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After reviewing all the three languages, SAS is said to be the easiest language among all, the graphical
user interface it provides is much better. Python is said to be the high-level language with object-
oriented purpose, so that the everything in python can be object. Learning of python may be easier as
compared to that of R but one need not to have any prior knowledge about SAS or Python. Whole SQL
on the other hand is a database management language but it can also be used with the other languages.
(Li, et al. 2012)
Data visualization
Both Python and R are used for data visualization purposes, but when it comes to graphic customization,
it is bit easier in the R then the Python, R can also use matplotlib and several other libraries which make
it standardized solutions in the statistical data analysis. Python normally doesn’t offer data visualization
but it supports the libraries and for data visualization. Therefore, these things make it Python language
is easier for visualization. (Brittain, et al. 2018) There are several pros and cons of Python in data
visualization like.
Pros.
The user can quickly learn the Python and it can lead to faster solutions development.
Has many packages for visualization.
Large community support.
Cons
Lack of clarity and documentation.
It can slowdown the process due to the libraries and packages.
The visualization dashboard is not available.
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Pros of R
Many native functions of data visualization.
Fast processing due to inherent features and characteristics.
Fastest MVP framework.
Cons.
Handling large volumes of data is difficult.
Usually, it is slower.
Has many packages spread.
References:
Li, Y., Zhang, Z., Liu, F., Vongsangnak, W., Jing, Q., & Shen, B. (2012). Performance comparison and
evaluation of software tools for microRNA deep-sequencing data analysis. Nucleic acids research,
40(10), 4298-4305.
Brittain, J., Cendon, M., Nizzi, J., & Pleis, J. (2018). Data scientist’s analysis toolbox: Comparison of
Python, R, and SAS Performance. SMU Data Science Review, 1(2), 7.

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After reviewing all the three languages, SAS is said to be the easiest language among all, the graphical user interface it provides is much better. Python is said to be the high-level language with objectoriented purpose, so that the everything in python can be object. Learning of python may be easier as compared to that of R but one need not to have any prior knowledge about SAS or Python. Whole SQL on the other hand is a database management language but it can also be used with the other languages. (Li, et al. 2012) Data visualization Both Python and R are used for data visualization purposes, but when it comes to graphic customization, it is bit easier in the R then the Python, R can also use matplotlib and several other libraries which make it standardized solutions in the statistical data analysis. Python normally doesn’t offer data visualization but it supports the libraries and for data visualization. Therefore, these things make it Python language is easier for visualization. (Brittain, et al. 2018) There are several pros and cons of Python in data visualization like. Pros. The user can quickly learn the Python and it can lead to faster solutions development. Has many packages for visualization. Large community support. Cons Lack of clarity and documentation. It can slowdown the process due to the libraries and packages. The visualization dashboard is not available. Pros of R Many native functions of data visualization. Fast processing due to inherent features and characteristics. Fastest MVP framework. Cons. Handling large volumes of data is difficult. Usually, it is slower. Has many packages spread. References: Li, Y., Zhang, Z., Liu, F., Vongsangnak, W., Jing, Q., & Shen, B. (2012). Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis. Nucleic acids research, 40(10), 4298-4305. Brittain, J., Cendon, M., Nizzi, J., & Pleis, J. (2018). Data scientist’s analysis toolbox: Comparison of Python, R, and SAS Performance. SMU Data Science Review, 1(2), 7. Name: Description: ...
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