  A substantial fraction of students who complete their college education at a
public university in the United States begin their journey at one of the 935
public two-year colleges. While the number of four-year colleges offering
bachelor's degrees in data science continues to increase, data science
instruction at many two-year colleges lags behind. A major impediment is the
relative paucity of introductory data science courses that serve multiple
student audiences and can easily transfer. In addition, the lack of pre-defined
transfer pathways (or articulation agreements) for data science creates a
growing disconnect that leaves students who want to study data science at a
disadvantage. We describe opportunities and barriers to data science transfer
pathways. Five points of curricular friction merit attention: 1) a first course
in data science, 2) a second course in data science, 3) a course in scientific
computing, data science workflow, and/or reproducible computing, 4) lab
sciences, and 5) navigating communication, ethics, and application domain
requirements in the context of general education and liberal arts course
mappings. We catalog existing transfer pathways, efforts to align curricula
across institutions, obstacles to overcome with minimally-disruptive solutions,
and approaches to foster these pathways. Improvements in these areas are
critically important to ensure that a broad and diverse set of students are
able to engage and succeed in undergraduate data science programs.
