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 outcomes that affect people's lives, and not much money.
And in my opinion, as inexperienced as it might be compared to more established scientists, computational biologists are ready for biology, but biologists are not ready for computational biology.
I wouldn't call it virtuous, but it is deeply intellectually satisfying.
Agree about biologists not being ready - computational biology needs more computer scientists, not more biologists.
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 growing more food won't necessarily help).
IMHO, the important problems in the world are much more social and political, than technological. The work twitter does may easily have greater beneficial impact, direct or indirect (Arab spring and all that), on global food security than working for Monsanto on GE crops. I wouldn't be so self-righteous for choosing to work on "hard" science problems. Are you really doing it for the benefit of the world, or just for the deep satisfaction of your own curiosity?
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.
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 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.
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.
I agree that there are intellectually satisfying problems to solve. However, without getting into the tedious debate on the values of basic science vs translational science, how much of that intellectual satisfaction is mental masturbation?
Are these problems really that important? How much of the cool intellectual questions will directly give you a meaningful biological interpretation? Perhaps this is more of a comment on our field. I found a lot of the intellectual satisfying questions during my phd to involve algorithms/data structures, which mostly are just the tools to get at the biological interpretation.
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, especially if you're in a hot area like bioinformatics, have made a name for yourself, and can get a position at a top-30ish place. Unlike in tech, those salaries often come in places with a lower cost of living than SF, too (at least if you want them to). Six figures goes pretty far in Atlanta, Austin, Urbana-Champaign, Ames, or Raleigh, for example.
You could beat that in industry, but either way you're making solidly in the top 10% of U.S. salaries. And if you really need more money, most universities will let you do 20% consulting time, or do a spinoff startup. There are admittedly other reasons not to go into science academia (the list is pretty long, actually), but fear that you'll have to take a vow of poverty doesn't seem like a strong one.
Depending on the institution, you may make slightly more than the minimum (starting at $39K), but postdocs in the sciences do not 'typically' make $50-$60K. As a monetary investment, academia is about as poor a bet as you can make: spend 5-6 years making ~$25K then another 3-5 years below $50K. Then you might be able to start making professor money if you're in hot field and willing to sell your soul to your work.
Sounds like recent American graduates may want to start reading the job listings in Europe, though. A postdoc where I teach in Denmark has a minimum civil-service salary of ~$55k, and in Switzerland the going rate is well above that: a friend works in Lugano, fresh out of grad school, for somewhere in the neighborhood of $80k, although that's a bit above the norm. Postdoc candidates with strong tech skills have good demand at institutions with large EU projects, so they're not lottery-win positions either.
Meanwhile the postdoc could have been making $100-120k for a profession where the job market is almost the polar opposite, which makes a difference for job security/stress. The 2x-4x salary difference, especially during the late 20s and early 30s, is a pretty big deal, especially if you're able to save that extra 2x-4x.
The even crappier pay in humanities makes it basically undoable except for the upper class or outsizedly talented. The low pay in science makes it doable for normal people, but there is a strong financial incentive not to.
Straight wetlab postdocs are usually around the NIH levels (~40K). For computational postdocs (especially if you have a good biology background), 50-60K isn't out of the norm.
Also a Post Doc from a top lab directly correlates with how much $$ you can make in industry.
From : 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.
A couple of years ago I got a long term freelance gig which I was describing to a professor I talk to about once a year the other day. He said:
"Interesting work, good money, and they leave you alone to get on with it. Sounds brilliant".
Although my current work has next to nothing to do with what I did at uni, it was a valuable experience. However I don't think most people are as lucky as me.
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.
 Figure 1.6 of http://royalsociety.org/uploadedFiles/Royal_Society_Content/...
The pay is shit, you're at the whim of the funding moods of the day, and contrary to your last statement most of the results don't really ever go on to affect anyone.
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 position in computational biology.
I worked in a lab at HMS that sounded interesting on paper but wasn't all that interesting in practice. The researchers did the same stuff as you would at The Office, they checked ESPN.com, went to meetings, typed in some SQL and Perl codes for a while, went on coffee break, complain about something, go to another meeting, fart around with the design of their conference poster, etc ad nauseum. Then they all just went to go work at some big corporation, anyway. The upside was low expectations so I was able to work almost full time as a contractor on something interesting at the same time.
Not to belittle the meaningful point, but becoming a "data scientist" at a startup or large corporation that makes their money by advertising is analogous to becoming a 'quant' on wall-street in the 80's. You have to be in it to make money, rather than caring about the types of data the developed algorithms are applied on.
Perhaps my short comment sounded more arrogant than I am... I'm motivated by wanting to help people. If given the choice between trying to help alleviate starvation for little money and trying to optimise advertising on some website for a shitload of money, I'll take the former.
Interesting that you think moral judgements are an indicator of self importance.
Is culture important? Is The Big Lebowski frivolous? Is Old Navy Performance Fleece is a waste of time? Should the cast of SNL all quit and start learning R? Do those folks not pay taxes and thus support most academic research?
I just have to challenge the assumption that it is obvious which things are important, moral, and noble and which things are frivolous. Perhaps in hindsight those things are clear. History will be the judge, as a wise man once said. Or maybe he wasn't wise. Or maybe he made some unwise decisions and learned from them. Or maybe it doesn't matter, and I'll give him the benefit of the doubt because the secret to happiness is thinking happy thoughts.
Definitely not. But the way one expresses them certainly is.
There is not necessarily less money in these fields, but a much much greater potential impact!
An alternative is to get a programming job doing something relevant (e.g. something with applied machine learning) and use those skills to work on open-source bio projects in your spare time. You'd then have some money, relevant experience, and demonstrated interest which could be a good foundation for graduate work if you decided to go that route, or for a career in data science if you don't.
Not to mention frustration surrounding funding for primary data generators and then all the other problems related to the extremely competitive world of academia.
- statistics, probability, and especially probabilistic inference
- 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 the major high throughput biological technologies and the kinds of data they produce, along with the current tools used for processing the data
You could pick up all of that in a year of intense self-study, and less assuming you already have some of those skills.
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)?
My profile says where I work.
I've conducted countless interviews / hires where it basically went: candidates P & Q are the best on paper and in person, but candidate P said x, y, z or did a, b, c, and seems to really want this job and work in our company
x, y, z was sometimes as simple as enthusiasm, and other times was in describing what he/she did in their spare time. a, b, c was usually a project for work, school or fun that was highly relevant.
Intellectually, I think I know that "enthusiasm" is a poor / weak predictor of success. But, emotionally, it's a go-to tie-breaker.
If you're going to go the coding route, put up a working page, publish a blog entry about it, publish a working app, etc.
The key is to show effort (I spent time on this) and relevancy (I'm solving a problem that you might care about).
A few little 1% gains from some A/B tests, or looking at geographic breakdowns of customers from IPs or addresses add up.
Thanks for the link. Do you know anyone who went through the program?
If you're not comfortable with putting me in touch, that's fine.
So the claim that recursive programming is the only or primary method of iterating over large data sets requires some explanation...
If you want to pay money for experience, why not get an actual degree from an accredited institution?
A more realistic alternative to Insight is to do a (paid) internship at a tech company. This is the path I took.
I highly recommend internships and they are wonderful if you can get one. Unfortunately not everyone can be so lucky, either due to lack of experience/technical abilities or an advanced degree (not everyone goes to college). I believe these alternative educational routes are democratizing such industries and many of them offer scholarships and tuition assistance programs.