feather discussion will then begin in earnest.