I've been to Big Data conferences where the main application was mean and standard deviation on a huge datasets, I'm just sick of companies inflating the Big Data bubble (and this comes from a "data scientist")"Data science" is a bizarre job description to me. Some companies' data science teams are doing work that could be done in Excel, and others are doing sophisticated machine learning research. In many companies, though, it's a watered-down version of the (extinct, sadly) R&D job description. I've heard people opine that 95% of "data scientists" have never implemented anything more sophisticated than an SGD regression, and it wouldn't surprise me.
Granted, you can get some neat insights and visualizations with off-the-shelf tools, but I still consider it important to know how the algorithms actually work and what the basic assumptions are. For example, you can usually use linear regression for 2-class classification problems (as opposed to the mathematically more correct logistic model, since probabilities are in [0, 1] and linear models diverge at the boundary) get a reasonable predictive model, but it's worth knowing when (and why) that short-cut breaks down.
In software, you hear "data scientist syndrome" used to describe people who have a lot of short job tenures because they (a) leave if there isn't interesting work for them, and (b) tend to be the first laid off, not because they're bad but because R&D is first to bleed when things go bad. The software industry forces you to choose between short-term job security (people doing interesting work are most exposed to organizational changes) and long-term career health (people not doing interesting work turn into dinosaurs). It stands to reason that the selection process for organizational credibility (favoring tenure) would be something other than deep knowledge of data science (which requires a stream of interesting work, and the behavioral correlates such as job volatility).
It's sad that the typical corporate culture of reliable mediocrity, at the expense of excellence, has also learned to use the vocabulary of "Big Data". Reality: truly Big Data (> 10 TB) is a huge pain in the ass. It's what you have to deal with when there is too much noise, or the model's essential complexity is too high, to get a good model out of "small data".