Also, could someone comment on the effect size of b = -.001, which is within the 95% CI. Because if that translates into a day in a hundred years in "longevity" terms or something like that, than I wouldn't call the result "significant", even though the 0 is not technically crossed.
Would you rather edit a messy dataset in pandas or in Excel? What about automatically generating reports? Going to give managers your ipython printout?
As for massive - something like daily options data for 3000 stocks, spanning a number of years, with information down to the tranche level (let's say 60 million rows if stored in a relational database fashion). In my case the analysis can be done on the stock level though, which means that only a 3000th of the dataset needs to be loaded into memory at any time.
Speaking of Dutch companies, I think I may be getting a job offer as a Data Scientist from booking.com. Anyone working there? Is it a decent company to work for? Ok pay and relocation package (would be moving from Norht America)? Thanks.
I have NOT worked there, so I'm not speaking from personal experience, and it may be different for data scientists than for programmers. From what I hear in the tech scene here, they seem to be the place that programmers go when they want to just code for 8 hours and go home in the evening and get paid well for it, rather than this startup environment where they're expected to be involved in product decisions etc. I hope that helps a little and I hope others can share more insight.
I work at Booking.com - have been here for two years and I consider it to be one of the best jobs I've ever had (admittedly, my previous jobs weren't great). Great team of people and we get a lot of flexibility to choose how we want to work. Personally, as a father I also really appreciate the work/life balance here - there is zero pressure to work silly hours.
Off-topic: I want to learn a functional programming language, and I was thinking to go with Scheme (because, you know, SICP...) Would there be any advantage for me to go with Haskell instead?
Scheme and Haskell are very different languages. Both are functional, but that's about where the similarities end. Scheme is impure, haskell is pure; scheme is strict, haskell lazy; scheme is dynamically typed, haskell static; all functions in scheme are variadic, all in Haskell are unary; etc. So it depends on what you're looking for, and what you're trying to use it to do. That said, here are some advantages of Haskell:
1) Speed. Haskell binaries are highly optimized and tend to be very fast.
2) Correctness. Haskell's type system is far more advanced than any other language with as much or more usage, and is very good at preventing runtime errors. (and can go a long way to preventing logic errors). Also, its purity prevents huge classes of bugs.
3) Interesting: Haskell introduces a lot of new concepts which can really open up your understanding of computer science. It's a lot of fun.
4) Forward-looking: many of the ideas introduced or popularized by Haskell, such as pattern-matching, type classes, no null pointers, etc, are manifesting themselves in the new languages these days (such as Rust and Swift). Haskell itself is also (slowly) making its way into industry. Learning Haskell, in some ways, exposes you to the "next generation" of languages and programming techniques. I'm not sure the same can be said of Scheme, which tends to be used pedagogically more than as a means to push the envelope.
Then again, I know Haskell a lot better than I know Scheme, so maybe I'm biased. But at least, it gives you something to think about.
By training I am not a computer scientist, but an applied mathematician, so it is a little difficult for me to make judgement calls on things like type system, lazy evaluation, unary/variadic (don't even know what that means). I am interested in AI (machine learning) and statistical inference. I was looking at Lisp/Scheme because I know that AI needs gave birth to Lisp. Plus you have newer things like Church[1] that extends Scheme to deal with probabilistic models.
Perhaps, but it wouldn't hurt to pollute your mind with SICP first. At least watch the Abelson/Sussman videos (an accelerated version for HP employees); the audio sucks on a couple of them (I mean, in a couple of the lecture videos the audio sucks with much greater force than it sucks in the rest of them, but it's still not quite as bad as Feynman's Robb lectures), but it's a small time sink for a lot of enlightenment.
Just randomly looked up one of the publications. It's not my area, so I can't talk about the meat of the paper itself, but what does the discussion section have to do with anything?
It's more of a motivation or justification of the work than anything -- they want to apply the paper's methods to control marine-related engineering systems which are becoming more important to the future of Taiwan.
It sounds like a non-sequitur because it was probably copied and pasted from the grant proposal to get funding for the work. The acknowledgments show that funding came from the "National Science Council of the Republic of China, Taiwan", so that organization would naturally want to know why they're funding work on fuzzy numbers and how it benefits the country.
Right, it just seemed like they didn't make any connection whatsoever between this particular research and a concrete naval/engineering problem. Kind of like saying "look this is math, engineering is important to Taiwan, engineers use math".
