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Basics of Python: Python is an interpreter language that means the code written in python gets,
interpreted at run time. It is a high-level language; hence it is highly readable. It is an open-source
language and works almost on any hardware and OS. There is a plethora of libraries available when
using Python for web development, software development, or when using for data analysis, data
visualization, or as a scripting language. Python supports structural as well as functional methods of
programming and it supports object-oriented programming.
(https://www.tutorialspoint.com/python/index.htm). It has numerous data types, and it does a dynamic
type check. It can be easily integrated with other software languages such as C, C++, dot net.
Basics of R: It is also an open-source programming language designed for graphical data analysis,
statistical computing, and scientific research. As it offers flexibility, a wide variety of inbuilt packages,
and different graphical libraries, it is preferred in data visualization. Like Python, it also does not require
a compiler for compiling the codes. And it is also platform-independent and can be used with any
hardware or operating system.
Qualities of Python for Data Visualization: As Python is a general-purpose programming language, it
does not have data visualization tools by default. But we can use 60+ libraries like Matplotlib, Seaborn,
Plotly. With such various libraries, Python is well equipped for data analysis and visualization. These
libraries provide high flexibility and versatility to any programmer to create desired graphics.
(https://www.inwt-statistics.com/read-blog/data-visualization-R-versus-python.html)
Qualities of R for Data Visualization: R has few packages by default for data/scientific visualization.
Packages such as graphics, ggplot, plotly can be used for speedy data exploration. It has various
functions using which we can create simple images like boxplot, scatterplots, etc. Similar to Python, R
has different libraries for the improvement of plots and making them more interactive.
SQL: SQL does not have any visualization functionality. External tools such as PowerBI, Tableau, etc. are
needed for visualizing. Many of these external tools have prebuilt templates that do not need any script
from the user and simple drag and drop can be utilized for creating a visualization, this comes at the cost
of limited flexibility and options such as color spectrums, visual design, legends, etc. For data
manipulation and summarization, we can use SQL but is not useful in model training.
SAS: from a variety of sources statistical analysis tools can mine, manage, alter, retrieve data. SAS is used
for the statistical analysis of data coming from different sources. It is a very user-friendly software that
can read data from databases, and spreadsheets. SAS is programmed to get the best results in data
analysis and transformation. It is also platform independent language and has a large set of customized
components for data manipulation. While working on analyzing financial data, SAS is the best choice any
business can have.
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Retrieved from: https://www.tutorialspoint.com/python/index.htm
https://www.inwt-statistics.com/read-blog/data-visualization-R-versus-python.html
Python vs R vs SAS | R, Python and SAS Comparison | Learn R, Python and SAS? | Intellipaat - YouTube

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Basics of Python: Python is an interpreter language that means the code written in python gets, interpreted at run time. It is a high-level language; hence it is highly readable. It is an open-source language and works almost on any hardware and OS. There is a plethora of libraries available when using Python for web development, software development, or when using for data analysis, data visualization, or as a scripting language. Python supports structural as well as functional methods of programming and it supports object-oriented programming. (https://www.tutorialspoint.com/python/index.htm). It has numerous data types, and it does a dynamic type check. It can be easily integrated with other software languages such as C, C++, dot net. Basics of R: It is also an open-source programming language designed for graphical data analysis, statistical computing, and scientific research. As it offers flexibility, a wide variety of inbuilt packages, and different graphical libraries, it is preferred in data visualization. Like Python, it also does not require a compiler for compiling the codes. And it is also platform-independent and can be used with any hardware or operating system. Qualities of Python for Data Visualization: As Python is a general-purpose programming language, it does not have data visualization tools by default. But we can use 60+ libraries like Matplotlib, Seaborn, Plotly. With such various libraries, Python is well equipped for data analysis and visualization. These libraries provide high flexibility and versatility to any programmer to create desired graphics. (https://www.inwt-statistics.com/read-blog/data-visualization-R-versus-python.html) Qualities of R for Data Visualization: R has few packages by default for data/scientific visualization. Packages such as graphics, ggplot, plotly can be used for speedy data exploration. It has various functions using which we can create simple images like boxplot, scatterplots, etc. Similar to Python, R has different libraries for the improvement of plots and making them more interactive. SQL: SQL does not have any visualization functionality. External tools such as PowerBI, Tableau, etc. are needed for visualizing. Many of these external tools have prebuilt templates that do not need any script from the user and simple drag and drop can be utilized for creating a visualization, this comes at the cost of limited flexibility and options such as color spectrums, visual design, legends, etc. For data manipulation and summarization, we can use SQL but is not useful in model training. SAS: from a variety of sources statistical analysis tools can mine, manage, alter, retrieve data. SAS is used for the statistical analysis of data coming from different sources. It is a very user-friendly software that can read data from databases, and spreadsheets. SAS is programmed to get the best results in data analysis and transformation. It is also platform independent language and has a large set of customized components for data manipulation. While working on analyzing financial data, SAS is the best choice any business can have. Retrieved from: https://www.tutorialspoint.com/python/index.htm https://www.inwt-statistics.com/read-blog/data-visualization-R-versus-python.html Python vs R vs SAS | R, Python and SAS Comparison | Learn R, Python and SAS? | Intellipaat - YouTube Name: Description: ...
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