  Demand for data science education is surging and traditional courses offered
by statistics departments are not meeting the needs of those seeking training.
This has led to a number of opinion pieces advocating for an update to the
Statistics curriculum. The unifying recommendation is computing should play a
more prominent role. We strongly agree with this recommendation, but advocate
the main priority is to bring applications to the forefront as proposed by
Nolan and Speed (1999). We also argue that the individuals tasked with
developing data science courses should not only have statistical training, but
also have experience analyzing data with the main objective of solving
real-world problems. Here, we share a set of general principles and offer a
detailed guide derived from our successful experience developing and teaching a
graduate-level, introductory data science course centered entirely on case
studies. We argue for the importance of statistical thinking, as defined by
Wild and Pfannkuck (1999) and describe how our approach teaches students three
key skills needed to succeed in data science, which we refer to as creating,
connecting, and computing. This guide can also be used for statisticians
wanting to gain more practical knowledge about data science before embarking on
teaching an introductory course.
