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 outcomes that affect people's lives, and not much money.
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 academics take it while constantly assuring themselves that "it's important and they love it". I am sorry that I am coming off as extremely cynical, but I really don't think propagating the idea that pursuing pure science is somehow more virtuous than other professions helps with the situation.
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.
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.
Ive got strong convictions too and the big data fad (real or not its still a fad) seems specious and unfulfilling. But as a PhD candidate who has had his funding cut and grant proposals continuously denied since sequestration, money of any kind is starting to sound good right now.
Not everyone has the luxury of working in the field they got a PhD in. Two of my friends were looking into switching to data science positions because they were having problems finding positions in their fields. One was on her second post-doc as an astrophysicist, and the other is a soon-to-gradute biochemist. Both did find positions in their fields, but not before much fear and existential angst.
calling all data scientists - why not consider becoming a computational biologist?
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.
You're everything that everyone hates in academia. Congratulations as I hear that self importance is one of the key ingredients to solving the biggest problems facing the world today.
Maybe you could help me out here. I'm split on the whole data science in research vs business. I'm an undergrad Senior majoring in CS (minors in math and Computational Science). All the data science jobs I see for research firms require a PhD. I'd much rather work for a research firm than as an analyst for a business (I think). Any suggestions for someone looking to get some experience before pursuing more schooling? (not like I don't enjoy my classes but I'd rather not drop the dough after undergrad if I can get decent experience and a salary to help pay for a graduate program)
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.
What skills do we need to learn to get into such a position? That is apart from statistics and programming? How much effort will go into learning that stuff?
Technical skills aside, the best piece of advice in the article is "show them that you want it."
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.
Should I start putting every substantial R/Python script I write, even if they are based on some tutorials, on the Github/Personal-Website? Is that how I "show"? I missed the Github bus for all my previous projects.
I'm currently finishing a PhD in economics and have spent a lot of time learning the exact technologies he suggests (Python, SQL, a bit of R). Working as a data scientist would be an awesome opportunity. But are most companies _really_ in need of so many data scientists, or is it just a trend?
I think it's a new name for an old thing, lots of jobs through the last century had things like "analyst" attached to them. Business has been about measuring things for a long time, look at Taylorism or Gosset at Guinness in 1899 for example.[1][2]
A few little 1% gains from some A/B tests, or looking at geographic breakdowns of customers from IPs or addresses add up.
It is a trend. The question is rather, is it a trend that is likely to persist? And that depends on whether you believe that organizations are likely to capture and store more data or less. If you believe the answer is 'more' then the problem becomes deriving insights from it. And that process - is data science.
There is no magical set of qualifications to become a data scientist. Just learn enough linear algebra, probability. Show people you can code. Maybe setup some github projects. It is not like people in tech are doing something magical with all these fancy data scientists. A little bit of math, a slap and dash of code.
[+] [-] Blahah|12 years ago|reply
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.
[+] [-] daemonk|12 years ago|reply
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.
[+] [-] gammarator|12 years ago|reply
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/...
[+] [-] acadien|12 years ago|reply
[+] [-] scott_s|12 years ago|reply
[+] [-] rdouble|12 years ago|reply
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.
[+] [-] besquared|12 years ago|reply
[+] [-] ksolanki|12 years ago|reply
There is not necessarily less money in these fields, but a much much greater potential impact!
[+] [-] dev1n|12 years ago|reply
[+] [-] jtmcmc|12 years ago|reply
Not to mention frustration surrounding funding for primary data generators and then all the other problems related to the extremely competitive world of academia.
[+] [-] rgovind|12 years ago|reply
[+] [-] fitandfunction|12 years ago|reply
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.
[+] [-] rogerchucker|12 years ago|reply
[+] [-] tomrod|12 years ago|reply
[+] [-] jkldotio|12 years ago|reply
A few little 1% gains from some A/B tests, or looking at geographic breakdowns of customers from IPs or addresses add up.
[1]https://en.wikipedia.org/wiki/Scientific_management
[2]http://www.umass.edu/wsp/statistics/tales/gosset.html
[+] [-] fiatmoney|12 years ago|reply
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[+] [-] rogerchucker|12 years ago|reply
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[+] [-] shoyer|12 years ago|reply
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.