Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

From PhD to Data Scientist: Tips for Making the Transition

insightdatascience.com

Post reply on HN
Print view View original

81–90 of 91 posts

Re: From PhD to Data Scientist: Tips for Making the Transition

#81

Post by gammarator »

_delirium wrote: post #40
daemonk wrote: post #37

Earlier quoted context omitted.

I am graduating phd bioinformatician, most likely going to transition into industry. It's very easy to be caught up with the self importance of academia because you are essentially in a bubble. It's great to be passionate about science, but I really dislike religifying academia. It's almost expected of aspiring academics to live like monks and just to be okay with shitty pay and long hours. That's bullshit and academ…

> shitty pay I can see this complaint in humanities academia, but pay in the sciences past the PhD student level is pretty reasonable. You could probably make more elsewhere, but it's not like you're scraping by on ramen noodles as a bioinformatics professor or anything. Postdocs typically make $50-60k, and professors start at something like $90k at the minimum, easily up to $120k, $150k, or more after tenure, especi…

Those salary expectations only hold in the very top tier of universities.

From [1]: Median starting salaries for assistant professors are more like $75k. Median full professors--who are nearly 50 years old--are earning $120k. (Admittedly this does not control for field.)

That's about what a green PhD gets offered at age 28 for a data science job in SF.

[1] https://chronicle.com/article/aaup-survey-data-2013/138309#i...

Re: From PhD to Data Scientist: Tips for Making the Transition

#82

Post by jtmcmc »

Blahah wrote: post #4

Sweet, according to his list I'm over-qualified. Interesting to think it would be so easy to make the transition to data science. Except I can't imagine wanting to work on less important problems than the ones I work on now. Global food security vs. social network analytics. Yeah, fuck the money. edit: calling all data scientists - why not consider becoming a computational biologist? We have hard problems, real outco…

really messy data with political silos surrounding access to it and often a really shitty sample size:feature space size ratio.

Not to mention frustration surrounding funding for primary data generators and then all the other problems related to the extremely competitive world of academia.

Re: From PhD to Data Scientist: Tips for Making the Transition

#83

Post by Blahah »

kylebrown wrote: post #67
Blahah wrote: post #44

Earlier quoted context omitted.

There is definitely an academic bubble. The infamous ivory tower. But in biology specifically, many of the problems are objectively important (as judged by society). And some people really do love spending all their waking hours working on them, and don't give a crap about the money. I wouldn't call it virtuous, but it is deeply intellectually satisfying. Agree about biologists not being ready - computational biology…

One bubble which could use piercing is the hard science one (please, humor me). Why do you think "improving the efficiency of photosynthesis" will have a greater impact on global food security than improving the efficiency of social and commercial networking? If I'm not mistaken, economists (eg Amartya Sen) agree that food insecurity is caused by dysfunction in the distribution mechanism, not by a lack of supply (so…

That's an excellent point. Food insecurity is caused by a wealth of factors including social, logistic and agricultural. In addition to the problems you highlighted, were are currently approaching the maximum yield capacity for many crops, and are pushing the maximum land under cultivation for some.

There are huge problems to solve in all those areas. The biological problems are made more important by the lack of progress in solving the world equality problems. By 2050, when the world population is something in the region of 9-12 billion, either billions will be starving or we will have solved one or more of those problems. The science problems are tractable, while the others are ill defined and involve many factors we cannot control, so I think there's a stronger moral imperative to work on the science.

The other consideration is that working in a job that, by chance, invokes positive social results is not equivalent to working directly on trying to solve a problem. Progress in science suffers because there aren't enough good people working on these problems, because so many of them are seduced by industry.

I don't work for Monsanto; that's a straw man. We're talking about academic computational biology jobs.

The answer to your last question is: both. I couldn't do a job where I didn't satisfy my curiosity. But I know working in tech would do that just fine - there are hard problems in many fields. I chose science because I want to use whatever skills I have to try to solve the problems I see.

Re: From PhD to Data Scientist: Tips for Making the Transition

#84

Post by vsbuffalo »

kylebrown wrote: post #67
Blahah wrote: post #44

Earlier quoted context omitted.

There is definitely an academic bubble. The infamous ivory tower. But in biology specifically, many of the problems are objectively important (as judged by society). And some people really do love spending all their waking hours working on them, and don't give a crap about the money. I wouldn't call it virtuous, but it is deeply intellectually satisfying. Agree about biologists not being ready - computational biology…

One bubble which could use piercing is the hard science one (please, humor me). Why do you think "improving the efficiency of photosynthesis" will have a greater impact on global food security than improving the efficiency of social and commercial networking? If I'm not mistaken, economists (eg Amartya Sen) agree that food insecurity is caused by dysfunction in the distribution mechanism, not by a lack of supply (so…

I completely agree. I work in plant sciences (as a bioinformatician) but my background is in econ, pol sci, and stats. Unless things have changed since I switched fields, most global food security problems are market related. There's more than enough food, and we're really good at getting it all around the globe quickly. Also, huge amounts of food are lost due to post-harvest losses. I'd bet a beer that in terms of absolute food weight to mouths, post-harvest research may have higher benefit/cost ratio than photosynthesis research. Evolution has been pretty damn good at getting photosynthesis as efficient as possible (read R. Ford Denison's Book Darwinian Agriculture for this point argued well).

Having experience in both fields, I don't work in plant genomics because I want to feed more people (I do, but if that's what solely motivated me I'd be working under economics still). I do it because genetics is awesome, and plants are great to study.

