
Radical Change Is Coming to Data Science Jobs - ideaoverload
https://www.forbes.com/sites/forbestechcouncil/2019/03/01/radical-change-is-coming-to-data-science-jobs
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dtjohnnyb
Interesting discussion on this article over on the machine learning reddit
[https://www.reddit.com/r/MachineLearning/comments/awu86b/dis...](https://www.reddit.com/r/MachineLearning/comments/awu86b/discussion_agree_or_disagree_data_science_jobs/)

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

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z92
That more likely falls in Data Engineering part.

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thatcantbeit
While some data science tools may be `excel-implementable` (or some
equivalent) in five to ten years, there's significant risk of mis-
implementation of results given how much of a mystery many methods are to the
people who'd use them.

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`?

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stult
I think the counterargument is that there will be some standardization of
these methodologies such that they don't require experts to implement. Similar
to how it used to require a programmer to develop any webpage at all, but now
most common use cases are covered by simple off the shelf solutions like
square space or WordPress. Moreover, the skills will disseminate quickly to
other fields. Originally only accountants used Excel and now it's an
indispensable skill for most professional jobs. So worker bees of the future
will know the differences between various types of NN just like the modern
worker bee can write vlookups or create charts in Excel even though that would
have been a mysterious skill thirty years ago. Meaning your theoretical Excel
package would cover many different types of NN, with users expected to
understand the difference.

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nicodjimenez
Pretty uninspiring content marketing piece.

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.

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scottlocklin
Man that's a nothingburger article.

>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.

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alzaeem
Open source packages such as Uber Ludwig ([https://eng.uber.com/introducing-
ludwig/](https://eng.uber.com/introducing-ludwig/)) - let alone the commercial
stuff - makes you wonder the same thing. Such tools should automate a large
portion of the mundane parts of the data science workflow. While there's a lot
more to the job than building/training a classifier, I wonder what the field
will look like once that part is commoditized.

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denzil_correa
Here's one way to read it - I just removed the word "data" to see if it made
sense.

> "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."

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mark_l_watson
Not just data science jobs: I think most ‘knowledge worker’ type jobs are
going to go through large changes: AI assistants that will make people very
much more productive in their jobs, and fewer jobs available because company’s
will get more work per employee. I expect salaries to soften, with the
exception of people who are at the very top of their fields.

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kthejoker2
That's funny, didn't expect to see that my old boss Nate Oostendorp wrote
this.

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.

