  Data science is a discipline that provides principles, methodology and
guidelines for the analysis of data for tools, values, or insights. Driven by a
huge workforce demand, many academic institutions have started to offer degrees
in data science, with many at the graduate, and a few at the undergraduate
level. Curricula may differ at different institutions, because of varying
levels of faculty expertise, and different disciplines (such as Math, computer
science, and business etc) in developing the curriculum. The University of
Massachusetts Dartmouth started offering degree programs in data science from
Fall 2015, at both the undergraduate and the graduate level. Quite a few
articles have been published that deal with graduate data science courses, much
less so dealing with undergraduate ones. Our discussion will focus on
undergraduate course structure and function, and specifically, a first course
in data science. Our design of this course centers around a concept called the
data science life cycle. That is, we view tasks or steps in the practice of
data science as forming a process, consisting of states that indicate how it
comes into life, how different tasks in data science depend on or interact with
others until the birth of a data product or the reach of a conclusion.
Naturally, different pieces of the data science life cycle then form individual
parts of the course. Details of each piece are filled up by concepts,
techniques, or skills that are popular in industry. Consequently, the design of
our course is both "principled" and practical. A significant feature of our
course philosophy is that, in line with activity theory, the course is based on
the use of tools to transform real data in order to answer strongly motivated
questions related to the data.
