(companion talk to Ostblom & Timbers arXiv preprint)
In the data science courses at UBC, we define data science as the study and development of reproducible and auditable processes to obtain value (i.e., 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 being 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 motivation, guided instruction and practice are key to effectively teach this challenging, yet important subject. Here we present examples of how we deeply motivate, effectively guide and provide ample practice opportunities data science students to effectively engage them in learning about this topic.