Dario, this is an interesting article. In some years you’ll be able to look back at it and understand that you have learned a lot since today. Like software engineering, data science is a specialist field, and what people — both employers and (potential) employees — tend to ignore is that specialists are not generalists. I’ve worked in software engineering for 25 years now, mainly as an analyst and architect, and I’m very much aware that for a lot of things I need to rely on specialists. What you describe in most of your arguments is related to the work of a what I’d call a data engineer. A data scientist would be one who looks at the data and the needs of the owner/user of that data, to figure out how the data can be used to extract to the _information_ (yes, it’s about information in the end, not data), whereas the data engineer is the one who automates the extraction of that information. This is similar to how a software architect or analyst and a software engineer split their work when realizing functionality. Both parts can be boring or interesting, depending on what you as a person prefer to do: interact with people and understand their needs, or automate what is needed to fulfill those needs. Figure out for yourself which part you like best and then focus on learning that. The other part you need to cover as well, but only just enough to understand what the specialist there can do for you. In other words: be the scientist that tries to understand the data user and make sure you are able to tell the engineer what he can do for you, or be the engineer and be ready to understand what the scientist asks of you (and then realize it of course)

CEO at Schinchoku and software architect at Delphino Consultancy B.V. — writing about software, and about the Shinchoku startup.

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