What really is a data scientist? I guess someone along the 
line had to come up with a name so those folks who understand queries and big data systems could be given a name. The term Data Scientist is not to be confused with Computer Science as we are talking two very different and distinct classifications of work. I’m not talking about “real” scientific research either as that speaks for itself. What I am talking about is “science to make money” or to find a way to make money. Sometimes as we read about some of the “monetary” incentives make their way over to the research side and some come out as frauds.
As I read all over the internet, data science is all about finding “value” and is not real scientific research. Even the Quants on Wall Street say the same thing when referring to economists and their so called “science” they reference. Back when quantitative solutions were young, sure it was exciting but even those in the quant areas back then had no clue it would evolve out to what we have out there today, a mess of fictitious math to alter risk to make money. Sometimes it is lowering risk while other times it’s accelerating risk when services, money, etc. are to be denied.
The financial markets lose track here as they seem to believe that their formulas and algorithms, even when accentuated with fiction will be “right”…well they might be right to generate money but not accurate as we know it. Talk to a real scientist and see how much research is tossed, a lot until they find the right combinations with big data sets, like huge genomic data sets. I don’t think the financial folks realize the amount of research that goes into real science. In the financial world I see this “science” as a methodology to move money and that’s what it starts and ends with, period.
With all the geniuses hired on Wall Street to do their research could they even for a minute tolerate the amount of time that “real” science research takes? Probably not but just write the code and formulas that “look good” and let’s make money. Companies today are faced with finding value with “big data” and why? Sometimes it’s like the old story of why did you 
climb the mountain, because it’s there. Is that the case with business and some aspects of big data? Nobody knew what a quant was until they hired a few mathematicians to create non linear formulas, then of course they were the design masters of business plans as a formula could help create the framework. As time moved forward with models, they of course became a lot more than framework people, they were assigned tasks like “risk assessment” to work on to see if adjusting numbers there would lead to greater profitability and guess what, it worked well for the banks and still does.
They all found out that formulas and algorithms move money, and in a way that is not visible to the average consumer so the algorithms do their thing quietly on servers 24/7 with arranging parameters to be met and move the money accordingly. If it is done well with training customer service individuals they are able to somehow explain some of these happenings in a way that makes sense to the consumer. What we end up with is the fantasy world of numbers coming together with the real world and what works with math does not always work with humans. So again, when we look at the people that are now called “data scientists” when you can find them, is this really science?
Hiding, Falsifying, And Accelerating Risk Has Become the Achilles Heel of the US Economy As the “Real” World” Clashes With the Values Created From a World of “Fictional Values” Of Formulas and Math
One scientific study that I have mentioned and blogged about a bit in jest is the full on study on how humans experience real pain when presented with math problems and this may be why those who have overcome the pain have all the money? We might have a scientific explanation for an occurrence that keeps repeating itself and for that matter a couple bodies of senators and representatives that feel pain sitting in their seats when in session? Maybe we should be working on the fear of math instead:)
“Algo Duping” – PLOS One Journal Publication Explains Why The Fear of Math Plays a Big Role As One Underlying Reason We All Get Duped And Those Who Don’t Fear Math Take All the Money, Gradually, Using “Mathematical Formulas & Algorithms”
So coming back around again to the “data scientist” topic, is this really science? Is making money a science or a more like a profession? Ok so we have let’s say our “data scientists” that have found value in some of the big data that resides on their servers…just the mountain…because it is there. Are they “scientific” enough to know how to use the queries with both linear and non linear methodologies and “stay in context”?
If you take things too far out of context, it comes out in the wash eventually and resentment will follow when consumers 
find out they have been fed a bunch of hog wash, reports and statistics to afford making money only. The link below has a video which I thought was very good as IT folks from big companies are sitting around discussing this exact topic and the one gal from T-Mobile had guts enough to say “what we are doing now is silly”..meaning she looked at what T-Mobile was doing in house, and she knew the difference between linear and non linear data sets as well as the time it takes to prepare the sets for queries, etc. She was not a data scientist either but was kind admitting she would like to find one:)
Big Data/Analytics If Used Out of Context and Without True Values Stand To Be A Huge Discriminatory Practice Against Consumers–More Honest Data Scientists Needed to Formulate Accuracy/Value To Keep Algo Duping For Profit Out of the Game
If analytics are used “out of context” yes it will in fact keep driving the nail even deeper with inequality. Christopher Steiner makes this point very well in this TED video when he talks about how algorithms are taking over the world. He states “who gets to control them and when does the automated algorithms cross the line between menace and a utility”…good question. Are these “data scientists” that will make this decision? Will it be the “data scientists” that direct people to write apps to carry out the “desired” end result? Will that end result be accurate or desired?
I am guessing the term “data scientist” is here to stay as it has been so widely assessed by so many in business as a “needed” person as like the mountain that data is there and something has to be done with it…and will the financial and healthcare financial areas be able to accept the amount of errors and false relationships that unexplored non linear data creates? How many consumers will be ill fated? In healthcare we see it all the time with algorithms used to deny and some were caught running a “breast cancer” algorithm in the past and we had United Healthcare with their 15 year algorithm that ran for 15 years to short pay.
So will these so called “data scientists” be able to draw the line between utility and menace, as we already know it has failed on Wall Street as there are far more “rich” menaces” than there used to be and again is this really science? I still think it’s an oxymoron but you can beg to differ with me if you like.
Last but not least will the people who take on these jobs ever be able to live up to what the general public “thinks” they can do with a horrifically high level of expectation and accuracy…hmmm….I’m chuckling on that one as the present level of Algo Duping and people’s expectations and what is really there is already causing some big awakenings for many:)
I find myself so much of the time today explaining this to individuals
and can imagine how it might grow to stifle itself as the OMG media and drama queen stories pick up with the “good guy” “bad guy” routines with driving emotions. We find this today amass in the media instead of an alternative of some helpful logic. “Data Scientists” as they exist and continue to grow will be anything but drama with their research and again is this science, really? BD
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