I run into this issue on my resume. Do i throw random skills i spent 4 months learning for some project and never used again? I feel like overloading these things devalues the skills i actually AM exceptionally competent at, as opposed to just capable.
My advice? Remember that your resume goes through at least 2 filters, HR and IT.
What happened was:
IT boss: "we need to hire a coder"
HR: "What skills?"
IT Boss: "Oh, Foo language. But if they're a good coder they can pick it up, so just a good coder"
HR: "...you're kidding, right? There are millions of resumes out there, most from people with no skills just trying to land a great job. Give me enough to filter"
IT Boss: (provides list of three things)
HR: "This still isn't enough. Practically EVERYONE will have these. Give me years of experience, skillsets, processes, etc.
IT Boss: "Fine, here" (gives long list of things that MAY be useful)
HR: (starts filtering resumes based on these words, removing lots of good people and including lots of bad people)
IT Boss: (looks at resumes) "These people are clearly lying and all over the place, I'm going to focus on one or two things to decide who to interview"
So in writing your resume, you want to make sure you have the buzzwords for the job to get past HR. These buzzwords are pretty much guaranteed to be on the job listing, even if they end up not being very essential to the job. Did they mention Scrum? Better have it on your resume, because you may be filtered out if it isn't, even if it's something you'd not consider worth listing. Also, use the same words. I once was asked if I had "shell experience", even though BASH was on my resume. I assume "Agile" and the various implementations are the same. If they mention XP, you better mention XP.
BUT when your resume then makes it to IT, who (1) know what these words mean and (2) aren't looking for the same things at all, you need to have what they want. I tend to use a sidebar on my resume to capture the HR buzzwords, and emphasize my work experience in the main body, so an IT person skimming it will see what they want to see.
One technique I've taken to handle HR buzzwords on things that I don't think are actually a big deal: If the job listing says "Must know React, Angular, Backbone, or other JS frameworks" and I wasn't really strong in any of them, I'd do enough research and testing coding to do a Hello World in them, then add "Exposure to Foo, Bar" on my resume. It tends to get through HR (word is present!), and I'm not lying to the IT people - they understand that I'm not claiming expertise, but I'm also saying I'm willing to give it a go.
As a corollary to all of this, you need to tweak your resume for every job posting, to match their buzzwords and remove ones they didn't list that aren't really core to your skills.
I do research on Deep Learning. That seems the exact kind of question they would deem relevant to their field.
I don't know much about NLP but if I had to guess the answer to your question is include it in the training data. I don't know what model they use, but presumably it parses out sentence structure in a specific format and uses that as input to some neural network architecture.
Not really sure why he didn't answer your question, it's the exact kind of question people do NLP to answer.
I think this is one of the reasons Econ as a field has stagnated. (THIs is v hard to defend but bear with me). People have no reason to share / peer review their findings. It is extremely advantageous to share no, or bad advice. Hence why you get the 2007/08 housing bubble, as hundreds of econ professors peddle COMPLETELY false economic theory and results based analysis.
In fact, many still are. Not much has changed in the field of economics. How data science isn't a mandatory requirement for such a data driven field just shows to me how immature the field is.
? I mean economics has been politicized extensively. Other than that economics is applied math and behavior. Systems of equations, Markov chains, game theory, ... Then there are specific levers one can push (inject money, take out money, regulate or not regulate). These basically modify the transition probabilities on specific states. 2007/08 was predictable fundamentally because wages didn't keep up with house rising house prices.
This. Python's stength as a language IMO comes from it's ability to interface with more efficient code. You can write the meat of your library at near C levels of efficiency, and have python treat it like a "black box" essentially. That seems to be how NN code works often as well.
Given that the R interpreter is written in C and admits trivial FFI bindings, as demonstrated by libraries like glmnet or gbm calling out to it, I don't see how this is an inherent advantage of Python.
While it doesn't have an inherent advantage, it has the mindshare and momentum of a community that has these tools now.
R could be just as capable as Python, but I think Python has largely won the race to be the most popular language for data analysis which in turn encourage more developers to commit to it, cementing Python's advantage.
R still has solid lead in statistics and a good mindshare amongst academics.
> R could be just as capable as Python, but I think Python has largely won the race to be the most popular language for data analysis which in turn encourage more developers to commit to it, cementing Python's advantage.
Your comparing Apples and Oranges. R is a domain specific language and will never be a general purpose language.
Let alone in industry investment coming from Microsoft and other major players.
R is above Python in Statistics in momentum and numbers. Python is a good choice but Python is still playing catch up to R due to the speed at which R is developing. R with data.table and Hadleyverse (https://www.r-bloggers.com/welcome-to-the-hadleyverse/) and RStudio the momentum has been clearly on the side of R.
R just 5 years ago was a fraction of the users it has today.
Python and R are both good choices with equal speed but the difference is that R is a domain specific language that has a lot of positive ecco system.
R is a LISP. I would disagree heavily with it being domain-specific. It is as capable and Turing complete as any language. The only argument you can create is about performance and the judiciousness of putting stats functions in the base library, as opposed to Common Lisp which ships with even less. Not only "will" it be a general purpose programming language, it already is.
Now that you've made this comparison, I can never unsee it. They're extremely similar! This makes me wonder whose going to be Musk's Tesla. I.e., what's the first amazing idea he's going to throw away (out of mainly hubris) that actually solves his problems.
