As the dominance of the digital world has increased, there is an insignificant increase in data being stored. You choose any subject and you can find data on it. The disadvantage is that all these data are not structured. So, to make it useful, these data need to be mined and interpreted. There are several programming languages like Python, SAS, and R to filter the data and make them useful. Many giant IT companies rely and operate on data analysis.
As the demand for data analysis is growing quickly, the market demand for data scientists has also grown enormously. One should know at least one of the programming languages used for data analysis to give their career an edge in the IT industry.
Python vs. SAS vs. R, all three do an excellent job on the platforms they have set out. To choose amongst them and decide which one is better is a very difficult task. Each has its own set of features that are unique in their own way to curb various requirements.
However, for professionals who want to build their careers in data science, we have provided exclusive comparisons between these 3-programming languages.
Related Article: SAS Tutorial
Before starting comparisons, let us first understand all 3 technologies.
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?
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R is a counterpart of SAS and is free as it is an open-source platform. It is mainly used in 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.
Related Article: Business Analytics With R Tutorial
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.
- As we have already discussed, Python and R both are open source languages and are free to download. Although we can get many documentations 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 a Herculean and involves 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 as 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.
Related Article: Compare R language with SAS
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 of all 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.
8. Deep Learning:
- Python has progressed drastically 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.
Read More: Python Tutorial for Beginners
R has a slightly steeper learning curve compared to SAS and Python. Since R is a low-level programming language, it requires proficiency and basic programming orientation. If not correctly implemented, even minor tasks will become a Herculean and involves complex code lines. Its overall learning can be considered as average to high.
Currently, there is a slight bend for Python in the job market. But due to the dynamic nature of the IT industry, we cannot determine which programming language is better. It totally depends on the requirement specifications and factors like the learning phase and cost.
Below are some cases where we can guide you for choosing the language. Go through it and select whichever is more suitable to your specifications:
For a freshie aspiring to make a career in data analytics, SAS is the best priority as it is simple and still tops in the market
For experienced professionals, we recommend learning any 2 of these will open a path for better job opportunities.
For the one who is willing to work in start-ups or as a freelancer, Python is a must.
If we need to divide these languages over a specific category, then these would be defined as follows:
All big IT organizations choose SAS as their data analytics tools
As R is very good with heavy calculations, it is largely used by statisticians and researchers.
Startups prefer Python over the other two due to its lightweight nature, large community and deep learning capabilities.
Now, we hope you got a clear idea about all the three languages and believe this knowledge will be helping you to choose the best programming language for your career advancements.
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