Earlier quoted context omitted.
Are you serious? How would you iterate over a set of rules and a big data volume, without using recursion?
A loop? Can you please explain?
'Explain'?
From PhD to Data Scientist: Tips for Making the Transition
71–80 of 91 posts
Re: From PhD to Data Scientist: Tips for Making the Transition
#72Earlier quoted context omitted.
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)?
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.
Re: From PhD to Data Scientist: Tips for Making the Transition
#73Any possibility for a dev-minded MBA (finance) to make the data science transition? I was pretty good back in the data with respect to R
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.
Are data scientists whom are in demand today dealing with neural networks and machine learning, or are a large majority still working with large sets of data and running correlation analyses/regressions? Your response above seems to indicate that it's not overly complicated.
Re: From PhD to Data Scientist: Tips for Making the Transition
#74Earlier quoted context omitted.
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.
Are data scientists whom are in demand today dealing with neural networks and machine learning, or are a large majority still working with large sets of data and running correlation analyses/regressions? Your response above seems to indicate that it's not overly complicated.
It depends; I personally haven't used neural networks since graduating. Standard machine learning algorithms get used. I work with large data sets all the time. Sometimes correlations, regression things. The point is that 95% of the stuff that gets used on a day to day basis is not hard to learn. Especially if your background in linear algebra, probability and statistics is good. The five percent that delves into more complicated things can be figured out on the job.
Re: From PhD to Data Scientist: Tips for Making the Transition
#75Earlier quoted context omitted.
A loop? Can you please explain?
'Explain'?
I assume the "explain" refers to the most important thing you can learn about recursive programming: that it is often but not always the least efficient strategy when compared with looping.
So the claim that recursive programming is the only or primary method of iterating over large data sets requires some explanation...
Re: From PhD to Data Scientist: Tips for Making the Transition
#76Earlier quoted context omitted.
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.
I doubt that. Firstly because most people don't hate anything in academia, and secondly because you know very little about me. 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 t…
But couldn't optimizing advertising on some website for a shitload of money lead one to develop a novel algorithm or modeling framework that had applicability to diverse fields including alleviating starvation?
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.
Re: From PhD to Data Scientist: Tips for Making the Transition
#77Earlier 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…
It's too bad you got downvoted. Your first question was pretty good.
Re: From PhD to Data Scientist: Tips for Making the Transition
#78Sweet, 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/...
Re: From PhD to Data Scientist: Tips for Making the Transition
#79Sweet, 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…
Calling all data scientists -- why not consider working on medical and/or biological data instead?
There is not necessarily less money in these fields, but a much much greater potential impact!
Re: From PhD to Data Scientist: Tips for Making the Transition
#80Earlier 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.
Certainly new algorithms and data structures are going to be crucial (e.g. Bowtie), but statistical analysis/scoring, even in a heuristic way, is always going to be an essential component just from the nature of the work being mostly about evaluating hypotheses from evidence.