But, I'd argue that the hard sciences are always a good worth investing in. Being capable of trying to understand our world with the scientific method is something that is uniquely human. We should use this talent as much as possible. Drosophila (fruit fly) genetics is a great example. Decades ago, drosophila was chosen because it was cheap to grow in a lab and had a short generation time. Yet through drosophila we've learned so much about genetics, development, and evolution in ways that are just unparalleled. Yet Sarah Palin[1] and others attack it as a silly waste of money. If we'd have limited drosophila research decades ago because we didn't think an organism with a ~700 million year split with humans would be useful for us, where would be? Much stupider, and much worse off. Basic science matters, big time.

[1]: http://www.youtube.com/watch?v=HCXqKEs68Xk

Re: From PhD to Data Scientist: Tips for Making the Transition

#85

Post by Blahah »

gammarator wrote: post #78
Blahah wrote: post #4

Sweet, according to his list I'm over-qualified. Interesting to think it would be so easy to make the transition to data science. Except I can't imagine wanting to work on less important problems than the ones I work on now. Global food security vs. social network analytics. Yeah, fuck the money. edit: calling all data scientists - why not consider becoming a computational biologist? We have hard problems, real outco…

The majority of PhDs will not obtain permanent careers in science. In the UK, it's less than 4% (0.45% professors) [1]. Most of your peers--and perhaps even you--will find themselves searching for careers in a new field at some point. Let's not badmouth them for taking a good opportunity. [1] Figure 1.6 of http://royalsociety.org/uploadedFiles/Royal_Society_Content/...

I don't think I badmouthed them, I just said I can't imagine wanting that. Being forced into it is another matter. I totally sympathise with anyone who is forced out of science due to lack of jobs.

Re: From PhD to Data Scientist: Tips for Making the Transition

#86

Post by Blahah »

vsbuffalo wrote: post #84
kylebrown wrote: post #67

Earlier quoted context omitted.

One bubble which could use piercing is the hard science one (please, humor me). Why do you think "improving the efficiency of photosynthesis" will have a greater impact on global food security than improving the efficiency of social and commercial networking? If I'm not mistaken, economists (eg Amartya Sen) agree that food insecurity is caused by dysfunction in the distribution mechanism, not by a lack of supply (so…

I completely agree. I work in plant sciences (as a bioinformatician) but my background is in econ, pol sci, and stats. Unless things have changed since I switched fields, most global food security problems are market related. There's more than enough food, and we're really good at getting it all around the globe quickly. Also, huge amounts of food are lost due to post-harvest losses. I'd bet a beer that in terms of a…

Evolution has been good at getting photosynthesis as efficient as possible in the most extreme cases. In rice and all other C3 plants, it could be at least 50% more efficient in the majority of field situations. That's what we work on. Projected yield improvements are on the order of 50%.

Absolutely agree about the importance of post-harvest problem-solving, but I disagree about the benefit/cost ratio. There are a few key things in photosynthesis which, if achieved (which won't cost that much), could have massive benefits. In post-harvest research there are many small, localised problems that change over time. It's a less tractable, but extremely important, set of problems.

Re: From PhD to Data Scientist: Tips for Making the Transition

#87

Post by Blahah »

advice4u wrote: post #54
Blahah wrote: post #38

Earlier quoted context omitted.

If you had most of these things I think you would have a good shot at a compbio position: - statistics, probability, and especially probabilistic inference - nix/gnutools - multiple scripting languages (Ruby, Python, Perl, BASH) - at least one data-oriented language (R, Octave) - understanding of molecular biology (read Molecular Biology of the Cell) - applying machine learning tools to new problems - understanding t…

This is something I would really love to do. Where do you work (in academia, I presume)? I have the programming background, and a bit of the bio background... but I am weak on statistics. How much of statistics and probability theory would I need (beyond a basic 1st-year college level)?

It's hard to say exactly, but if you can work through all the problems in Barber's Bayesian Reasoning and Machine Learning, and some other standard 'frequentist' stats text, you'd be well placed to get started.

My profile says where I work.

Re: From PhD to Data Scientist: Tips for Making the Transition

#88

Post by Blahah »

njbooher wrote: post #72
petegrif wrote: post #68

Earlier quoted context omitted.

A lot of statistics. It's core.

It's unfortunate that biological statisticians have hijacked the term 'computational biology'. There's still a lot of computer science to be done in the area, particularly in genome assembly what with new sequencing technologies appearing every few years.

I should have added data structures and algorithms to my list. De-novo assembly, alignment, phylogeny, and pretty much all sequence work rely heavily on advances in maths and comp sci.

Re: From PhD to Data Scientist: Tips for Making the Transition

#89

Post by Blahah »

rdouble wrote: post #33
Blahah wrote: post #30

Earlier quoted context omitted.

I agree about the pay. It really sucks. But the other things depend what you work on and where. If it's cancer or food security, the funding is there and not going away. And you can choose how direct the outcomes are by choosing the position. I'm not saying everyone should do it, but if you're good enough to breeze into highly paid positions at top tech companies, you're good enough to get a really interesting positi…

Yeah, it can be interesting but often times it's almost the same sort of thing as any lousy computing job on a day to day basis, it just pays worse, with weird academic attitudes and bureaucracy tacked on. Plus, when I did it you were stuck using some janky Perl scripts and whatever bogus Java package was promising to replace the perl scripts of the day. I worked in a lab at HMS that sounded interesting on paper but…

That sounds tedious, but it's not my experience. People do leave to work at big corporations because the stress and relatively low pay of academia drives them to it. But the work here is pretty exciting.

Re: From PhD to Data Scientist: Tips for Making the Transition

#90

Post by UK-AL »

gburt2 wrote: post #25

"Recursive programming"... as in, programming using recursion? Why would this be important to "data science"? Surely loops are just as effective.

I haven't read the article but i'm guessing this a reference to divide and conquer methods. Such as map reduce used in hadoop and such.
Post reply on HN