Data scientists are the new rockstars

Bullshit.<p>"Big Data" and "Data Scientist" are the latest buzzwords like Web 2.0 and Java were during their ORA hype-r eras.<p>There is a lot more data. There are a lot more tools and technology to deal with data. There is a need for high quality people that understand how to handle data. But there are no rockstars, there are people that have passion and spent years learning and teaching their craft. But they aren't rock stars. The best may get paid as much as a very well off doctor or business owner, but they aren't going to fill stadiums around the world, sell millions of t-shirts or be targets of media gossip columns.

So plain old "scientists" don't already use Data the right way? How about boring old "statisticians?" Not enough javascript?

Please, let the term "rock star" die.

What troubles me most about the title "Data Scientist" is that it really means "Statistician" or "Statistical Analyst". There are so many interesting things you can do with lots of data, stats is but one of them. What do we call people who are good at some of the other disciplines?<p>I've spent bits of my career working with fairly large data sets at one time or another, and providing discovery, insight and analytic tools into that data, but very little of it seems to have anything remotely to do with what the job descriptions for "Data Scientist" are asking for.<p>Consider this, building a very large graph of the internet, then using various models on that graph to find unique and actionable insights: such as finding routing bottlenecks for a video delivery service, involves lots of data, lots of scientific like exploration, yet isn't a "data scientist" job by the job reqs.<p>How about this, building a text parser that can finely categorize and make recommendations for a research organization based on millions of grant proposals, all categorized into various "mission silos" that research organization is built around. Not a "data scientist" job.<p>Analyze multi-lingual news stories to build a real-time alert system for conflict analysts. Not a "data scientist" job.<p>Building a tool that can scan multi-spectral aerial imagery and automatically extrapolate man-made structures from natural, catalog all of the different vehicle makes and models, and generate a predictive model of commuting patterns, or make recommendations for housing development based on perceived socio-economic conditions? Not a data-scientist job.<p>Collecting information on who propositions who from a dating web site, normalizing the data for population and writing a report on the findings? <i>That's</i> a "data scientist's" job.<p>It's not that that kind of work isn't valuable, only that there are so many other kinds of things that involve what might intuitively be called "data science" that calling just the one discipline "data science" is doing a disservice to what should be an amazing discipline -- part Computer Scientist, Part Analyst.

As someone who's statistically inclined, I am not looking forward to this.

How do you call the phenomenon that leads statisticians to rename themselves "data scientists"? I propose the Freud-inspired "science envy".

Is there a type of science that does not involve data?

This is a pretty fluffed up article that missed the wave of "data scientists are awesome" articles last year.<p>But I think it's important to not underestimate the shift that has been happening. The race to automate tasks has always been accelerating, but it hit an inflection point a few years ago when mainstream business people realized what current technology can achieve. Pretty much every industry I've seen has been or can be drastically transformed by better data management and predictive analytics.<p>It's going to affect all of us, so I think it's worth following closely.

Data Science and "Big Data" have done a <i>really</i> good job of marketing themselves. As a result, people recognize that these roles are valuable, Data Scientists get paid &#38; treated well, and it tends to work out well for everyone.<p>What can we learn that we can apply to technology in general? I feel like "programmers" and "IT" tend to be undervalued while Data Science tends to be accurately or even overvalued.

If anyone from the site reading this, didn't work on my nexus galaxy. A dumb share popover took up the whole screen and didn't scroll away.

Delusional thoughts from fantasy island.

It is confused to give data science much credit for the success of Netflix. Netflix succeeded because of content, wide platform availability, and solid marketing. They ran a million dollar prize contest that ultimately did nothing for their code base, but did generate a lot of positive press.

But who are the new ninjas?