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Choosing between SAS, R and Python
Updated on 03rd Mar, 23 5963 Views

Big Data Analytics has been adopted by all businesses that are seeking a higher growth trajectory, irrespective of the size and nature of their business. There are three main data analytics tools, namely SAS, R, and Python that are most commonly opted for by businesses. As in all cases where there are choices and competition, a major comparative study is made between these three platforms to find the best fit. Python has for long been left out of debates concerning which one is better, however, it is gaining prominence in the market too, and rightly so. We will discuss a few points that may help in opting for the most suitable technology.

Learn about the difference between Python and R in our comparison blog on R vs Python.

Ease of useAverageGood
Speed of codingAverageExcellent
Community supportExcellentGood

Check this Video on R, Python, and SAS Comparison

A Brief Background

  • SAS has been the undisputed leader in commercial analytics and offers a wide range of statistical tools, great GUI (Enterprise Guide and Miner), and incredible technical support. It is not open source and hence the most expensive platform in the market, though it has the latest statistical functions to justify the price.
  • R is the open-source alternative to SAS and mostly holds ground in academics and research. It capitalizes on its open-source nature by making available the latest techniques very fast. It is a very cost-effective option and there is extensive documentation available online for anyone seeking to master the platform. In R, there is a special advantage of advanced statistical features such as for computationally intensive tasks, C, C++, and Fortran code can be linked and called at run time. Some more advanced features can help edit R objects directly.
  • Python is another open-source scripting language that has grown to encompass libraries and functions for most statistical operations and model building. With the introduction of Pandas, it has further strengthened its operations on structured data.

Learn about the difference between SAS and R in our comparison blog on SAS vs R.

Finding the best fit

  • For the cost-conscious: SAS being commercial software, it is expensive. It has the highest preference and rules the space where private organizations are concerned. R and Python, on the other hand, are free and can be downloaded by anyone seeking their services.
  • From the learning perspective: If you know SQL, then you can easily learn SAS, and even if you don’t, the platform has a stable GUI interface in its repository. The platform also has extensive libraries and wide-ranging documentation that are available on the websites of various universities. However, SAS training certification can be expensive. R is the toughest to master as you have to start from learning and understanding coding, with longer codes for simple procedures. Finally, Python is famous for its simplicity and though there are not many GUI interfaces for the platform just now, it won’t be long before Python notebooks become more widespread as they have great features for documentation and sharing.
  • SAS has functional graphical capabilities that are basic and customization of plots is a tricky business. R has the best graphical capabilities with innumerable packages. Python has options to use native libraries or derived libraries, and is fairly good, though not a match for R, but better than SAS.
  • SAS is the market leader for corporate jobs since most big organizations work with the platform. R and Python are sought mostly by startups for whom cost efficiency is paramount. The two open-source platforms are witnessing a gradual upswing in the market though.

Certification in Bigdata Analytics

All three systems come with a list of their own pros and cons. SAS is clearly the leading technology to work with for big data analysis, though knowledge of R and Python will help as additional expertise.

The opinions expressed in this article are the author’s own and do not reflect the view of the organization.

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