R vs Python for Data Science: Which One Is the Best Programming Language for Data Scientists in 2024?

Python vs R has become a common topic of discussion among data scientists. Both languages have become extremely popular in the data science community. Python & R - both help data scientists drive meaningful insights from the sea of data. Data science languages are in huge demand due to their additional features and seamless functionalities. Today we will be answering the question - Which is the best for data scientists? R or Python?

Let’s get started!

Table of contents:

- R vs Python for Data Science: Which one should you choose?

- Python

• Advantages of Python

- R

• Advantages of R

- Factors determining the best programming language for Data Science

• Past programming Experience
• Statistical Learning or Machine Learning
• Academia or Industry
• Data Visualization
• Speed

- Conclusion

Data science is a booming industry with the integration of different programming languages and state-of-the-art technologies. Data scientists often interact with statistical programming languages. Data analysts mostly use Structured Query language (SQL) to communicate with databases. However, when data scientists need to clean, manipulate, analyze as well as visualize the data they tend to choose between Python or R.

Read on to learn more about the best programming language for data science. Before diving into the discussion, first, let's overview both the languages and understand their pros and cons.

R vs Python for Data Science: Which one should you choose?

Python and R are both free, open-source languages. They both, run smoothly on most common operating systems i.e. Linus, macOS and Windows. Both languages have a long list of functionalities and can easily take on any data analysis task. Beginner or expert, the languages are easy to learn and execute. So which language is the best for you? You will be able to answer that question by the end of this discussion. So, without further ado, let's get to know these languages better.

R vs Python for Data Science: Python

Python is a general-purpose, object-oriented programming language. Its easy syntax makes it perfect for collaborations. It ensures smooth execution of tasks with flexibility, stability and code readability.

Advantages of Python

• Easy to understand and perfect for beginners.
• As Python is a high-level language, it ensures a smooth approach to object-oriented programming.
• As it is an open-source language, it is flexible, ever-evolving and continuously improving.
• The syntax is easy to read and understand.
• Easy syntax facilitates better collaboration across the team.
• Python has an intuitive syntax that is similar to natural language.
• It provides a plethora of libraries and frameworks. Some of the most popular libraries are Pandas, Matpltlib, NumPy and SciPy.
• The provision of a vast library saves a lot of your time and replaces the solution strategy easily.
• The Python Community is vast with thousands of data scientists. Such a community can be extremely helpful when facing an issue.

What does high-level language mean?

A high-level language is any language that is similar to a human language. Its syntax is easy to read and understand by humans. On the contrary, a low-level language is one whose syntax is more machine friendly.

When you write a code in a high-level language, it gets converted into a low-level language. This enables the machine to recognize and run it with ease.

Examples of high-level languages: Java, Python, C++, C#

R vs Python for Data Science: R

R is a popular statistical programming language that is built to facilitate computing and data visualization. R has numerous abilities, including statistical analysis, visualization of data and manipulating data.

Advantages of R

• R specializes in statistical analysis and data reconfiguration.
• R has a bunch of tools that enable data analysis, data visualization and representation.
• It has quite a few packages that allow you to manipulate data.
• R is an open-source language that warrants continuous improvement.
• R was originally designed to ensure smooth statistical analysis. So, R helps in creating complex statistical models with ease.

Now you are well aware of the advantages of both languages. Be it R or Python both are commonly used and have a vast community to take their side. Still, confused about which language is the best for you? Here are a few points to consider before choosing a language for data science.

R vs Python for Data Science: Factors determining the best programming language for Data Science

As the list of advantages of both languages is long. Let's consider some factors which might help you in determining which language works best for you.

R vs Python for Data Science: Past programming experience

Python has a similar syntax to other programming languages. So, if you have some past coding experience - using Python will come naturally to you. R is a bit different when it comes to syntax and might take you more time to catch up with!

R vs Python for Data Science: Statistical learning or machine learning

R was originally designed to draw statistical data and make statistical analyses. So, R smoothens the path for statistical models and data visualization. If you are someone who comes from a statistical background, you might enjoy using R more. On the other hand, Python was originally a machine learning language. So, if you want to integrate data analysis tasks with applications - Python is the language for you.

R vs Python for Data Science: Academia or industry

Python is a full-fledged programming language that is commonly used in organizations. It is used in production systems, data analysis as well as coding. On the contrary, R is a statistical programming language and better suited for education, academia and research.

R vs Python for Data Science: Data visualization

Diving in the pool of data and making sense of it comes naturally to data scientists. But if the same data is not presented well, it might not peak the interests of others. R is making data beautiful by making the graphs talk. So, when it comes to data visualization - R is the clear winner.

R vs Python for Data Science: Speed

R is a low-level language, which means longer codes and more time for processing. Python being a high-level language renders data at a much higher speed. So, when it comes to speed - there is no beating Python. In the fight - R vs Python for data science - Python seems to be much faster with an easier syntax.

In the data scientists community battle of R vs Python for data science is going on for a long time. By the data analyzed above, we can say that:

• In terms of data visualization, statistical support and data analytics of large data sets - R scores better than Python.
• In terms of ease of implementation, speed and machine learning - Python scores better than R.

However there is one more very important factor to consider while choosing a language between Python vs R for data science. It is the organization you want to be a part of. Different organizations use different languages. If you are a fresher, go through the requirements of your target organizations and roles. It is always beneficial to be well-versed with the required skillset of your role.

R vs Python for Data Science: Conclusion

The battle between R vs Python for data science has been long continuing. There are whole communities of developers who support one language or the other. Python and R are both trending languages and have gained immense popularity in the data science industry.
To succeed in the data science industry, it is crucial to know at least one of these languages.

The learning curve of R is a bit steep compared to Python. Python with its easy syntax and better speed has become a favorite of many data scientists. R has been winning hearts with its better data visualization capabilities.

If you are someone who is new to data science - Python would be easier to understand. On the other hand, if you have experience in data science - R would come naturally to you. However, the most important point to consider is the industry requirement. So, you need to be clear with your goals and choose a language that best suits your target roles.