Python or R: Which to Learn as a New Data Analyst?

Thinking about becoming a data analyst? It’s a very promising career path, but data analysts are often required to master at least one programming language. Let’s explore whether this should be Python or R.

If you scroll through a couple of data analyst job descriptions, you’ll notice that most of them have a requirement for at least one programming language – Python, R, or SQL. SQL is somewhat unique as the key language for communicating with relational databases. But Python and R seem to be used for the same tasks – data analysis and visualization.

There are benefits to mastering both Python and R, but beginner data analysts often wonder which of these two programming languages they should learn first. After all, they want to be able to perform complex analyses of large amounts of data and to be of higher value to any data-driven organization. It can be challenging to make this choice without any prior experience as a data analyst, but many potential employers expect even beginner data analysts to have some knowledge of at least one of these languages. Furthermore, you’ll need to demonstrate these skills with a strong portfolio of relevant data analysis projects. So you need to start somewhere.

Both Python and R are widely used for data analysis and visualization. To help you make the best choice, let’s discuss the key factors a beginner data analyst needs to consider when choosing their first programming language. I hope that after reading this article, you’ll be able to make your choice and start learning right away.

If you happen to choose Python, I recommend taking our Python for Data Science learning track. With five interactive courses and hundreds of coding challenges, you’ll master all the basics you need to start your career in data analysis.

And now let’s dig into the Python vs. R debate. We’ll start with a brief review of the history of these programming languages.

A Brief Look into History

Both Python and R have quite a long history, with their first implementations dating back to the 1990s.

Python was created by Dutch programmer Guido van Rossum and first released in 1991. In the 1990s and early 2000s, Python was most popular as a scripting language. But later, as data science and machine learning got to the top of the Google search requests, Python also became a key programming language for these areas. It continues to be widely used for task automation and the development of web applications and software, but Python’s ongoing surge in popularity owes much to its key role in data analysis, data visualization, and machine learning.

R was first introduced in 1993 by Robert Gentleman and Ross Ihaka, statisticians at the University of Auckland in New Zealand. This programming language emerged as an open-source implementation of S, a language for statistical analysis. The name is a play on the name of the S language as well as the first initial of R’s two creators. In 1995, R was officially released as an open-sourced project; it has since become one of the most important programming languages for statistical analysis.

Even though both Python and R are widely used for data analysis, their application areas are quite different. Let’s have a look.

Python vs. R: Common Uses

Python is a general-purpose programming language. This means it can be used in a variety of different applications, including task automation and the development of software, web applications, and games. With the emergence of data science and machine learning, Python was also recognized as a great tool for data analysis and data visualization. It was already widely used in organizations, and its simple syntax allowed it to be easily mastered by business analysts, data analysts, and others without any programming experience. As a result, Python became the most popular language for data analysis in business and production environments.

But when it comes to academia and research, another programming language is the leader.

R was built for working with data, specifically for statistical analysis and data visualization. Thus, it’s highly optimized for these particular tasks. Created by two statistical professors, R became the key programming language for data analysis among scientists. It has all the necessary tools that academics and researchers need to analyze the results of their experiments, test their scientific hypotheses, and find patterns in the large amounts of collected data.

Thus, we have two great languages for data analysis that are widely used in different environments. While Python dominates the business environment, R is dominant in research. This is an important factor to consider when choosing your first programming language for data analysis – are you looking for a career in business or academia?

Let’s take a look at other important factors to take into account.

Should You Choose Python or R?

When choosing a programming language to start with, you need to consider more than just application areas. The following aspects might impact your choice:

Beginner-Friendly

Python and R are both appropriate for beginners with no previous coding experience:

• Python has easy-to-read syntax, which results in a lower learning curve for beginners. It also has a very rich ecosystem of libraries and packages that makes complex data analysis and building advanced machine learning models more approachable. And with Jupyter Notebooks or other interactive development environments (IDEs) for data science, your data analysis experience with Python will be smooth and fun.
• R is also considered a beginner-friendly language. It might have a steeper learning curve at the beginning, but once you understand the basic features, it gets significantly easier. Having a background in statistics would probably make learning R a bit easier. Like Python, R has a long list of packages that assist with data loading, cleaning, visualization, analysis, and modeling. To analyze data with R, you may use RStudio or Jupyter Notebook (after having an R kernel installed).

Use Cases

While R is used only for working with data, Python has a wide range of applications:

• Python is a great tool for data analysis, but it is also used for scripting, testing language, for software and web development, etc. With Python, you can both build a machine learning model and deploy it in a production environment. Choosing Python as a first programming language provides you with a wider range of potential career paths. If you decide later that you want to move from data analysis to software development, knowing Python would give you a solid basis in this new area.
• R is a programming language that focuses exclusively on data analysis. With R, you can only build projects related to statistical analysis and visualization. If you are determined to build a career around data analysis, especially in the research environment, R is the right choice for you.

To sum up, if you plan to deploy your work in a production environment and want to start with a wider range of potential career paths, Python would be the better choice for you. If you plan to work in academia and use a programming language for statistical analysis and hypothesis testing, you should probably go with R.

Learning Resources

There are plenty of tutorials and online courses available for both Python and R. However, with that wide range of resources comes another challenge – selecting the best ones.

When it comes to Python, I recommend starting with our Introduction to Python for Data Science course. This course includes 141 interactive exercises and covers basic calculations and visualizations, working with variables and data frames in Python, etc.

When you’re ready to go beyond the basics, try our Python for Data Science learning track. It includes five interactive courses that will teach you how to work with strings and process JSON, CSV, and Excel files in Python.

If you choose to learn R as your first programming language for data analysis, try to find interactive courses that let you practice coding right from the start. You may want to check Coursera, Udemy, and DataCamp for high-quality online R courses and tutorials.

Salary Prospects

Salary should also be a factor when choosing your first programming language. Unfortunately, it’s hard to find reliable statistics for the salaries of Python vs. R data analysts. But there are several resources that report the average salaries of Python and R programmers.

For example, the Stack Overflow Developer Survey 2022 collected responses from 70,000 developers from all over the world and revealed that Python programmers are usually paid slightly better than R programmers. Specifically, experienced R programmers make an average of $70,328 per year, while experienced Python programmers are paid $74,127 per year.

According to Glassdoor data, both Python and R developers earn $100K+ in the US. If we compare programmers working in these two languages, yearly income would be about the same – $115,708 for R programmers and $113,916 for Python developers.

Bonus. If you are looking for a job in data science, here are two useful resources: 15 Python Interview Questions for Data Science and Python Data Science Project Ideas.

Thanks for reading, and happy learning!