throw_away_777

8 ms·

31.
by throw_away_777 10y ago
Congrats to the Kaggle team! One great thing about Kaggle was that the team listened and sought out feedback from users (even if they didn't always follow the feedback). I hope that doesn't change with the acquisition.
32.
by throw_away_777 10y ago
Why do you think candidates for data science jobs aren't qualified relative to other positions?
33.
by throw_away_777 10y ago
I really wish "data" journalism did a better job of conveying uncertainty. It is incredibly common to see comments like such-and-such increased 10%, without any comment on what the number of such-and-such actually was or if the 10
34.
by throw_away_777 10y ago
They gave me the standard whiteboard interviews on-site after that. This was for a data scientist position. I did not get offered the job (but I accepted a good job offer a couple of weeks later, so whatever).
35.
by throw_away_777 10y ago
The cost of a mishire is huge and obvious, but don't discount the cost of passing up on a good hire, which is much less visible. But every time a good candidate is passed on, the odds of a mishire go up. There isn't any way to det
36.
by throw_away_777 10y ago
The thing is, the companies giving homework assignments still go through the regular onsite interview process. So it really is just an added burden of stress and time on the candidates. Arguably studying CS problems also helps job performan
37.
by throw_away_777 10y ago
It depends how desperate the candidate is for a job. A coding assignment may have a hidden cost of filtering out many qualified candidates, if qualified candidates don't feel the need to apply to companies with these assignments. Anecd
38.
by throw_away_777 10y ago
Often the assignments are more complex than described. I wouldn't be at all surprised if this was the case here. Also 2 hours is actually a really short amount of time, it can take 30 minutes or more just to understand what the person
39.
by throw_away_777 10y ago
To add on to this, a take-home assignment should be more than just another filter. The companies I interviewed for with take-home assignments didn't actually discuss the assignments at all in the in-person interview - I spent the time
40.
by throw_away_777 10y ago
The lack of feedback from interviews is definitely one of the most frustrating parts. But remember that the success rate for onsite interviews is around 20-50% depending on company (companies don't publish this stat, but see for instan
41.
by throw_away_777 10y ago
I think there is a strong selection bias going into effect here. I know plenty of young people who are struggling to find good jobs and not optimistic about the future, and I live in a "liberal" city.
42.
by throw_away_777 10y ago
"On the fact that median male wage was higher in 1969 than it is today" The obvious explanation for this is women entering the workforce, it is basic supply and demand. While this is good for the economy and society as a whole, cl
43.
by throw_away_777 10y ago
I agree that neural nets are state-of-the-art and do quite well on certain types of problems (NLP and vision, which are important problems). But a lot of data is structured (sales, churn, recommendations, etc), and it is so much easier to t
44.
by throw_away_777 10y ago
I've always found it curious that Neural Networks get so much hype when xgboost (gradient boosted decision trees) is by far the most popular and accurate algorithm for most Kaggle competitions. While neural networks are better for imag
45.
by throw_away_777 10y ago
The models of the universe with and without dark are fundamentally different, it is not inventing "invisible stuff to explain that the model is still right". The nature and composition of fundamental particles are an integral part
46.
by throw_away_777 10y ago
The problem with this approach is that people pay much more money for limited gains they can exploit than for general gains that benefit everyone equally. Advancing academic knowledge provides gains distributed over a very large number of p
47.
by throw_away_777 10y ago
This definition of engineer is so broad as to be meaningless. There is no "fake world" with "fake world" problems, pretty much everyone is trying to create real world solutions to real world problems. Similarly most jobs
48.
by throw_away_777 10y ago
There definitely does seem to be a bias against academia in industry. From my experience applying to jobs, most employers seem to value 1 year in industry more than 5 years in academia. The data science field is flooded with academic applic
49.
by throw_away_777 10y ago
It is interesting to note that the statistical evidence for global warming is relatively weak (at least compared to particle physics, where 3 to 5 sigma is expected for discoveries), https://wattsupwiththat.com/2016/04&
50.
by throw_away_777 10y ago
The easiest way to make big changes is to make a lot of small changes. Try to focus on small improvements you can make and work towards them. Don't get frustrated if progress is slower than you expect.
51.
by throw_away_777 10y ago
In my opinion, the solution proposed in the article of: " choosing a women over a man when growing your team, just because" is counter-productive. These kind of suggestions cause people to question why a minority got promoted, if
52.
by throw_away_777 10y ago
Well perhaps they should consider paying better wages or offering better working conditions if they truly have problems finding people.
53.
by throw_away_777 10y ago
Most physicists who recently graduated who I know (who don't do a postdoc) have transitioned or are looking to transition into data science. I am in the looking to transition group, so my sample may be biased though.
54.
by throw_away_777 10y ago
It is just a rough guess based on my experience as a grad student. The average professor at my university had 1-2 grad students at a time, and with 6 years to finish a PhD and about 35 years where a professor takes on grad students it works
55.
by throw_away_777 10y ago
On average, a tenured physics professor will graduate a bit more than 10 PhD students over the course of their career. Physics as a field is not growing, so at most 10% of PhD students become professors. If you don't become a tenured p
56.
by throw_away_777 10y ago
The hard part of coding professionally is getting a job. Practice is much more important than native ability for most intellectual activities until you get to the very peak of competitive performance.
57.
by throw_away_777 10y ago
To get a job at a company you have to be given the opportunity to interview, and to succeed at the interview. If you only evaluate performance at the job level, you don't evaluate performance of anybody who failed the interview. This i
58.
by throw_away_777 10y ago
What do you mean by large? Probably as your data is in csv format it is small enough that python or R are both good choices. Personally I'd recommend python as it has a large community using it so googling questions about python librar
59.
by throw_away_777 10y ago
From someone who is looking to transition to data science, the field is terrible if you haven't had an industry job before. I am ranked in the top 150 on Kaggle and can't even get phone interviews without someone in my network rec
60.
by throw_away_777 10y ago
When you say "clean data", what exactly do you mean? I've often seen this claim that cleaning data takes a lot of time, but it seems like an ill-defined term.

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