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Mathematicians becoming data scientists

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Re: Mathematicians becoming data scientists

#111

Post by throwaway729 »

visarga wrote: post #98
throwaway729 wrote: post #76

Earlier quoted context omitted.

I think you got the parent's meaning exactly backward. The 90/10 thing is about their phd field of study (mathematical logic), and they're saying that something almost opposite of that is true for data science.

I think it's still true for data science. Nobody can keep up with the flood of research taking place.

> Nobody can keep up with the flood of research taking place.

That's very different.

No one can keep up with the flood of JS frameworks, either.

Information fire hoses are very different from extreme differences in ability.

Re: Mathematicians becoming data scientists

#112

Post by chillee »

emmanuel_1234 wrote: post #80
olivierva wrote: post #36

Slightly off topic: They still can't build proper software. Academics (including mathematicians) are notoriously bad at writhing production grade software. This leads to handovers of 'proof of concepts' to seasoned software developer team who than struggle with the (often complex) mathematics/science behind it. Imho universities should give a bit more attention on how to write quality software; a bit of test driven d…

This is precisely why I dread hiring academic-only profile. Note: I am myself from academia, but I was lucky enough to specialized in CS-related field (NLP). When I wanted to leave academia, Data Science was not a thing where I live and I had to start again at a junior dev position (i.e.: it was that, starving or staying in academia). Now, I work with a lot of people way smarter than I am, who are mostly useless beca…

I've always heard the definition of data scientist as "Somebody who knows less about programming that programmers and less about statistics than statisticians"

Re: Mathematicians becoming data scientists

#113

Post by throwaway729 »

olivierva wrote: post #36

Slightly off topic: They still can't build proper software. Academics (including mathematicians) are notoriously bad at writhing production grade software. This leads to handovers of 'proof of concepts' to seasoned software developer team who than struggle with the (often complex) mathematics/science behind it. Imho universities should give a bit more attention on how to write quality software; a bit of test driven d…

> They still can't build proper software. Academics (including mathematicians) are notoriously bad at writhing production grade software. This leads to handovers of 'proof of concepts' to seasoned software developer team who than struggle with the (often complex) mathematics/science behind it.

So you want employees who can understand complex mathematics and science but are also good software engineers.

Those people exist, but you have to pay to get them.

(As an aside, lots of Ph.D.'s -- especially in CS -- build systems that are as good or better than a lot of industry code. All of the best and worst code I've read has come from Academia.)

Re: Mathematicians becoming data scientists

#114

Post by throwaway729 »

kriro wrote: post #91
uptownfunk wrote: post #85

I interview probably two to three people a week for a DS job. It's a really difficult role to hire for. I'm basically looking for mathematicians with some knowledge of stats who also enjoy coding and follow best practices in coding (i.e. They didn't just pick it up and hack something that works but care enough to document and structure so others can follow it at a minimum) but we also need the MBA component as well,…

Interesting, thanks. It obviously depends a lot on the job but I would have thought that a good math background (which typically CS grads should have) would be enough for many applications. Especially since you mention the business problem aspect. I think that you can actually get quite far in DS (at least in deep learning) with rather "basic math". Unless you're innovating at the bleeding edge it's usually enough to…

Exactly. This is one place where the "good school" heuristic is actually a good one -- those programs almost uniformly demand some mathematical maturity of their students.

I've known many CS undergrads who are better mathematicians than people with graduate Mathematics training.

Re: Mathematicians becoming data scientists

#115

Post by throwaway729 »

nickdavidhaynes wrote: post #110
throw_away_777 wrote: post #79

Earlier quoted context omitted.

This definition of engineer is so broad as to be meaningless. There is no "fake world" with "fake world" problems, pretty much everyone is trying to create real world solutions to real world problems. Similarly most jobs are focused on the end goal. A data scientist job usually involves a mix of science and engineering.

>There is no "fake world" with "fake world" problems You've never met a number theorist.

> You've never met a number theorist.

And you've never met a cryptographer ;-)

Re: Mathematicians becoming data scientists

#116

Post by posterboy »

hocuspocus wrote: post #101
frozenport wrote: post #100

Earlier quoted context omitted.

