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