The battle of choosing the “Best data science tool” has been going on for ages. Depending upon the pros and cons of the tools, a few tools are selected over many others. Choosing one between Python and R is quite challenging since they both possess qualities that neutralize their data science input.

Students, as well as professionals, keep asking for the best programming language for their day to day tasks. However, the choice of a programming language depends upon the challenge they are facing. The R language is designed by keeping the statisticians in mind. On the other hand, Python is known for it’s easy to understand syntax. Let us find out how Python and R programming language performs respectively in the data science battle.

Python in brief

Code readability and productivity are the main focus of this programming language – Python. People who are into data analysis or applying statistical techniques are Python’s essential users, especially for statistical purposes. If a person wishes to get into engineering, it is more likely for that person to prefer Python. The learning curve of Python is relatively low. However, it is a flexible, easy to read, and simple language; hence, someone who wishes to do something innovative – python is an excellent language for them.

Python has a package index called PyPi, which contains libraries. The users can contribute to the same. Python has a vast and great community but is a little scattered since it can be used. We can call Python as a General-Purpose Language since it is used in various industries. However, data science for Python is getting a dominant position in the Python universe. With this, there is a need for individuals who possess Python skills. It is mainly for data science, and hence the expectations are keeping on growing. Thus, we will see many data science practices originating from Python.

R in brief

R was created as an open-source language in 1995. It was introduced as an implementation of the S language. The R programming language focuses on a user-friendly and better delivery of graphical models and statistics for Data Analysis. It was initially used for research and academics only. But as the enterprise world discovered it, R programming language became the fastest growing in the corporate world, especially for statistical purposes.

R’s community is as huge as Python’s, but its strength is that it provides support through the mailing list, has an active Stack Overflow group, and user-contributed documentations. The users can also contribute through CRAN, a repository of curated R packages. These packages consist of R and data functions, allowing immediate access to the latest functionalities and techniques without any need to develop anything from scratch. The beginners might struggle a little in coping with R. However, if you are a professional programmer, you won’t have difficulty learning an R programming language.

General number comparison of Python and R

There is a lot of information on the web in blogs and articles that will compare the two – Python and R and give numbers regarding the popularity and adoption. These sources are great to know how greatly Python and R are evolving themself in computer science’s overall ecosystem. It is tough to compete between Python and R and comparing them side by side. It’s said that Python is used for general purpose; hence, Python’s community is scattered and is used widely in several fields. On the other hand, R is used only in the environment related to data science. It indicates that Python is favored compared to the R.

However, an individual’s salary with python skills is around $94,139, and R is $115,531.

Data science related number comparison of Python and R

For the polls which focus on data science using programming languages, R leads. A similar pattern appears when explicitly focused on the Python and R community. Some people are switching from Python or R. There is a rapid growth in the number of programmers using both Python and R for data science.

Both Python and R skills have a massive demand for job trends. Are you a data science enthusiast and wish to start a career, you are good to go with your choice’s preferred language? You can learn under the guidance of trusted ATOs by joining Data Science with the Python course.

Also Read: Data Science Course or Data Analyst Course

Pros and Cons of Python


  1. Python is a general-purpose language. It would help if you had less time to learn, code, and have more time to discover it more and play around it.
  2. Python Notebooks make the work easier related to data and Python. Sharing a notebook with your peers is also possible without needing them to install anything. You can spend more time doing the work and reduce the stress of organizing the code, note files, and outputs.


  1. When it comes to data science, visualization is a crucial part. Even if there are libraries that support visualization nicely, there are not too many options to choose from.
  2. Python does not offer an alternative to many of the vital R packages.

Also Read:  How Important Python Programming Is for a Data Science Career?

Pros and Cons of R


  1. The data can be understood more effectively and efficiently when visualized. The visualizations in R are less convoluted, and the results are always pleasing to the eyes.
  2. R is developed with keeping statisticians in mind. Statisticians communicate with each other regarding new concepts of code and packages using the R language.


  1. R programming language is slow. It was developed to make the life of statisticians easy, not computers. The slow working could result from poorly written code; however, a few packages can improve its speed.
  2. The learning curve of R is steep and non-trivial! If you are a beginner, even finding a package will consume a lot of your precious time.

Also Read: Why Learn Data Science with R Programming?


To conclude which programming language is the best for you, you should identify the level of challenges you will deal with. If you wish to use Python skills, the data science with python course will get you all covered!

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