Python vs R vs SAS
Comparisons of Python, R, and SAS
Programming Languages for Aspiring Data Analysts
With the predominance of a digital world, there is an unending stream of data being recorded and stored. The highlight of this being that data can be found on almost any subject and can be extrapolated to give rise to specialized conclusions. The drawback is that the majority of this data is no longer available in a structured form and the unstructured data needs to be mined, stored and interpreted so that it makes sense. It is here that specialized programming languages like R, Python, and SAS may be implemented.
Huge multi-national Corporates operate on the principle of data analysis, making profits in billions, simply based on tailoring their services to customer choice. Becoming a data scientist is a lucrative profession and for becoming a data scientist, you must definitely learn at least one of the programming languages. Learning more than one language would definitely give you an edge in the industry.
However, if you ask which language is the best, there can be no single answer. Each language has its own features and elements that the user finds unique to suit his needs. For your ready reference, we have given an exhaustive comparison between the three main programming languages that dominate the data science scenario.
Difference Between Python, R and SAS
Factors for Comparison |
Python |
R |
SAS |
| Overview | A widely used object-oriented language, emphasizing on productivity and code readability. | A flexible and powerful scripting language and the open source counterpart to SAS. | A prominent data analytical tool in the market with wide-ranging capabilities and extensive preference by huge corporates. |
| Cost | Python and R are free programming languages that can be downloaded and used by individuals as well as organizations. | Although SAS has introduced a free University edition, it is a proprietary software and companies need to pay a huge amount to make use of SAS in their system. | |
| Updates | Both are open source and hence updates are quickly available | Updates are available only through periodic new version roll-outs | |
| Learning Ease | Python has the advantage of being simple to learn and can be used by beginners as well as experienced data scientists. | With the steepest learning curve among the three, R is a difficult language to learn as it needs a working knowledge of coding. | SAS is easy to learn, being similar to SQL. Programmers who have worked with SQL, find it quite easy. |
| Speed | It is a high level programming language and the ideal choice for critical yet fast applications. | Since it is a low-level programming language, longer codes are needed for simple procedures resulting in reduced speed. | Due to the drag and drop feature, components can be picked up and used directly, without worrying about the coding part. |
| Testing for Updates | Since these are free and updates are available immediately, extensive testing is not done and there have been errors in these updates. | Because of scheduled version updates after extensive testing, there are hardly any errors with new updates. | |
| Data Handling Capability | All three languages score similarly as far as data handling capability is concerned. They have good data handling capacity and can handle parallel computations. | ||
| Graphical Capabilities | Python has its own packages like Vispy and Matplotlib providing excellent graphical capabilities. | With ggplot, Lattice, RGIS and other such packages, R provides the best visualization diversity among the three. | Though SAS has been working recently to improve its graphical capabilities, it comes nowhere near the other two. |
| Deep Learning | With Tensorflow and Keras, great advancement in Deep Learning have been made. | kerasR and keras are the packages that act as interface to the python package Keras. | Deep Learning is in its infancy as far as SAS is concerned. |
| Customer Support | Being free, there is no customer support but, a very huge online community support is available to answer problems | A large online community for support but not as large as that for python. | Excellent customer support at corporate level to troubleshoot any issue. |
| Popularity | Python has recently seen unprecedented growth and is set to outpace the other 2 languages in the market. | Hugely popular with statisticians and individual programmers due to its huge repository and flexibility. | The most popular in the corporate world and used by most of the huge corporations for at least some aspect of their functioning. |
Taking into consideration the above factors, aspiring data analysts may appreciate the intricacies of each of the three programming languages compared above and their specific advantages in different scenarios. Learning one or more of these is sure to highlight the profile of an individual, helping him boost his career in international markets.
At Besant Technologies, we provide the best possible way to learn programming, keeping in mind the current global scenario. Our team of professionals provide hands-on training and step-by-step learning facility through which a candidate may become proficient in any of the programming language of his choice. Give your profession a boost, with Besant Technologies.
