The Difference Between R-Programming & Python in Data Science
Technology is rapidly transforming in every industry. Large organization heavily investing in building data solutions tools. They are building data solutions from scratch when they could taking an advantage of available tools & solutions. Many organizations are re-inventing the wheel and wasting resources.
Here, we would like to explain few tools which are really effective for data analysis, data modeling, data prediction, classification etc.,
Python is the most popular which is a open source platform and there are a lot of flexible tools used for Data Analytics. Python’s compatibility and cuts the development time and its easy to use syntax makes it the most popular language. It’s easy to use for quantitative and statistical modeling.
Python has wide range of libraries which helps to interpret and manipulate the data. Some of the most popular libraries such a s Numpy, Pandas, SciKit learn, Mat plot lib, Sea born used for the analysis which makes very easy to retrieve the predictions.
As compared to other programming languages like R, Python has proved itself as highly scalable and faster language. It provides flexibility on solving different problems which cannot be solved using other programming languages. Matplotlib provides a strong foundation around which other libraries like ggplot, pandas plotting, pytorch, and others are built. These packages help to create charts, web-ready plots, graphical layouts, etc.
R- PROGRAMMING :
Like Python, R is also open source platform which can be easily accessible to perform analysis. However, R is built by the statisticians and encompass their statistical knowledge. R has now one of the richest ecosystems to perform data analysis. it is estimated that there are more than Ten Thousand packages available in CRAN (open-source repository). The difference between the R and other programming languages is the output. R has fantastic tools to communicate the results. RStudio comes with the library Knitr.
For the data analysis you can have plenty of library options to perform with R-Programming, The rich variety of library makes R the first choice for statistical analysis, especially for specialized analytical work. We can make a document very easy as well the presentations also with the help of R and R studio.
R can be the best tool to perform data analysis and all other data science operations because of its powerful communication libraries. Besides, R is equipped with many packages to perform time series analysis, panel data and data mining. On the top of that, there are not better tools compared to R
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