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Mathematicians becoming data scientists

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Re: Mathematicians becoming data scientists

#81

Post by stale2002 »

M_Grey wrote: post #71
stale2002 wrote: post #67

Earlier quoted context omitted.

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.

The money is good because people are willing to pay. If the world needed more PHDs, then the world would pay them more. Thats how markets work. Supply and demand.

Re: Mathematicians becoming data scientists

#82

Post by stale2002 »

throw_away_777 wrote: post #79
stale2002 wrote: post #66

Earlier quoted context omitted.

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.

My definition of "real world" problems are ones that people are willing to pay for. If nobody is willing to give you money for whatever it is that you are doing, then it probably isn't solving someone's actual problem.

Re: Mathematicians becoming data scientists

#83

Post by mrcactu5 »

I remember asking why i was rejected from Palantir and vaguely recall the phrase "too mathematical".

After years of trying I have been unable to make the transition from Mathematics into Data Science and have since shifted my attention to other things.

Re: Mathematicians becoming data scientists

#85

Post by uptownfunk »

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, a large part of this industry is how do you take what a DS does and deploy it in a business and ensure the business derives value from it. But I think coming at it from a mathematical background puts you in a much stronger footing than the cs guys who try to learn the math later. I rarely find strong candidates without a formal mathematical background.

Re: Mathematicians becoming data scientists

#86

Post by digitalzombie »

11thEarlOfMar wrote: post #10

I've been encouraging my daughter, a statistics major, to pursue data science by including Python/R in her studies and then possibly heading back for an MBA. But not sure if an MBA would be a benefit. Thoughts from actual data scientists?

Not actual data scientist but I have a classmate with MBA. We both were in CS in undergrad.

She turned out she didn't like managing people and politics so yeah...

If you want data science stat and comp sci is the way to go imo. I'm bias cause I'm doing stat for master now.

There are some stat classes in MBA and they do prediction and stuff but your daughter may not like it. I certainly don't, they do power point and excel and visualization. Their regression classes ignore checking if their model's assumptions are valid are not (QQplot, residual, etc...) it's very dumb down.

Re: Mathematicians becoming data scientists

#87

Post by alexpatin »

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 might be more expensive than some of the youngins too. Just a possibility. I'm sure there could have been something else but new kids on the block often take less than they can.

Re: Mathematicians becoming data scientists

#88

Post by IndianAstronaut »

wolfkill wrote: post #70

This post mostly describes me. I am a PhD student in applied mathematics and returned school to get my PhD precisely because I wanted to get a job outside of academics, most likely as a data scientist or scientific programmer. 2.5 years into my program I still struggle to see beyond the degree but am currently looking at non-academic internship opportunities as a way to start to untangle myself from academics. I also…

Take a look into data engineering. A lot of data engineering jobs require you to implement mathematical models at scale.

Re: Mathematicians becoming data scientists

#89

Post by godmodus »

A few fellow students graduated with a BA and had their aplications rejected by most companies due to a lack of experience.

Theory is nice. But Experience trumps all. Esp when you think it mostly boils down to PCA/DBSCAN, regression and scatter matrices.

A month of Python/R/D3 on some initial data like pubmed/twitter or any toy dataset would go a looong way.

Re: Mathematicians becoming data scientists

#90

Post by kriro »

godmodus wrote: post #89

A few fellow students graduated with a BA and had their aplications rejected by most companies due to a lack of experience. Theory is nice. But Experience trumps all. Esp when you think it mostly boils down to PCA/DBSCAN, regression and scatter matrices. A month of Python/R/D3 on some initial data like pubmed/twitter or any toy dataset would go a looong way.

I think doing well on Kaggle competitions would already be a good enough indicator for smart companies so it might be a good idea to have student teams do those on the side. Might be a decent idea to inform students that these exist. I feel like this applies even more for other degrees like econ(ometrics) (since I feel like a good math grad should not have trouble getting any data science job).
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