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.
When you’re ready to start learning a programming language, consider enrolling in the IBM Data Analytics With Excel and R Professional Certificate. You’ll have the opportunity to learn how to conduct data analysis using R in as little as three months. Upon completion, you’ll have earned a career credential that demonstrates your expertise.
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 one of the more popular languages 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.
Considering a career change? Join Career Chat on LinkedIn to keep up with programming trends and data analysis job opportunities. As you learn about R and Python for data analysis, consider these helpful resources:
Broaden your knowledge: Data Analysis Terms and Definitions
Learn more about Python: Python Syntax Cheat Sheet
Hear from an expert: 7 Questions With a Data Analytics Professor
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与其以需求量来衡量每种编程语言,不如了解哪种语言最流行,因为这可能预示着更广阔的就业前景、更强大的库和更多的社区支持。
虽然 Python 是这两种语言中更受欢迎的语言,但最好还是查看一下招聘信息,看看哪种语言是首选或必需的。
Python 作为编程语言如此流行是有原因的。Python 被认为简单易学,其多用途结构使其适用于各种需求。
另一方面,R 是由统计学家创建的,用于更专项的课程,因此一开始可能比较难学,尽管许多人认为它总体上是一种相对简单的语言。
SQL 是数据分析师的另一种标准编程语言。分析师可能使用的其他语言包括JavaScript、Scala、Java、Julia 和 C/C++。
一般来说,掌握一门以上的编程语言是个好主意,这样可以增加你的通用性和竞争力。幸运的是,一旦掌握了另一种语言,学习新语言往往会变得更加容易。
TIOBE. "TIOBE Index for January 2025, https://www.tiobe.com/tiobe-index/." Accessed September 12, 2025.
Stack Overflow. "2025 Developer Survey: Technology, https://survey.stackoverflow.co/2025/technology." Accessed September 12, 2025.
GitHub. "PYPL PopularitY of Programming Language, https://pypl.github.io/PYPL.html." Accessed September 12, 2025.
RedMonk. "The RedMonk Programming Language Rankings: January 2025, https://redmonk.com/sogrady/2025/06/18/language-rankings-1-25/.” Accessed September 12, 2025.
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