The original article is here
I think this article will be a trigger of next datascience courses DS is attracting more and more people every day, also from a different domains. Sometimes, those people doesn’t have a PhD or a degree, and of course they could solve ML/DL problems because they are smart! (such Elon Musk :-) ). ML/DL/AI are the new buzzword, and all people want it, though they don’t know why or how…
Not all companies need ML, and ML is not the silver-bullet for all problems around the world. I think that is more valuable that a person could explain an idea, or solve a problem with creativity than know which hypermarameter is the best one… they give the name “storytelling skills”, all is refered to DS.
Think, we have been storing data since 1960…
They need some framework to set us on it. They say “Data Scientist”, they “need” the unicorn, actually they need a fullstack person, with knowledge in ML or DL where required.
When tools (hard and software) will get more stable then AI/ML/DL will be democratized for all, and I think that the market will become stable, and these proposals start to be common.
You need some knowledge baseline, but right now we have the best example… web development. Web Development tools are reach by children, youngs, adults, experts, non-experts, more-than-30-years,… Web Development is democratized now. I hope than by the time, AI stuffs will be able to use by any person.
Summary (or best parts for me)
There are 70% more open roles at companies in data engineering as compared to data science. As we train the next generation of data and machine learning practitioners, let’s place more emphasis on engineering skills.
In talking to dozens of prospective entrants to data fields including students at top institutions around the world, I’ve seen a tremendous amount of confusion around what skills are most important to help candidates stand out in the crowd and prepare for their careers.
Data engineers are in increasingly high demand compared to other data-driven professions. In a sense, this represents an evolution for the broader field.
When machine learning become hot 🔥 5-8 years ago, companies decided they need people that can make classifiers on data. But then frameworks like Tensorflow and PyTorch became really good, democratizing the ability to get started with deep learning and machine learning.
Today, the bottleneck in helping companies get machine learning and modelling insights to production center on data problems.
In addition to learning how to use linear_regression.fit(), learn how to write a unit test too!
There will always be a need for people that can effectively analyze and extract actionable insights from data. But they have to be good.
As I said above, they always exist!