To all the jokes of "data science is just statistics done by engineers" and other such things, read Breiman's "Statistical Modeling: The Two Cultures."  It talks about how the field of statistics largely ignored "algorithmic modeling" techniques, and therefore historically those techniques have been developed outside of academic statistics, either in computer science departments or in industry.
If you look at all the big name people who are pushing forward on deep learning and machine learning -- Yann LeCunn, Andrew Ng, Geoffry Hinton -- at Facebook, Google and other places, they don't have statistics degrees, they have computer science degrees. There's a whole wave of techniques and schools of thought that developed outside of statistics. To come back now and say "data science is statistics done by engineers" as some slight against engineers is malicious, parochial and wrong, and it annoys me greatly that it comes up so often on Hacker News.
I myself like Information Science more than Data Science, but I do not care that much for semantics. There was a need to specify a role of someone who makes sense out of data, gathers insights, using the tools from mathematics, computer science, statistics, and information theory. It's also a different type of science, data-driven science, as opposed to theoretical/metaphysical, empirical, or computational science.
There was an old joke that AI stood for Advanced Informatics. I think the commercialization of the term "AI" is a bit harmful and obfuscating. Companies tumble over one another to market their professionals as Applied AI or their products as AI. AI is the automation of human thought. It includes philosophy and cognitive science, both fields seem completely missing for applied AI.
I know many AI researchers already switched to calling themselves ML researchers a few years back. This, because the field of AI became muddied with futurist adherents of the Singularity. Did not help that the public perception of AI is somewhere between "Skynet is coming!" and "AI will take my job". Nowadays, ML is also heavily saturated and hyped beyond repair. Meanwhile the field of AI has not even solved the common sense problem.
Data science is rudimentary-level statistics, done on a Mac sipping a latte, while pretending that data mining and other disciplines haven't existed for decades already
And DevOps too. Honestly I’d like to see more thought pieces about statistical devops workflows that aren’t from startups which intentionally complicate the process to sell their own product.
I'm legitimately asking because I had to just admit to myself that I didn't really know what "false equivalency" means and looked it up.