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The current problem with things like data science thought pieces and MOOCs is that they don't prepare for the realities of machine learning/data science in the real world. (longer blog post on the subject: https://minimaxir.com/2018/10/data-science-protips/)

Doing a unique project is much better for learning cost/benefits of implementing AI/ML, although this post may be overoptimistic on how that can lead to a job offer.

Thanks for posting the article. While I've read up on ML, I don't work in the industry. I'm curious to see what other people think of the article. Can other people weigh in?

I agree with my sibling comment and would add:

Max is correct to point out the irony in his anti-thoughtpiece thoughpiece as he falls into the same trap of vagueness as those other articles. Specifically, he rails against general “black box” approaches to modeling, then takes a general “black box” approach to the work of operationalizing a model (much harder than building the prototype to begin with!).

The discussion of “pulling data” does not match the practical reality, since pulling via BI tools is not scalable and rarely automatable. SQL may cover this insofar as you dump data from SQL to...what, though? A Python session on your laptop? Automating this process allows a data scientist to scale their impact.

For more specificity on engineering practices required for data science, I recommend Robert Chang’s series of posts: https://link.medium.com/CG7c7mQdyS

For details on how a data scientist can impact an organization, I recommend this from the FirstMark blog by Jeremy Stanley and Daniel Tunkelang: https://firstround.com/review/doing-data-science-right-your-...

For clarity, I only covered the data science part of the perspective; the data engineering/DevOps part is another, more difficult can of worms which would require its own post!

I'm a junior SWE at a mid-sized AI startup. The article is pretty much spot on, but I'm not sure that the romantic view of data science the article argues against is actually that prevalent.

It seems like you would need a very specific level of knowledge to romanticize data science in that way. Most people know too little (So you're basically trying to build skynet?), and most of the rest are either in the industry or know someone that is, and so have a more realistic view.

I don't know, I did maths in undergrad, maybe some of the more clueless CS majors thought this way.

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