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Operations research (including Monte Carlo simulations), machine learning (and clustering, scoring, feature selection), pattern recognition, computational complexity, data architecture (NoSQL, API, Map-Reduce, real-time), algorithms, statistics (when you add predictive power, robust inference for big data, remove p-values, most of the general linear model, hypothesis testing and keep experimental design, sampling, distributions, stochastic processes, robust statistics, estimation, cross-validation, and non-parametric confidence intervals) -- all this is part of data science.

Saying data science is dead is saying that none of this is useful anymore. But what is indeed dead, is the illusion that (1) there are people mastering ALL this stuff - actually there are, but they are rare and (2) that such people can find a job and help companies - wrong except in some modern environments; that's not understanding how the corporate world works, with it's numerous silos and battles between e.g. engineers and scientists to get money from top management for their projects, making an hybrid role impossible (though in some ways, a full data scientist is similar to a management consultant, which is also an hybrid role).

Of course, learning data science will open more doors (to the job seeker), offer more rich, diversified career paths as you can more easily make lateral moves (from research to engineering since you wear both hats). Modern companies are more flexible than old big corporations, and don't mind having an hybrid role such as data scientist to extract considerable value out of data - indeed to identify or create the right data in the first place, being an active rather than a passive data consumer. But even big old-fashioned companies benefit from hiring someone who could fit in multiple departments (marketing, IT, research, engineering). When layoffs arrive (they always do) it allows these companies to keep these great employees. And for small companies with limited budget, hiring a data scientist who does development AND science, or outsourcing to vendors, or training an engineer to become a data scientist, are the only options.

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