I caught up with an old grad school friend earlier this year. He's a top-notch statistician who's built a successful career working in quants departments of large insurance and health care companies. With little simplification, I'd characterize his role over the last 20 years as a predictive modeling expert. His work is primarily “big iron” -- revolving on Teradata, Oracle and SAS. Besides being a senior statistician, he's also a more-than-capable data integration and statistical programmer.

In the past few years especially, we've had “discussions” on the differences between data science (DS) and statistics/machine learning as disciplines. He's characterized DS as little more than a trumped up moniker marketed by the newest analytics generation to brand themselves with a sexy statistics job title – for work that's indistinguishable from what he's been doing for years.

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