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"Have you or somebody you know successfully made the transition from (non-computer) science into a tech career? In the current economy, what are the chances you'd hire a highly motivated science dropout with programming competence and basic stats knowledge?"

I did it, have worked with others who have done it, and would now hire people who did. The important part is your ability to program. There are tons of PhD students (even in CS), who can't (or won't) write code. There are even more who write horrible, unmaintainable code. You have to be better than those folks.

Also, do not be deceived by "data science": it's mostly a bullshit term, and translates roughly to "programmer who knows basic statistics", rather than "scientist who knows some programming". Nobody wants to hire you if you can't implement your theories in a production context.

The bottom line is that if you're a good coder, nobody cares how you wasted your youth.

Also, do not be deceived by "data science": it's mostly a bullshit term, and translates roughly to "programmer who knows basic statistics"

This is utter rubbish; I really wish people would keep quiet about things they know nothing about. I suggest that you have never actually discussed a domain with a data scientist if you think it's "basic statistics".

In our dev. shop, we have a lot of great programmers, but none of them can touch our data scientist when it comes to working out what our tens of millions of users are actually doing and what their salient attributes are.

As for the data scientist needing to 'implement their theories', that's what the developers are for. The data scientist does the analysis, then works with the developers to implement systems that incorporate the results. Neither group is capable of the other's work.

"I suggest that you have never actually discussed a domain with a data scientist if you think it's 'basic statistics'."

Utter rubbish, perhaps. But since I've actually done the job, I do happen to know something about the subject. It's a marketing term, not a term of art.

The vast majority of "data science" performed at web companies boils down to knowledge of summary statistics and probability theory, a smattering of basic statistical models, and (most importantly) the ability to write code. There's not much that would challenge an advanced undergraduate, let alone a doctoral-level statistician.

I did it. I did a Master's (never had any intention of a PhD) in engineering, mostly focused on OR decision making applications in geography. So I knew maps and could write Matlab and Python scripts and I'd played around with some C/C++. I progressed into a tech career starting with a place where the mapping & geography knowledge was a huge asset. My CS experience was limited to 2 undergrad courses and reading a few books. It's taken a few jobs since then and about 5 years but I'm definitely purely a software developer now. Two things that I think helped were having a technical bachelor's degree (even though it's not CS) and having some projects I could talk about that that meant I knew stuff about the company's products that CS students wouldn't tend to know, like mapping projections and norms in GIS. When they asked me questions about stuff like sorting algorithms, I could explain some assumptions I would make based on the kind of work they do, not just give the generic 1st year CS course answer. Dev Bootcamp or similar might be worthwhile to give you some buzzwords for your resume and some projects you can show off. If you can get those on your own or your experience buzzwords match what you see in job postings, then it's probably not worthwhile. I wouldn't put the bootcamp on my resume, I'd put the skills and projects it got me down as personal interest projects.

I've known several people who have transitioned into the tech industry without a CS degree. Particularly good examples that I can think of got their degrees in physics or some type of engineering; I even knew a guy who got his degree in political science. Really, anyone who is interested and motivated enough can learn programming, analysis, and good Unix practices.

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