I particularly agree with the comment saying:
no software is capable of trawling through the bowels of the organisation to find out the correct interpretation of the `Extra2` field on the `Sales` table that takes three values: "TRUE", "Error" and null.
This, data cleaning, and understanding how best to store the data for better insight are the true bulk of data science work, very little is the shiny model building work. I guess this is close to the Industry specialist as outlined in the article though
I'd compare data scientists more to CPAs. You can have software like TurboTax and Quickbooks, but CPAs don't seem to be going anywhere. Similarly, anything that's more complicated than cookie-cutter data analysis will require someone who knows how to develop, build, and debug the algorithms themselves.
Use cases of data science are often too specific for most data science to turn into button pushers. Look at the vast array of ways a neural network can be implemented. Which one of those implementations will be in the `excel package`?
Radical change is coming technical jobs in all fast moving fields, whether in biotech / software / hardware / ... all the more reason to spend enough time learning new things.
>Over the coming years, I foresee data scientists dividing into at least five types of workers
Yeah, sorry my dude; that happened before "data science" was even considered a profession.
The other tools; trifecta is a helpful thing, but I doubt it's helpful enough people will actually pay for it. Auto-sklearn/DataRobot was the result of a DARPA request a few years ago, and doesn't even vaguely solve the right problem. It's also just R-caret which has existed for 10 years now.
My prediction: data science in 5-10 years will look pretty much the same as it does now. Just like aircraft in 5-10 years will look pretty much the same as they do now, or did 5-10 years ago.
> "A radical change is coming to science jobs. Similarly, I believe the job of a scientist as we know it today will be barely recognizable in five to 10 years. Instead, end users in all manner of economic sectors will work with science software the way non-technical people work with Excel today. In fact, those science tools might be just another tab in Excel 2029."
Anyway, the sooner enterprise stops putting data science in an ivory tower the better off we'll be. The number of Fortune 500 clients I have who can't seem to grasp that an algorithm or model by itself is not a business solution is disheartening.