Readers of this blog and my site know that I'm an enormous fan of Breaking Bad. In the episode "Hazard Pay", Mike says to Walt, "Just because you shot Jesse James don't make you Jesse James."
For a quick preview of the episode, click below (SPOILER ALERT):
Breaking Bad Sneak Peak: "Hazard Pay"
Along the same lines, Vincent Granville writes on Analytic Bridge about the explosion of fake data scientists:
Books, certificates, and graduate degrees in data science are spreading like mushrooms after the rain.
Unfortunately, many are just a mirage: some old guys taking advantage of the new paradigm to quickly re-package some very old material (statistics, R programming) with the new label: data science.
To add to the confusion, executives, decision makers building a new team of data scientists sometimes don't know exactly what they are looking for, ending up hiring pure tech geeks, computer scientists, or people lacking proper experience. The problem is compounded by HR who do not know better, producing job ads which always contain the same keywords: Java, Python, Map Reduce, R, NoSQL. As if a data scientist was a mix of these skills.
And I couldn't agree more.
To be sure, software vendors, independents, and consulting firms have a strong incentive to overstate their skills whenever a hot new technology or trend erupts. I laughed a few years ago when all of these self-anointed "social media experts" appeared from out of nowhere. What did that position entail, exactly? They knew how to set up a Twitter account or a Facebook fan page? What were the criteria--and how could you prove that someone was an expert?
In his post, Granville pinpoints another crucial reason that many organizations are not hiring true data scientists: their HR departments. Does a hiring manager really know if someone is every bit the NoSQL or Hadoop expert he claims to be? I doubt it. In Too Big to Ignore, I call out HR folks for their general inability to utilize data on a regular basis. If that's the case, how can HR people verify that people possess the skills they say they do?
Personally, I would give applicants real-world scenarios and make them solve the problems--or at least try. A few years ago, I heard of a recruiter who made applicants manually write SQL during the interview. It's a brilliant move. You just can't fake INNER JOINs.
Simon Says
Be wary of snake oil salesman who claim to be data scientists. Because true data scientists are so important in an era of Big Data, they're not cheap. To quote Ronald Reagan, "Trust but verify."
Feedback
What say you?
2 Comments
Certainly what qualifies whom as a data scientist, or data analyst, is a slippery slope, but not sure it is fair to blame it on snake oil. I was at an IBM big data event yesterday though, and they are making a huge effort into helping universities add or augment data science to curriculum. Good or bad? While not a data scientist, and not trying to be one, I can relate to this situation:
Back in the 1970s I was a software developer, and we were rare commodities, a special breed. Few colleges had Comp Sci programs back then. Then Comp Sci exploded in the 1980s. I remember riding the MBTA Red Line during rush hour and seeing an ad from a local community college stating "If you can program you can get a good job." I remember thinking to myself, "Damn, guess my job is now a commodity too." Funny thing though, in order to be a decent developer, your brain had to work in a certain way, and probably the majority of those who went to that community college to learn how to program ended up elsewhere in terms of career - though some undoubtedly found their calling writing software.
Bottom line is this is what happens when a specialty emerges from the shadows - it becomes less of a specialty. In fact, I would argue that ALL business personnel could use a little Data Science 101 course, it raises all boats. And "true" data scientists shouldn't worry about all of this, there will always be a need for those entirely steeped in this particular black art, just like excellent software developers remain in demand.
It is inescapable that all the attention paid to "big data" will result in an increase in those wanting to be data scientists when they grow up. And then the demand and interest will normalize as just part of the cycle of innovation.
I'm a big believer that we all need to understand Big Data a bit more, Evan. Great comment.