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Like many data scientists, you are wondering what programming language to learn?

Whether you have experience with other coding tools or not, the unique features of both, including the vast array of libraries and collections may initially seem intimidating, but don’t worry, we’re here to help!

Not surprisingly, both R and Python boast their respective benefits for many applications and are widely used by experts in its global community. This article will help you decide which tools are right for you.

Getting started is a good idea to rethink what you want to use programming language based on your data science. For example, a data scientist working primarily in genetics research can find themselves in the use of R (which is widely used throughout genetics and is popular among bio-communicators), while someone working on models for image analysis, an employee at Tesla builds a self-driving car, technology and people working with Python because of its sophisticated image manipulation tools. But finally, it is still your choice, although it is generally not a good philosophy to blind yourself to what others are doing, take the time to find out why these experts prefer certain languages. It’s important to be able to “speak” the same language as your future colleagues.

Who uses R and what is its purpose?

R was initially developed as a platform for statistical computing, hosting all standard experiments, time-series study, clustering, and more. It includes a vast community of data miners, which means there are a lot of packages accessible from R developers and users. In terms of graphics, there are plenty of packages and layers for planning and analyzing maps like ggplot2. Importantly, the R new style artificial intelligence scenario is compatible with neural networks, tools for machine learning and Bayesian inference, and for deep learning packages such as MXNet and TensorFlow. You can read more about these in a quick list of useful R packages.

R seems to have a firm following, not only for data scientists but often for statisticians and related disciplines that require data manipulation (for example, in medicine, finance, and sociology). For data scientists, finding a widely used program is important; We can make our findings easily translatable and speak to as many sections as possible in one language.

Who uses Python, and what is its objective?

Python is a great tool for programmers and developers. Whether you are creating algorithms for simulating life atoms or providing anti-spam software, you will be at your place using its interface and host of functions. Released in 1989, it is considered one of the most significant general-purpose object programming languages. Python is always popular among new programmers (among them data scientists), which means a rich community of users and problem shooters.

Similarly, in the exciting topic of artificial intelligence, Python is also a very preferred option. Python holds tools for machine learning, TensorFlow, and neural networks. In addition, some of the more general objectives are included, and its users avail from libraries such as:

  • NumPy – Supports statistical analysis
  • SeaWorld – Supports to create layers
  • Pandas – Supports data generation

R vs. Python: Limitations

To the most interesting part: how do they fit into each? One of the main pieces of advice is to uncover the limits at the outset. Speaking from experience, leapfrogging from using Matlab, which is a great place for online support (usually some awesome person who has written a valid code for your needs), LabVIEW where there is no online presence, I know a great deal of panic and failure Getting frustrated because you don’t.

Some important things to consider in the application of data science:

  • Processing speed
  • The online community
  • Steep learning curve
  • User-friendly interface
  • Utilized widely

Let’s take a deep look on the above topics.

Processing speed:

R is considered slow. This requires storing its objects in physical memory, which is not a good option when trying to use big data. With that being said, fast processors reduce this limit, and there are various packages to deal with. However, Python is well suited for large databases and the ability to load large files fast.

Online Community:

As I mentioned, there is a wide support network that allows you to access both R and Python, which is a great source of errors that you can’t fix right away.

Steep learning curve:

This may or may not be considered a limitation of R, but due to its exponential power for its steep learning curve statistics. Developed by experts in this field, the R is an incredible tool, but you pay the price for it in your initial time investment. Python, on the other hand, is very attractive to new programmers for its ease of use and its relative accessibility.

Both programs require you to learn words that may initially seem intimidating and confusing (for example distinction between a “package” and a “library”), a Python-based user-friendly experience in R based on user-backed statistics by its mature predecessor, S. Based on IE, However, Python syntax is strictly consistent with users and refuses to run unless you encounter mistakes that can be easily missed (however these improve the user experience in the long run, as it makes us better, smarter code writers). The R has a beautiful attribute with its many educational users, giving it greater control over its graphics design, allowing for a variety of visual exports and setups.

Significantly, both are interpreter-build, and it has been found with other languages ​​(like C ++), which makes finding bugs much easier.

User-friendly interface:

Rstudio is widely regarded as the preferred platform for interfacing in R, and once you become familiar with it, you will understand why. It is classified as an Integrated Development Environment (IDE) and includes a console for direct code implementation with all the functions for plotting, interactive graphics, debugging, and workplace management, and see the Arstudio IDE features for detailed guidance.

Python offers a number of ITEs to choose from. The advantage is that it gives you a good chance to choose a familiar one based on your background. For instance, if from a computer science background, Spider is an explicit option. At the same time, beginners in this field can find Bigarm accessible and intuitive.

Widely used:

We have touched on this topic, and I emphasize that this is subjective to the field you have chosen. If you are leaning on education, finance, health, and R, then R is very widely spoken, and you will need to gain it. At the same time, those interested in software development, automation, or robotics may find themselves immersed in the Python community.

R vs. Python: Benefits


  • R is a great option if you require manipulating data. It has over 10,000 packages for data fighting on its CRAN.
  • You can create beautiful, output-quality maps very easily; R allows users to change the aesthetics of the graphic and customize them with minimal code, which is a big advantage over its competitors.
  • Its statistical modeling is the pioneer in this field, creating statistical tools for data scientists to be favored by skilled programmers.
  • Users avail from their interface to Github’s comprehensive platform to discover and experience the best software.


  • It is very easy and intuitive to learn for beginners (unlike R, developed by Python programmers, and the easy utility makes it a preference for onboard universities).
  • It wins over a broad array of users and creates a thriving community of sectors and an enhanced connection between open source languages.
  • The strict syntax will force you to make better code and write more condensed and clear code.
  • Python is fast at dealing with large datasets and can easily load files, making it ideal for large data handlers.

By taking all this into your account, selecting a language to start with depends on what you want. If you work in a data science-related field and who trains in statistical analysis, you will definitely notice that R serves most suitable for you. But, if you are one who sees them branching out into multiple fields, you can use Python’s generic and varied network. You can also agree that learning both (at least enough to read the other person’s syntax) will be beneficial for you as you learn both for their respective strengths. This will surely open more opportunities for you when it comes to settling jobs, and more significantly, provide you that transparency to choose which career track you want to perceive. But don’t be overwhelmed! Learning a second language is easier than the first! No doubt, you will be excited to unlock up a unique community to engage yourself as you evolve as a data science professional.