For each attribute I give a score to each of these 3 languages (1 – Low 5 – High). The information below will still be useful. So, if you are looking for purchasing a tool for your company, you may not get complete answer here. I am comparing these from point of view of an analyst. I’ll compare these languages on following attributes: Since introduction of pandas, it has become very strong in operations on structured data. Today, it sports libraries (numpy, scipy and matplotlib) and functions for almost any statistical operation / model building you may want to do. Python: With origination as an open source scripting language, Python usage has grown over time.There is a lot of documentation available over the internet and it is a very cost-effective option. Because of its open source nature, latest techniques get released quickly. R: R is the Open source counterpart of SAS, which has traditionally been used in academics and research.However, it ends up being the most expensive option and is not always enriched with latest statistical functions. The software offers huge array of statistical functions, has good GUI (Enterprise Guide & Miner) for people to learn quickly and provides awesome technical support. SAS: SAS has been the undisputed market leader in commercial analytics space.Here is a brief description about the 3 ecosystems: So, without any further delay, let the combat begin! While I’ll discuss global trends about the languages, I’ll add specific information with regards to Indian analytics industry (which is at a different level of evolution).I think it is more than just a worthy consideration now. Traditionally Python has been left out of the comparison.Any comparison which was done 2 years ago might not be relevant any more. The data science industry is very dynamic.Probably yes! But I still feel the need for discussion for following reasons: Hasn’t a lot already been said on this topic? So, I thought I’ll discuss it with all my readers and visitors! This has also been one of the most commonly asked questions to me on this blog. I know that we all will benefit from the discussion. The reason for me to start this discussion is not to watch it explode (that would be fun as well though). Python is one of the fastest growing languages now and has come a long way since it’s inception. R has probably been the biggest debate the data science industry might have witnessed. If you love discussions, all you need to do is pop up a relevant question in middle of a passionate community and then watch it explode! The beauty of the process is that everyone in the room walks away as a more knowledgeable person. Windows in mobile OS to comparing candidates for upcoming elections or selecting captain for the world cup team, comparisons and discussions enrich us in our life. Note: This article was originally published on Mar 27th, 2014 and updated on Sept 12th, 2017 Introductionįrom Samsung vs. You can also choose any of the three tools depending on which stage of your Data Science career you are in.Each of R, SAS and Python have their pros and cons and can be compared over criteria like cost, job scenario and support for the different machine learning algorithms.The long-running debate of R vs SAS has now been joined by Python.
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