Windows Or Mac For Data Science
When you plug a drive that's been formatted for Mac into a Windows PC, you may find that it isn't readable. This is because Mac uses the HFS+ format natively, whereas Windows uses ExFAT or NTFS. Get Data Off of an Old Android Phone. Donate Your Computer's Downtime to Science. This work is released under a Creative Commons License. Create mac partition on windows. Jun 30, 2016 - I may experiment with in-memory data management and predictive. Linux - it depends on industry company you work for.
I see many Data Science (DS) tutorials done on MACS, and many DS blogs recommend MACS as the best developing platform, thus the quote 'Data Science is statistics on a Mac' came more than once into my mind. I'm quite fascinated by MACS (be it iMAC or MacbookPro) but I never could get a valid reason to why Data Scientists in particular use them (if that is true, of course) All who I asked said: 'because it has Unix', but I hardly manage to accept it as a valid reason (at least for me). The hardware is very weak for Machine Learning processes, and the OS is inefficient and cumbersome (no file path, no drag and drop photos and music files, windows don't fully open by default nor close, etc) The only benefit I found so far in MACoS (or Linux for this matter), is that when running parallel processes, the RAM is being shared, unlike on windows, but quoting one of my lecturers: 'you need to be mad to switch OS for that reason only' Would be grateful if you could enlighten me on the subject.
This question is highly subjective and therefore person-specific. Like asking what colour keyboard is best for programming faster. A large amount of Computer Science research at universities is done using Unix based operating systems (a.k.a. Linux) because it is completely open source and allows the user to tweak things as they please. Windows never allowed this officially. Apple's OSX doesn't either.
However, OSX is based on Unix like Linux, and so there are many more similarities in every day usage between Linux and OSX than Linux and Windows. Many of the poster-boys/girls from data science have an academic background, where they likely used Linux. Because many of the latest research ideas come from universities (where I claim most researchers use Linux), it is common to see open-source projects that have only been tested and packaged for Linux usage. Making them run on OSX is a smaller jump than going for Windows, as OSX and Linux are more alike.
Other guesses as to why many pick Apple products, are that they generally have higher build quality, they have a reputation for many thing just working (though IMO, Microsoft has become just as good in that respect) and they are marketed as being luxury consumer products - and so are visually appealing. I'm sure there are books written on how social psychology with respect to this. All this ends up with the price being somewhat inflated for the actual hardware parts that you get. In the end, someone who wants to get work done and has the necessary skills will manage it on a 10 year old netbook running Windows. Likewise, owning a Linux server with 8 GPUs and a Macbook Pro to remotely connect to that server is no guarantee of good work. I for one have worked on all three platforms, for data science as well as other tasks, and although I have my preferences, the operating system rarely poses the biggest problem.
Share • LinkedIn • Facebook • Twitter 12 Data Analysis, Machine Learning model training and the like require some serious processing power. If you're someone who's just entered the world of data or if you're a veteran data scientist that needs an upgrade on his/her local machine this post will provide you with the comprehensive guide that is necessary to make the right choice when it comes to buying a machine that is capable of handling your data-sets. When it coms to choosing the right machine you usually have to choose between two factors: • Portability • Processing Power The higher the processing power the heavier the laptop gets and hence it's portability is reduced and vice versa. The next thing to note is that with higher power the battery life also shrinks and as a result you are losing out on portability yet again. Huge datasets these days have outgrown the processing power of a single machine and will depend on you accessing the cloud for processing, in which case portability is going to be of value to you. With that said, let's identify the minimum requirements that you would require when it comes to a laptop worthy of being called a data scientist's weapon of choice. RAM The minimum ram that you would require on your machine would be 8 GB.