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.
Python and R are both free, open-source languages that can run on Windows, macOS, and Linux. Both can handle just about any data analysis task, 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
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 gets converted 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:
Hear more about what R can do from Carrie, a data analyst at Google.
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.
According to several popular programming language indices, TIOBE , Stack Overflow , PYPL , and RedMonk,  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.
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 easy-to-read syntax gives it a smoother learning curve. R tends to have a steeper learning curve at the beginning, but once you understand how to use its features, it gets significantly easier.
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 for 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.
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.
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.
Earning a Google Data Analytics Professional Certificate or IBM Data Analyst Professional Certificate gives you a framework for learning a statistical programming language within the greater context of data analysis. The Google certificate teaches R, and the IBM certificate teaches Python. Both include other job-ready skills, like SQL, spreadsheets, and data visualization. Not only can you learn to program, you can learn how all these critical data skills work together.
If you’re interested in starting a career as a data analyst, these programs are a great way to build your foundation through videos, assessments, interactive labs, and portfolio-ready projects. Both can be completed in less than six months.
This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.
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Skills you'll build:
Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study
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.
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 April 2021, https://www.tiobe.com/tiobe-index/." Accessed May 29, 2022.
Stack Overflow. "2020 Developer Survey, https://insights.stackoverflow.com/survey/2020." Accessed May 29, 2022.
GitHub. "PYPL PopularitY of Programming Language, https://pypl.github.io/PYPL.html." Accessed May 29, 2022.
RedMonk. "The RedMonk Programming Language Rankings: January 2021, https://redmonk.com/sogrady/2021/03/01/language-rankings-1-21/." Accessed May 29, 2022.
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.