  In the data science courses at the University of British Columbia, we define
data science as the study, development and practice of reproducible and
auditable processes to obtain insight from data. While reproducibility is core
to our definition, most data science learners enter the field with other
aspects of data science in mind, for example predictive modelling, which is
often one of the most interesting topic to novices. This fact, along with the
highly technical nature of the industry standard reproducibility tools
currently employed in data science, present out-of-the gate challenges in
teaching reproducibility in the data science classroom. Put simply, students
are not as intrinsically motivated to learn this topic, and it is not an easy
one for them to learn. What can a data science educator do? Over several
iterations of teaching courses focused on reproducible data science tools and
workflows, we have found that providing extra motivation, guided instruction
and lots of practice are key to effectively teaching this challenging, yet
important subject. Here we present examples of how we deeply motivate,
effectively guide and provide ample practice opportunities to data science
students to effectively engage them in learning about this topic.
