  What do we teach and what should we teach? An honest answer to this question
is painful, very painful--what we teach lags decades behind what we practice.
How can we reduce this `gap' to prepare a data science workforce of trained
next-generation statisticians? This is a challenging open problem that requires
many well-thought-out experiments before finding the secret sauce. My goal in
this article is to lay out some basic principles and guidelines (rather than
creating a pseudo-curriculum based on cherry-picked topics) to expedite this
process for finding an `objective' solution.
