[_qbxp]

I'll be completely honest, as your standard "data scientist" who hopped on the bandwagon and came from having a PhD in academia in an unrelated field, I cringe at these articles. I'm not entirely sure why. I think it may be two-fold:

1. A little bit of the selfish "oh no, the secret's out, at what point is my salary going to drop when the demand is met by the dedicated Master's degrees and bootcamps?"

and

2. These articles seem so incredibly corny, it's almost embarrassing. The "hottest job"? Ahhhh, stop it. But these things go in an out of phase, similar to back in the day when "anesthesiologist assistants" (CRNAs, AAs) were the hottest thing for Bloomberg to talk about. It will not last forever.

The irony is that I probably only knew "data science" (always in quotes) existed because I read one of these cheesy articles. I mean, we all know that statistics have been around forever, but that there were dedicated positions where you could run stats, build models, and then deploy them all in a single role was foreign to me.

So it's a combination of a potentially irrational fear of self-preservation, and laughing at the state of affairs where some basic stats work will pull in that kind of money.

I tend to have fears about the future, always wanting to hedge myself so I don't become outdated. In the data science sense, I see the field becoming super super broad and eventually saturated with new supply, so I debate on whether I should pivot into management of analytics in general or not. I.E. getting my hands off the keyboard. Ultimate goal would be to help define, strategically, how statistics/data mining/machine learning/yada/yada/yada are used at a company.

[peatmoss]

My goodness, are you me? I've been having exactly the same thoughts. Provided one finds a data science role that roughly aligns with your training / interests, the actual work is comically easy compared to what you go through in academia.

A boot camp can easily teach someone to, say, estimate a linear model or run k-means. I dread the future when the industry decides the right way to put up barriers is by creating ever less-realistic interview loops that are even more coin-flippier, dice-rollier, card-shufflier.

[CuriouslyC]

A boot camp probably won't tell the process to make sure a linear model is the right choice. It probably also won't tell you when you should and shouldn't use k-means. And in most cases you probably won't have very good answers as to the certainty of your models.

Imagine you're hiring someone to build a house for you. Would you feel comfortable with someone who's just been drilled on how to use individual tools? I would want someone who had been taught a step by step process for how to put together a house.

[peatmoss]

> A boot camp probably won't tell the process to make sure a linear model is the right choice.

Very true. On the other hand, it's a pleasant rarity when I see positions that appear to index more heavily on, "how well is this person able to conceptualize the problem and choose an appropriate method?" than "can this person do X?"

Lots of folks can do X; fewer can conceptualize a research question and choose the appropriate X; even fewer can carry out the X and communicate robustly what it means.

The latter two start to get into squishy territory, but also are where the value is. They also seem to get the least focus in advertising / recruiting / interviewing data scientists.

It reminds me of studying evaluation methods in planning. One that people are really familiar with (at least anecdotally) is cost-benefit analysis. Conceptually, it's very simple. The problem is that the costs and benefits that are hardest to measure are very often NOT measured. And they're very often the sorts of things that people find the most important. So, you end up with an answer that encodes a ratio of easily measured things rather than important things.

So too with data science. Easier to check whether someone can remember basic probability rules and carry out a linear regression than it is to diagnose whether someone can reason carefully about an amorphous business problem.

[rossdavidh]

If the example of coding bootcamps is indicative, then there will be a period of hiring of Data Scientists with minimal background, and an institutional learning period of "oh they are cheaper but don't deliver results we can use", and thus the job will still remain one with more openings than qualified candidates for some time to come.

[baxtr]

Just add a grain of AI or blockchain and you’ll be fine!

[frockington]

Do a linear regression, call it ML with AI and you'll be running your own team in a week

[sannee]

Just the other day, I heard someone talking about a "single layer neural network with no activation"...

[retbull]

y=mx+b :D I won

[subcosmos]

with just a single neuron!

[abhishekjha]

What does that look like?

[tzahola]

Genius.

[peatmoss]

Hmm... do you offer career counseling services? How can I sign up? ;-)

[DrNuke]

Stop worrying & go vertical, aka use these (and any other momentarily fashionable) tools in the niche domain you are a PhD expert, so that you will never become outdated.

[finebalance]

I don't think it's irrational. As somebody with just a masters in a tangentially related field (economics), I find it surprising how low the barrier to entry is for this job (I got in, after all). Anybody with a decent undergraduate level understanding of stats could learn to do most of the production stuff that companies generally do in a few weeks, at best.

[achompas]

I agree completely with the above, and I feel like I'm in a similar position. Mind if I contact you to discuss a bit more?

[wakkaflokka]

Sure! My screen name @protonmail.com

[achompas]

Thanks! Just wrote you.