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Mathematicians becoming data scientists

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Re: Mathematicians becoming data scientists

#71

Post by M_Grey »

stale2002 wrote: post #67
M_Grey wrote: post #23

Earlier quoted context omitted.

"Mathematicians becoming data scientists..." At best you're talking about a career change, at worst you're talking about a watered-down version of your intended career.

A watered down version of your career that pays 3 times as much, working on problems that will actually affect people's lives.

Oh yeah, you're going to change the world by figuring out who to market baby formula to, and that internet-connected toaster is a winner.

The money is good though, that's true.

Re: Mathematicians becoming data scientists

#73

Post by contravariant »

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…

Then again, those are things that can easily be thought. The harder parts of programming such as choosing the right abstraction, or coming up with the right approach for solving a problem, or even formulating a problem, are more of mathematical nature.

Re: Mathematicians becoming data scientists

#74

Post by theparanoid »

ordinaryperson wrote: post #63
nerdherfer wrote: post #58

One side of this that bugs the crap out of me: I have a PhD in math and, after getting sick of teaching mediocre students with a palpable aversion to mathematics, I spent a couple of years applying for data science jobs and getting very few responses. The few interviews I got seemed to go well, but I had no offers and gave up on leaving academics. I have experience in software development (C++), I have a portfolio of…

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.

Re: Mathematicians becoming data scientists

#75

Post by rcar »

ykler wrote: post #30

I was a PhD student in mathematical logic, and I know a couple people who got PhDs in this area and became data scientists. One thing about this field (and other areas of math) is that almost everyone in it feels mediocre. It's like 10% of the people are 10x better than the other 90%, 1% are 10x better than the next 9%, .1% are 10x better than the next .9%, etc. The top two people in the field seem to be notably bett…

Your "90/10 all the way down" explanation is a really good one that I may borrow. I often tell people new to the field that because there's more in the overall field of data science that anyone can know, everyone feels like they're deficient from time to time since an aspect you don't understand but someone else does will naturally come up. It can be useful when that happens to keep in mind the subjects that you do know well and others don't to help stave off the impostor syndrome that can crop up.

Re: Mathematicians becoming data scientists

#76

Post by throwaway729 »

rcar wrote: post #75
ykler wrote: post #30

I was a PhD student in mathematical logic, and I know a couple people who got PhDs in this area and became data scientists. One thing about this field (and other areas of math) is that almost everyone in it feels mediocre. It's like 10% of the people are 10x better than the other 90%, 1% are 10x better than the next 9%, .1% are 10x better than the next .9%, etc. The top two people in the field seem to be notably bett…

Your "90/10 all the way down" explanation is a really good one that I may borrow. I often tell people new to the field that because there's more in the overall field of data science that anyone can know, everyone feels like they're deficient from time to time since an aspect you don't understand but someone else does will naturally come up. It can be useful when that happens to keep in mind the subjects that you do k…

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.

Re: Mathematicians becoming data scientists

#77

Post by throw_away_777 »

nerdherfer wrote: post #58

One side of this that bugs the crap out of me: I have a PhD in math and, after getting sick of teaching mediocre students with a palpable aversion to mathematics, I spent a couple of years applying for data science jobs and getting very few responses. The few interviews I got seemed to go well, but I had no offers and gave up on leaving academics. I have experience in software development (C++), I have a portfolio of…

There definitely does seem to be a bias against academia in industry. From my experience applying to jobs, most employers seem to value 1 year in industry more than 5 years in academia. The data science field is flooded with academic applicants right now, don't believe the rumors. But it can happen, I know of physics postdocs over 40 who transitioned to industry, and grad students do it fairly often.

Re: Mathematicians becoming data scientists

#78

Post by ecesena »

laughfactory wrote: post #28
ecesena wrote: post #13

Earlier quoted context omitted.

Personally I think a PhD would be better than an MBA if she really wants to do data science, but generally speaking I'm for as more edu as possible, and I'm sure she'll benefit from an MBA too.

The PhD will only be useful in a small (but growing) subset of data science jobs. Their are data scientists who develop new algorithms and techniques, and those who apply the. For application, the PhD is probably overkill and extensive experience with a bachelor's, or a master's is better. For theory the PhD can't be beat, of course. I say the opportunity costs are high, and should be carefully weighed, because I hav…

I didn't say necessarily a PhD in machine learning. I think, generally speaking, that a technical PhD vs an MBA could make for a better data scientist.

This said, I don't have an MBA, and most of the MBA people I know are not technical at all or try to stay away from the "technicalities". There's for sure a huge need of technically and business savvy people.

Re: Mathematicians becoming data scientists

#79

Post by throw_away_777 »

stale2002 wrote: post #66
pryelluw wrote: post #49

Earlier quoted context omitted.

Can you elaborate as to why and how? Im genuinely curious. :)

An engineer is a person who creates real world solutions to real world problems. What matters is not how elegant your solution is, what matters is solving the problem. And doing it under cost and time constraints. An engineer is first and foremost, a person focussed on the end goal, above all else.

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.

Re: Mathematicians becoming data scientists

#80

Post by emmanuel_1234 »

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 because they can hardly prototype their stuff in Python or run an SQL query. And they'd expect to only work on the best, cleaned and formatted dataset and only do high-end maths on those. Reality hits hard, we're losing money paying them and they're wasting their time not doing what they like. Add to that the frustration / jealousy that this creates.

In that regard, I like that famous definition for Data Scientist: "A programmer that know more about statistics than most programmers, or a statistician that knows more about programming than most statisticians".

And don't get me started on the general repulsion for understanding the basics of how a business runs from academia. Data Science is all about application.

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