Python vs R for Data Science: Which Language is Better

Python vs R: which language is better for a data science career? Is it hard to choose one out of these two amazingly flexible data analytics languages to learn?

Python and R are the two most popular programming languages for data science. Both languages are free and open source. Both are absolutely essential if you're interested in machine learning, large datasets analysis, or creating complex data visualizations. Although Python and R are both good for data science, there are a few factors that may help you to determine which language is better for you.

Python vs R: An Overview

Python

Python is an object-oriented and general-purpose language. It makes collaboration across your organization easy through its simple syntax. It allows you to execute tasks with better stability, flexibility, and code readability.

R

R is also a preferred programming language of many data scientists. R is for statistical analysis, used for data analysis and statistical modeling. R is a highly extensible and the best way to model data.

Python vs R: The Main Advantages or Reasons Why You Should Learn One of these Languages for Data Science Career

Python

• One of the easiest languages to start your data science journey.
• Python is a high-level language that provides a great approach to object-oriented programming.
• Since it is an open-source language, it's flexible and continuously improving.
• Python is one of the most beginner-friendly programming languages and easy to learn for data science.
• Python does not cost you anything to use or download it or to add it to the application.
• Due to elegant syntax, the programs are easier to read.
• It has an enormous community.
• Python is the language that is a leader in scaling very fast.
• It provides hundreds of different libraries and frameworks which makes it more popular. Some of the great libraries are Pandas, NumPy, Matplotlib, and SciPy. It saves a lot of your manual time and replaces the whole solution easily.
• Python has a huge and powerful community that can get you support from other specialists and make your learning path easy.
• It is also said that Python can also provide you with high paying opportunities.

If you're wondering how much python is required for data science work, check out our article on How Much Python is Required for Data Science.

R

1. R has been designed especially for statistical analysis and data reconfiguration.
2. R has many different tools to help us in data visualization, data analysis, and representation.
3. It provides various important packages for data and database manipulation and wrangling which is really helpful in data science.
4. R makes machine learning easier and more approachable.
5. R is also an open-source tool, hence the developments in this language happen at a rapid scale.
6. Since it was originally designed for doing statistical analysis, R makes it easy to create complex statistical models.
7. Once you’ve gotten through the fundamentals - data manipulation, visualization, and machine learning can be easier in R.
8. It can make you more flexible and marketable employee when you look for data science jobs.

Factors to Determine What data science programming language is Better for You

Following are a few factors that may help you to determine which data science programming language is better for you.

1. Programming Experience

Due to similar syntax to other languages, Python is the language for you to make a career in data science if you have some programming experience. The programs are easier to read. You might face a hurdle to jump into the programming process in R.

2. Machine Learning or Statistical Learning

Since R was originally designed for doing statistical analysis, R makes it easy to create complex statistical models. If you are from a formal statistics background you can use R easily in data science. On the other hand, Python with its flexibility for production use is better for machine learning. Python is much better when you need to integrate data analysis tasks with applications.

3. Choose Between Academia or Industry

R is a statistical programming software and better for academia or research. Python, on the other hand, is a full-fledged programming language and many organizations use it in their production systems.

4. Visualize Your Data in Beautiful Graphics

In data science, graphs are made to talk and R is making it beautiful. R language is specialized around statistical computing and graphics. Python has such advantages in Matplotlib but R still seems to be much better at data visualization.

Conclusion

Python is a versatile and powerful language that data scientists can use for a variety of tasks. As a beginner or non-programmer, you can pick up Python pretty easily and develop a versatile data science toolkit.
R, on the other hand, is designed specifically for the programming environment. You need to understand and go deeper if you want to make it far in your career as a data scientist.

Both languages have their respective strengths and help you to make your career as a data scientist. As a data scientist, you need to learn one programming language. And the choice between Python or R depends on objectives of your mission: Statistical analysis or deployment, and your industry or company most-used tool.

Also, check out our post "Python vs R for data science" in which we'll find the best language for Data Science.