Python vs. SAS vs. R, all of three do an excellent job on the platforms they have set out. This debate is similar to the famous old discussion on Mac vs. Windows vs. Linux, and in the present day scenario, we know that there is a place for all three.
However, for professionals who are in search of best Data Science platform to build their career and organizations which makes advanced analytics capabilities, this is an excellent question staring at them.
Python: It is a general-purpose interactive, interpreted, high-level and object-oriented programming language. It supported structured and functional methods and Object-Oriented Programming Languages and probably used for scripting like scripting language for building large applications. Python is capable of providing high-level dynamic data types and supports automatic garbage collection and dynamic type checking. It can for quickly integrating with COM, C, C++, ActiveX, Java, and CORBA.
SAS: It is the undisputed market leader in the space of financial analytics. This program offers significant statistical functions of distinct variety with great GUI for individuals to enable quick learning and supplies technical support that is amazing. Nevertheless, it is an expensive alternative and isn’t appropriately enriched with updated statistical functions.
R: It is a SAS’s open-source counterpart that has applied to professors and researchers. Since R is an open source framework that immediately releases updates. Plenty of documentation accessible over the web and is available at budget-friendly prices.
Now, let’s discuss some of the crucial aspects of data management capabilities, secure of learning, graphical skills, applications on big data, cost-effectiveness and advanced modeling. These parameters weight will vary based on what point of career you are in and your ambitions.
In case of standalone system’s data handling and management, SAS is better, smooth and safe. In the present day scenario, data size is increasing, and it is crucial to allocate memory for software and languages carefully. R comes with a significant disadvantage that it only works on RAM, which is a big problem since little exercises will also take time to run based on your machine’s RAM. Therefore, manipulating data is more natural when it comes to packages such as Plyr and DPlyr.
It is not a big deal that we face also face in case of Python. When extensions like Numpy and Pandy compared to others, fundamental analysis and data handling work are similar to Python's breeze. However, over the data manipulation parameter, all three of Python, SAS, and R fare equally well.
Python gets full marks even though it is a scripting language due to its flexibility and simplicity it provides to the users along with intuitive syntax. Python has become the new phenomenon in the data analytics toolkit and with its friendly-analytics libraries. Choosing Python is not a time taking the task, it does make you can allocate it some time to master. Its overall leaning is difficult to medium.
SAS is the most comfortable language across all three to learn and can be picked up by anyone without a prior programming language. The ability to parse SQL codes, integrated with macros and other native flavors makes SAS discovering a child’s play for individuals with primary SQL know how. Its overall learning difficulty is low-to-medium.
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R has a slightly steeper learning curve compared to SAS and Python. Since R is a low-level programming language, 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.
While considering data science, the graphical capability is a crucial aspect of better data understanding. For this case, R wins hands-down with packages like Lattice, ggplot, RGIS, etc. Even Python has excellent graphical capabilities such as VisPy, Matplotlib, but relatively in comparison to packages in R still labyrinthine.
Base SAS in recent times has worked on graphical capabilities improvisation; the available options are still not up to the mark when compared with R and Python. The graph packages of SAS are also not well documented. Hence, R takes the lead here. R is the leading one when compared with the other two.
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Most companies seek for end-to-end applications when it comes to Big Data instead standalone analysis or ad-hoc. It is the primary region where Python over SAS and R wins the thunder. It is a proven fact that Python is the only language that Hadoop-Spark supports apart from JAVA and Scala.
Similar to Python, R has excellent integration with Hadoop and offers superior parallelization capabilities, and in case of analytics, it is the best fit for large-scale machine learning.
In the present years, SAS is providing distinct options to execute analytics within Hadoop without moving cluster data but includes flexibility with open-source platforms. Python and R stay the first preference for professionals in Data Science. Therefore, SAS needs to run at a rapid pace with the evolving world to remain relevant.
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Among all three, Python has a slight edge over R as it is the best platform to develop applications and incorporate into a production environment.
It is one area where Python and R have the upper hand compared with SAS. It is a known fact that they are open-source and has been a critical factor in their phenomenal rise of usage.
Coming to the case of SAS, it is expensive and licensed software with excellent support especially in delicate scenarios and critical areas where there is no room for experimentation. However, it is not a perfect match for small companies and startups and is proven to be indispensable itself. Most of R and Python versions have no support systems and come with no warranty.
In case of benefits, most small and medium-sized companies choose R and Python. Especially R is best fit for organizations that have a primary focus on analytics whereas Python is the first choice of most tech companies as it provides reliable end-to-end integration and develops applications according to analytics leveraging with its friendly libraries.
Most Global companies choose SAS and are still the market leader with a vast number of job opportunities. Most big organizations even choose SAS. R/Python. Also, the number of Python/R jobs is continuously increasing over last few years.
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Python and R have the most extensive digital communities but lack of customer service support. On the other hand, SAS has dedicated customer support service along with a community.
It is a terrible task to make a conclusive argument on these three trending technology platforms as making selection among these technologies depends on several parameters like the strength of user community, industry nature, integration and usage flexibility, future outlook, etc.