  The field of data science currently enjoys a broad definition that includes a
wide array of activities which borrow from many other established fields of
study. Having such a vague characterization of a field in the early stages
might be natural, but over time maintaining such a broad definition becomes
unwieldy and impedes progress. In particular, the teaching of data science is
hampered by the seeming need to cover many different points of interest. Data
scientists must ultimately identify the core of the field by determining what
makes the field unique and what it means to develop new knowledge in data
science. In this review we attempt to distill some core ideas from data science
by focusing on the iterative process of data analysis and develop some
generalizations from past experience. Generalizations of this nature could form
the basis of a theory of data science and would serve to unify and scale the
teaching of data science to large audiences.
