Is it better to learn R or Python for a career as a data analyst? Learn more about how to choose the best statistical programming language for your career goals.
One of the most important skills for a data analyst is proficiency in a programming language. Data analysts use SQL (Structured Query Language) to communicate with databases, but when it comes to cleaning, manipulating, analyzing, and visualizing data, you’re looking at either Python or R.
In this article, we'll explore how Python and R are used for data analysis, including how they differ from one another, how to choose the right one for you, and ways to learn them. Afterward, if you want to take the next step to learning either of these impactful programming languages, consider enrolling in either Google's Data Analysis with R Programming course or IBM's Data Analysis with Python course.
Python and R are both free, open-source languages that can run on Windows, macOS, and Linux. Both can handle a wide range of data analysis tasks, and both are considered relatively easy languages to learn, especially for beginners. So, which should you choose to learn (or learn first)? Before we dig into the differences, here’s a broad overview of each language.
Python is a high-level, general-purpose programming language known for its intuitive syntax that mimics natural language. You can use Python code for a wide variety of tasks, but three popular applications include:
Data science and data analysis
Web application development
Automation/scripting
A high-level programming language features a syntax that is easy for humans to read and understand. Low-level languages are those that can be easily understood by a machine. Examples of high-level languages include Python, C++, C#, and Java.
When you write code in a high-level language, it converts into a low-level language, or machine code, that your computer can recognize and run.
R is a software environment and statistical programming language built for statistical computing and data visualization. R’s numerous abilities tend to fall into three broad categories:
Manipulating data
Statistical analysis
Visualizing data
Hear more about what R can do from Carrie, a data analyst at Google, in this lecture from Google's Data Analytics Professional Certificate:
There’s no wrong choice when it comes to learning Python or R. Both are in-demand skills and will allow you to perform just about any data analytics task you’ll encounter. Which one is better for you will ultimately come down to your background, interests, and career goals.
As you make your decision, here are some things to consider.
Both Python and R are considered fairly easy languages to learn. Python was originally designed for software development. If you have previous experience with Java or C++, you may be able to pick up Python more naturally than R. If you have a background in statistics, on the other hand, R could be a bit easier.
Overall, Python’s readable syntax gives it a smoother learning curve. R tends to have a steep learning curve at the beginning, but once you understand how to use its features, it gets significantly easier.
Tip: Once you’ve learned one programming language, it’s typically easier to learn another one.
In general, it’s a good idea to “speak” the same language as the team with which you’ll be working. This makes it easier to share code and collaborate on projects.
If you’re just starting out, you may not know what company you’ll eventually work for. Take a look at a few job listings for the companies and industries you’re most interested in. Do they tend to list R or Python as a requirement? This could be a good indication of which direction to take your learning.
While both Python and R can accomplish many of the same data tasks, they each have their own unique strengths. If you know you’ll be spending lots of time on certain data tasks, you might want to prioritize the language that excels at those tasks.
Python is better for... | R is better for... |
---|---|
Handling massive amounts of data | Creating graphics and data visualizations |
Building deep learning models | Building statistical models |
Performing non-statistical tasks, like web scraping, saving to databases, and running workflows | Its robust ecosystem of statistical packages |
Think about how learning a programming language fits in with your longer-term career goals. If you’re passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you.
If, on the other hand, you’re interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit.
The same is true if your personal or professional interests extend beyond data and into programming, development, or other computer science fields. Python is a general-purpose language used for a much wider range of tasks than R.
According to several popular programming language indices, TIOBE [1], Stack Overflow [2], PYPL [3], and RedMonk, [4] Python is far and away the more popular language across the broader tech community.
While this doesn’t necessarily mean it’s better, it does suggest that it’s more widely used and may have a more robust community for ongoing support and development.
Python and R are both excellent languages for data. They’re also both appropriate for beginners with no previous coding experience. Luckily, no matter which language you choose to pursue first, you’ll find a wide range of resources and materials to help you along the way. These are just a few options for getting started.
Another great way to decide whether to learn R or Python is to try them both out. Coursera’s Guided Projects offer a hands-on introduction in under two hours without having to buy or download any software.
With Getting Started with R, you can start writing basic R commands and learn how to install packages and import data sets. With Introduction to Python, which takes under an hour to finish, you can write a guessing game application as you learn to create variables, decision constructs, and loops.
Not ready to commit to a course or a boot camp yet? You can read step-by-step guides for troubleshooting Python basics like syntax, if-else statements, exceptions, and working with loops in Coursera's free programming tutorials.
If you prefer focusing on one skill at a time (or if you’re adding a new coding language to your existing data analyst skill set), a course in Python or R could get you started. There are a ton of classes out there to choose from. On Coursera, the most popular options among learners are Programming for Everybody (Getting Started with Python) from the University of Michigan and R Programming from Johns Hopkins University.
Tip: For many learners, it may be better to pick one language and get proficient rather than trying to learn both at the same time.
If you’re interested in starting a career as a data analyst, Coursera offers a wide range of educational programs to help you build job-ready skills. Consider taking one of the following courses today:
To learn data analysis skills and R programming, take the Google Data Analytics Professional Certificate. In this beginner-friendly program, you'll gain an immersive understanding of data analysis job roles, learn how to clean and organize data for analysis, and build critical skills using tools like spreadsheets, SQL, and R programming.
For data analysis skills and Python, enroll in the IBM Data Analyst Professional Certificate. Through this beginner-level program, you'll master up-to-date practical skills data analysts use every day, learn how to visualize data and present findings, and develop a working knowledge of the Python language and its extensive libraries.
To learn to program and analyze data with Python, consider the University of Michigan's Python for Everybody Specialization. In as little as two months, you'll learn how to develop Python programs to gather, clean, analyze, and visualize data.
Instead of measuring each programming language in terms of demand, it can help to know which is most popular because that may indicate greater job prospects, more robust libraries, and increased community support.
While Python is the more popular language of the two, it’s a good idea to review job postings to see which language is preferred or required.
There’s a reason Python is so popular as a programming language. It’s considered easy to learn and its multi-purpose structure makes it applicable to a wide variety of needs.
R, on the other hand, was built by statisticians to serve more specialized uses, so it may be more difficult to learn at first, though many consider it a relatively easy language overall.
SQL is another standard programming language for data analysts. Other languages analysts may use include JavaScript, Scala, Java, Julia, and C/C++.
It’s generally a good idea to know more than one programming language to increase your versatility and competitiveness. Luckily, it's often easier to learn a new language once you’ve mastered another.
TIOBE. "TIOBE Index for January 2025, https://www.tiobe.com/tiobe-index/." Accessed February 4, 2025.
Stack Overflow. "2020 Developer Survey, https://insights.stackoverflow.com/survey/2020." Accessed February 4, 2025.
GitHub. "PYPL PopularitY of Programming Language, https://pypl.github.io/PYPL.html." Accessed February 4, 2025.
RedMonk. "The RedMonk Programming Language Rankings: January 2021, https://redmonk.com/sogrady/2021/03/01/language-rankings-1-21/." Accessed February 4, 2025.
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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
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