There is a doubt in every upcoming data scientist’s mind, whether to choose R or Python. In most cases, Googling is the popular choice to search for the best solution. But most people fail to find the result, and still not start anything to become the Machine Learning or Data science expert. So, I am going to explain the best of R and Python to make the forthcoming data scientist dream come true.
Machine Learning and Data Science are the prosperous fields that are growing constantly.This both are putting a light on various complex problems. Therefore, looming various applications and solutions on the intricate real-time issues.
So, the opportunities for the Data Scientist and Analysts are booming at the moment around the globe.Moreover, the advent of technologies like Artificial intelligence, IOT, and Big Data has completely influenced the need for data experts.
Hereafter, the aspiring Data expert needs the most powerful tools to battle the most complex data sets. The various Machine Learning tools and libraries are developed for the people to automate the tasks of data analysis, recognition, and aggregation.
For this R and Python programming languages are the front-runners to develop the libraries. So, most get confused between this two and waste clocks of time choosing one. Oh No! then when you gonna start, Let’s get ready to know the winner of R or Python.
Contents
Before knowing the differences between both the languages – will look into the similarities they carry.
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Both R and Python are Open source Programming languages.And had large communities to contribute to the documentation and development
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Both can be used for Data analysis, analytics and Machine learning projects
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Two of them holds advanced tools for the implementation of data science projects
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The salary for the data scientists who prefer R and Python gets almost similar.
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The current version of Python and R is 3.x.x
Battlefield of R and Python
History:
Python is developed by Guido Van Rossum in the year 1991, which is inspired from C, Modula-3 and ABC.
R is created by Ross Ihaka and Robert Gentleman in the year 1995, which is implementation of S programming language.
Purpose:
The vital purpose of Python implementation is creating the software products and making the code simple and readable for the programmers.
Whereas, R is mainly implemented for user-friendly data analysis and to solve complex statistical problems.It is mainly statistical-centric language.
Ease of learning:
Python is simple to learn due to its code readability.It is a beginner friendly language, where without any prior experience in programming can start using Python.
R is difficult to learn at the starting stage of its implementation.By using more often can reduce its difficulty and produce its effectiveness in solving complex formulas in statistics.For the experienced programmers, R is the GO TO option.
Communities:
Python has good support from various communities in the development of the language for the future applications.Programmers and developers are the active members of the communities like StackOverflow, mailing-lists etc., which are part of developing python.
R also gains huge support from various communities like mailing-lists, user-contribution document and so forth.Most of the statisticians, researchers, and data scientists are actively involving in the development of the language.
Flexibility:
Due to the productivity-centric language, Python gains a lot of flexibility in the implementation of various applications.It also encompasses various modules and libraries for development of large-scale applications.
R also focussed on flexibility in implementing complex formulas, tests in statistics, visual implementation of data and so on.It also encompasses various package readily available for use.
Applications:
Python is the captain of developing the various application in the software firm.It is used to support web development, gaming, data science, and stack increases.
Coming to R, it is mainly focussed on implementing the data science projects, which is focussed on statistics and visualization.
Finally, in the battle of R or Python, both have its positives and negatives.In the most cases, they are specific-centric languages because of R focusing on Statistics and visualization, whereas python has a simplicity to develop any application.
Therefore, R can be mainly used for Research and Academics, statistical analysis and data visualization.And on the other hand, Python is used for easy debugging, delve into data analysis, and so forth.R will be very efficient for statisticians in the field of data science and python is better for the programmers and developers that aiming for the data scientist.