There might be a cost issue. Employers will see the PhD as raising salary expectations without adding value. The only real way to make inroads is to get a job related to your PhD and then do coding work. Typical interview for higher jobs aren't focused on tests or quizzes. If that's what your getting your not applying to the right places - and there might not be many places.

> There might be a cost issue. Employers will see the PhD as raising salary expectations without adding value. It could be true in some fields (biotech and life sciences, pharma/chemistry, "real" engineering, ...) but in math/CS? Most companies hire PhD grads at one job grade/level above entry-level, i.e. it's typically not worth much more than a ~2 years head start salary-wise. If a candidate has spent 4/5 years gai…

Phds might be relatively rare and so advantaged. But maybe not enough to warrant a second Phd if the first one is unrelated to the specific positions. I would add that to the GP otherwise.

Re: Mathematicians becoming data scientists

#117

Post by shas3 »

An earnest question I've heard from people trying to switch is: what's a good benchmark for testing where you stand in terms of coding skills? How do you calibrate and measure your production-quality coding skills in Scala, C, etc. when you have spent all your life in academia?

I feel Kaggle is sufficient for the exploratory part of data science. But Kaggle's relevance to testing and honing "production quality" code-writing skills is sometimes minimal.

In the larger context of programming and software engineering, scientific programming is fairly easy to code-up (though they are harder to conceptualize and understand mathematically). The coding part of non-CS/non-CE/non-some-parts-of-EE academia is pretty much mostly scientific programming. Yet, production quality code is seldom just scientific programming.

Re: Mathematicians becoming data scientists

#118

Post by Almaviva »

laichzeit0 wrote: post #104
kriro wrote: post #91

Earlier quoted context omitted.

Interesting, thanks. It obviously depends a lot on the job but I would have thought that a good math background (which typically CS grads should have) would be enough for many applications. Especially since you mention the business problem aspect. I think that you can actually get quite far in DS (at least in deep learning) with rather "basic math". Unless you're innovating at the bleeding edge it's usually enough to…

It makes me almost feel sad for the people that followed the "why bother doing a CS degree, you can learn to code without one" trend that probably peaked and is now maybe beginning to die a slow death as the market is flooded with people who can write code. If they ever want to transition into DS they're going to have to skill up and do the equivalent of undergrad CS math curriculum (discrete, calculus, linear algebr…

> you cannot pick this up in any meaningful way in a "few months" of after hours/weekend study

You can't pick up coding like this either. See Peter Norvig's famous "Teach yourself programming in 10 years" article. The delta in the wisdom you obtain, between a few side projects over months and battle hardened experience with real products and code bases over years, is immense.

Re: Mathematicians becoming data scientists

#119

Post by cottonseed »

pavlov wrote: post #35
cottonseed wrote: post #26

Earlier quoted context omitted.

This is the wrong comparison. Read the second footnote. An intern is a developer in training. The post is talking about mathematicians (who spent a decade or more learning a highly technical field) going to work in an environment where, largely, their peers, boss and consumers of their work do not know, have no interest in and and will never learn the most basic concepts of their field.

I suspect the majority of programmers work in that kind of environments...? The startups and Googles of this world are the exception when it comes to employer tech-savviness.

The advice is for mathematicians who largely don't.

Re: Mathematicians becoming data scientists

#120

Post by ordinaryperson »

theparanoid wrote: post #74
ordinaryperson wrote: post #63

Earlier quoted context omitted.

It's never too late to switch. I'd suggest figuring out which niche of data science most aligns with your personal interests and start there. Hiring managers don't discriminate based on "too many years in academia", it's more what's the reason they should hire YOU over candidate y? Diehard animal lover? I bet the Sierra Club would love a statistician who could help them quantify how many elephants are being poached i…

As an over 40yr old, yes it's probably too late to switch. After 30 it was a shock to me getting turned down for jobs when I aced the technical interviews.

You can be turned down for a job for a lot of reasons, personality fit is probably the No. 1 reason people are passed on (no data here, just my own anecdotal experience). I wouldn't assume it's ageism.

But that brings me back to my original point, you have to find a place where your personality and interests are a match, not just RandomJob.com.

If you have a higher degree in math, there is always another job out there waiting for you.

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