R vs SAS vs Python
We know that R vs SAS has been a long-standing debate in the field of data science. With Python recently gaining popularity, it also joined the debate. Since Data Science is a very dynamic topic with advancements happening every year, any comparison that has been done 2 years ago might not be relevant anymore. So here’s shining new light on this important topic.
Before we conduct this analysis, it needs to be noted that there is no universal winner. Of course, these usages will depend on the kind of product you are building, and for what purpose. Sometimes Excel might just do the work with its pivot tables. For comparison, we choose 10 criteria and gauge each of them in the field.
Availability/Cost/Upgrade
1) Both R and Python are open source and are free. SAS is costly, but popular and holds the highest market share in the commercial marketplace.
2) Upgrades on R and Python are easily available with their open support communities. SAS has relatively slower upgrades and also costs for using them.
Ease of learning
SAS is the easiest to learn with simple tutorials and comprehensive documentation. R has a steeper learning curve and needs some proficiency and programming orientation. Python having a simple syntax is relatively easy, but still is a programming language.
Data handling capabilities
SAS is industry-grade compliant; data handling is safe, smooth and fast, but isn’t viable in the long run for customizations. R has the richest ecosystem with about 12,000 packages for specialized data analysis but works on RAM making it a disadvantage for using it. Recently, python also has been picking up Numpy, Panda libraries and is expected to give a strong competition
Graphical Capabilities
Data science relies on graphical capabilities for better communication of the insights. R wins hands down with its elaborate packages when compared to SAS. Python even though has been progressing swiftly, it currently ranks behind R
Complex Statistical capabilities
All of them are capable of standard modeling and statistical analysis. But with Python and R giving the flexibility to program for customized results, it eclipses SAS in this field. SAS has fixed functionalities that can be varied to only an extent. Furthermore, Python is easy to program than R.
Big data capabilities
Currently, big data capabilities are a common prerequisite when it comes to Data analyst roles. This requires an end-to-end application rather than ad-hoc or standalone analysis. SAS has recently been upgrading but Python and R, owing to their open-source flexibility, have faster upgrades. This is also likely to be a good reason for R and Python to completely change the data analytics industry.
Customer support
R and Python have the biggest online community, but no customer service support, so it may not be very easy to find support for technical issues. SAS has a dedicated customer service support to help with the debugging.
Job scenario
Earlier, SAS was a quick passport to enter into data analytics. But with the changing domain and its applications, there is increasing support for R and Python. Currently, for analytics aspirants, adding one of the open-source technologies has become a dire requirement. However, in big organizations, SAS still dominates but the trend seems to be on the downside.
For beginners, even though SAS is easier they will have better opportunities by learning Python. It also has cross-functional capabilities like web development and deep learning. Based on the functionalities, Python is best used for ML integration and deployment while R is the best tool for pure statistical and business analytics.
