  Modern data and applications pose very different challenges from those of the
1950s or even the 1980s. Students contemplating a career in statistics or data
science need to have the tools to tackle problems involving massive,
heavy-tailed data, often interacting with live, complex systems. However,
despite the deepening connections between engineering and modern data science,
we argue that training in classical statistical concepts plays a central role
in preparing students to solve Google-scale problems. To this end, we present
three industrial applications where significant modern data challenges were
overcome by statistical thinking.
