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> But, all those things people did in the '90's or even earlier. It was called "data warehousing" or "decision support" back then. I would say data warehousing is more concerned with things like OLAP, Star Schema, ETL, etc. than what people are calling 'data science' right now. The same thing with 'decision support', since data warehousing grew out of decision support systems. The most overlap here is with 'data mining' algorithms like association rules clustering. > The fundamental techniques - linear regression, logistic regression, k-mean clustering - go back even earlier, to the OR community post-WW2. Here I think you've got a stronger argument. OR has a long, proud history of using applied math for business objectives. But again, I would say most of OR deals with different problems and different techniques - it's more about prescriptive analytics, constrained optimization, linear programming, simulations, etc. than the type of predictive modeling in most data science. I see data science as a separate field even though it's stitched together from a bunch of others. It's certainly not entirely new, and certainly overhyped in some annoyingly-breathless news reports. I could say the same thing about CS - was it entirely "new" when it started as a discipline? Isn't CS "just" applied math? |