SAS in Data Science

Statistical Analysis System, or SAS, is a software suite that has been evolved by SAS Institute for a plethora of disciplines surrounding information technology and data analytics. Over the years, SAS has progressed many times over. It now comes with better sort along with hash tables, and awesomely fast SAS for genuinely big data. In case your customer or corporation demands you to use SAS, be at ease and begin dreaming of doing magic with facts, due to the simple reason that SAS is a simply amazing software.
SAS is one most of the four most important languages popularly used by data scientists, the other three being R, Python and SQL. As per KD Nuggets, the leading source of data science information and news, nearly two-thirds of SAS users don’t use any other language. It’s pretty apparent then that SAS is more popular amongst data scientists and people in associated fields than every other language.
Allow us to share how SAS fares with regard to other languages used for data science.

  • With R: R is the open source counterpart of SAS, which has traditionally been used in academics and research. Moreover, R has a whole lot of aspects and factors to it, which makes it noticeably quite tough to get an expertise in. As far as updates are involved, SAS updates get launched inside a managed environment; more importantly, they’re well-examined. However when you consider that R is an open source and a no-charges language, there would possibly regularly be chances of errors in new developments or updates. SAS has a huge, supportive community to assist you, which one doesn’t find while coping with R.
  • With Python: SAS in all likelihood will always have a position in legacy systems and entrenched analytics platforms, however newly evolved analytics platforms and toolkits could be likely choosing up steam with Python. As of the present, it helps libraries (which might be named scipy, numpy, and matplotlib) and features for almost any statistical operation / model building you could want to do. Since the time pandas were introduced, Python has emerged as very sturdy in operations on structured data.
  • With SQL: Even though SQL can’t be referred to as a programming language completely, it is used to connect to extensively spread databases. It enjoys popularity in effective data science, regardless.

SAS is undoubtedly a leader in commercial analytics space. It has a big selection of statistical functions, best GUI for people to learn quickly and, of course, gives notable technical support. However, it is a highly priced alternative, and might not be constantly up-to-date to have the latest statistical capabilities.
No one language is better than others; it all depends on what your aim is. If you are a manager in an enterprise, you need to do an analysis, and then choose the tools that are best for fulfilling your goals.
As far as learning is concerned, if you become comfortable with a software, coding on other software becomes relatively easy. So, make a decision based on your interest, convenience and the intended usage while you are looking for the perfect language to learn.

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