It's what an ill-defined discipline requires: to be jack of all trades and master of none! Choose real skills over mouthful titles.https://twitter.com/suzatweet/status/1098490857374736384 …
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Software is complicated, people are complicated. The mix?
Writing about stuff to learn how it works, mostly in Rust.
Lead Engineer at @TrueLayer. He/him.
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Ehsan Retweeted Suzana Ilić
It's what an ill-defined discipline requires: to be jack of all trades and master of none! Choose real skills over mouthful titles.https://twitter.com/suzatweet/status/1098490857374736384 …
Ehsan added,
Do you think the situation has been changing recently or it's always as shameful as it used to be?
I think at least the hype started to decline around 2016 and the market is saturated now (in US). Frankly, todays Data Science is not as ill-defined as 2015 or so and has fallen back to some extent to its BI origin, maybe like any other hypes ... (1/2)
Yet still evolving ML engineering field has come out with clear objectives and in my opinion will stay much longer as it attackes the problems with better engineering skills. One can argue ML engineering is not new though! For todays problem it lacks maturity in a wider sense.
I'd say that Data Science is naturally factoring out in all the sub-disciplines that people and business were (irrationally) assuming a single person could master on its own. I'd say BI, ML engineering and Data Engineering will come out as legitimate fields, bringing order.
The only issue is size: small companies cannot afford to factor out, hence they still require a one-man-for-all-purpose skillset. Only medium to large business can do that (once they realize it is necessary).
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