Python vs R vs SAS

Python vs R vs SAS

Last updated on 13th Oct 2020, Artciles, Blog

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In this topic, we are going to compare all the three languages on various aspects to give you a clear perspective about the market value and capabilities of these languages, so that you can choose the language with which you can move forward.

It is a well-known fact that to learn data analysis, you can use three important languages that are Python, R, and SAS.

If you are a fresher in the data science community and do not have experience in any of the languages mentioned above, then it is vital to be acquainted with at least one language.

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What is Python?

It is an interactive and interpreted high-level object-oriented programming language. It is known for simplicity and clear syntax which in turn increases readability. It is easy to learn and understand. It is largely used as an open-source scripting language that supports many libraries used for model building or statistical operation on data. It is used by many biggies like Google, Quora, Reddit, etc.

What is SAS?

SAS has been proved as one of the unchallenged leaders in the field of data science. It is known for its huge variety of statistical functions, good GUI and great technical support experience. It is also easy to learn. SAS is used by various IT companies like Nestle, Barclays, Volvo, and HSBC. But, it is not open-source and ends up being an expensive option for a beginner.

What is R?

R is a counterpart of SAS and is free as it is an open-source platform. It is mainly used in the academics and research section. As it is open-source, it is highly extensible and there are quick releases of the software with the latest techniques. You can find multiple information sources for R over the web.

Comparison Factors: Python vs R vs SAS

Let us now compare some factors of Python, SAS, and R to choose the best which suits your requirement.

1. Cost-Effectiveness:

  • As we have already discussed, Python and R both are open source languages and are free to download. Although we can get a lot of documentation for these languages, it does not have any tech-support and warranty.
  • Small and medium-sized companies prefer these 2 languages over SAS due to its transparency nature in all functionalities without purchasing any license.
  • On the other hand, SAS has licensed software and a very expensive one. Mostly big IT companies can afford to buy and work over it. There are various features that can be utilized only after purchasing a few upgrades. 

2. Learning Ease:

  • Python is very easy to learn and understand due to its simplicity and versatility. It can be used by beginners who are new to programming as well as to data science. 
  • As R is a low-level programming language, it takes time to understand and learn to code in R. If not correctly implemented, even minor tasks will become Herculean and involve complex code lines. Its overall learning can be considered as average to high.
  • SAS is one of the easiest languages across the world. Anyone having no prior programming knowledge can learn SAS. Those who are familiar with SQL can easily understand SAS. Moreover, it has a very good GUI and comprehensive documentation which makes it easy to learn.

3. Graphical Capabilities:

  • In the case of graphical capabilities, Python gives a tough competition to R with the help of graphical packages such as VisPy, Matplotlib. But it is still complex when compared to R. 
  • R has the best graphical capabilities because of the packages like Lattice, ggplot, RGIS, etc. The graphical presentation is very much important when we are talking about data science. R produces a dynamic and interactive graphic interface.
  • SAS provides functional graphical functionalities. But it is purely functional. To do any customization over it is a difficult task to achieve. To customize, we need to understand the SAS Graph package thoroughly.

4. Data Management Capabilities:

R computes everything in RAM. This is a big disadvantage of R as it is dependent on a machine’s RAM size. Any task performance can vary and perform as per the machine’s RAM. Although it has been removed. For data management and handling factor, we can conclude that all three of Python, SAS, and R fare equally well and all provide a parallel way of computations.

5. Community & Customer Support:

  • As Python and R being open-source languages, there is no technical support provided for any issues. Although, there are various big online communities from there you can get great help for any issues. 
  • SAS provides an awesome technical support experience that is not available for Python and R. It also has a great community.

6. Job Opportunities:

  • As Python and R are open source and free, those are mostly used by startups or organizations looking for cost-effectiveness. According to a survey, there is a tremendous spike for Python/R job openings. They are giving tough competition to the SAS market.
  • As most big organizations use SAS, there are a large number of jobs opening all over the market for SAS. It is still a market leader globally.

7. Application Advancements:

  • Due to the open nature of R and Python, the development of new features and techniques are fast as compared to SAS. Although there are chances of issues in development as they are not well-tested due to its open contribution.
  • SAS introduces a new version in the form of software releases or rollouts. As it is a licensed one, all the features and updates are well tested. It is less prone to errors as compared to Python and R.
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8. Deep Learning:

  • Python has progressed dramatically in the field of deep learning by introducing TensorFlow and Keras.
  • R has introduced KerasR and Keras packages. These are behaving as an interface for Python Keras packages.
  • SAS has recently introduced deep learning and it is still in the development phase. There is a long road to travel for SAS for deep learning.

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