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04 Apr 2018 12:31:01 PM
Here I am again. A layperson trying to make sense of the complex landscape of data science. And today I'm thinking about data engineering.
When I was a schoolboy I wanted to become a mechanical engineer. I
loved taking things apart, seeing what each part did, and then putting the machine back together again. The problem was that often when I’d put all the pieces back then the object no longer functioned as it once had. It rarely functioned at
So I didn’t take up an apprenticeship as an engineer.
Nowadays - in a world built from data – I wonder if there are modern schoolboys (equivalent to me with my train sets and clocks back in the distant past) taking data architecture apart for fun and putting it back together?
If so, how will they know whether they are gifted, or – like me with physical objects –
the opposite, before they decide whether or not to embark on a career as a data
engineer? In an invisible world, will it be as clear to them as it was to me that the thing they have ended up with is not the same as what they started with?
And, if not, how is this being - or how could this be - addressed in education and training?
Answers on a postcard, please. Or even better you could comment
Message cannot be blank.
04 Apr 2018 12:38:13 PM
Kaggle is a great place to start. Plenty of datasets, challenges and competitions
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