SAS vs. R vs. Python

Here is a brief description about the 3.

SAS: SAS has been the undisputed market leader in commercial analytics space. The software offers huge array of statistical functions, has good GUI (Enterprise Guide & Miner) for people to learn quickly and provides awesome technical support. However, it ends up being the most expensive option and is not always enriched with latest statistical functions. Some great uses for SAS are

  • Data Entry, retrieval and management

  • Report writing and graphics design

  • Statistical and mathematical analysis

  • Business Forecasting and decision support

  • Operations Research and Project management

  • Applications development

SAS is used by reputed companies like Barclays, Nestle, HSBC, Volvo and BNB Paribas.

R: R is the Open source counterpart of SAS, which has traditionally been used in academics and research. Because of its open source nature, latest techniques get released quickly. There is a lot of documentation available over the internet and it is a very cost-effective option. R is a simple and effective programming language. It is more than just a statistics system. It does the following work

  • Easily manipulates packages

  • Manipulates strings

  • Works with regular and irregular time series

  • Visualize data

  • Machine learning

R is used by top rated companies like Bank of America, bing, Ford, Uber and Foursquare.

Python: With origination as an open source scripting language, Python usage has grown relatively quick over time. Today, it sports libraries and functions for almost any statistical operation / model building you may want to do. Since introduction of pandas, it has become very strong in operations on structured data.

Python is a object oriented programming language that has a clear syntax and readability. It is easy to learn and will help you work more quickly and effectively. It has become more popular in a short period of time because of its simplicity.

Python is used by famous companies like ABN-AMRO, Quora, Google and reddit.

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