Comparisons and discussions always enrich our lives and we are busy making comparisons in almost everything , be it smartphones, operating systems, technologies, electing candidates or anything else.
Let us bring in a similar spark in comparing the three main data analytic tools:
- SAS
- R
- Python
SAS and R have always been a strongly debated topic in the field of data analytics. Python surely is another worthy contender. This discussion is surely going to help you to enhance your knowledge and know that for your research which one is the best analytical software.
Firstly, let’s look at the background of the three tools individually:
SAS: For a long time, SAS has been the undisputed market leader in the field of commercial analytics. The kind of function it offers are many in number. It supports quick learning and gives a strong technical support because it has a good GUI(Enterprise and Guide Miner). One drawback of the tool is that it is expensive and is not upgraded with the latest statistical functions.
R: R has found most if its usage in the field of academics and research. It is an open source technique, because of which the latest techniques get released far more quickly. There are sufficient tutorials theatre available online for the same, and it is a very cost effective option.
Python: Over the years, the use of python has grown significantly. At present it offers functions for almost all statistical operations that one may want to perform. Today it depicts extreme strength in operations and structured data.
The comparison of these tools can be done on the following attributes:
- Availability
- Cost
- The ability to handle data
- Graphical representation options
- The advancement in tool options
Availability: SAS needs to be bought in comparison to R and Python which are free softwares and can be downloaded easily. Here of-course, SAS lags behind because of its inaccessibility.
Cost: SAS is expensive vis-à-vis the other two. However, it is still commercially the most used software. Individuals usually don’t invest in it for personal use.
The ability to handle data: A few years down the lane SAS had more capacity in data handling, proving more advantageous than the other two. However, it is not the same case anymore since all three have similar data handling capabilities.
Graphical representation options: SAS lacks features in graphical representation and R has the best among the three. Python’s graphical options lie somewhere in between.
The advancement in tool options: R and Python are able to upgrade far more easily as compared to SAS because all its upgrades are released in a controlled environment and are well-tested. On the contrary, there are more chances of error in the open contributions made in R and Python.
