  Learning to see through data is central to contemporary forms of algorithmic
knowledge production. While often represented as a mechanical application of
rules, making algorithms work with data requires a great deal of situated work.
This paper examines how the often-divergent demands of mechanization and
discretion manifest in data analytic learning environments. Drawing on research
in CSCW and the social sciences, and ethnographic fieldwork in two data
learning environments, we show how an algorithm's application is seen sometimes
as a mechanical sequence of rules and at other times as an array of situated
decisions. Casting data analytics as a rule-based (rather than rule-bound)
practice, we show that effective data vision requires would-be analysts to
straddle the competing demands of formal abstraction and empirical contingency.
We conclude by discussing how the notion of data vision can help better
leverage the role of human work in data analytic learning, research, and
practice.
