Written by Coursera Staff • Updated on
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, you might also consider enrolling in either Google's Data Analysis with R Programming course or IBM's Data Analysis with Python course.
Python vs. R: What’s the difference?
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.
What is Python?
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
Read more: What Is Python Used For? A Beginner’s Guide to Using Python
What does “high-level” mean?
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.
What is R?
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:
How do I choose between Python and R?
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.
Python and R: By the numbers
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.
Learning curve
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.
Tip: Once you’ve learned one programming language, it’s typically easier to learn another one.
Read more: Popular Programming Languages
Company
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.
Strengths and weaknesses
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 |
Career path
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.
How to learn R or Python: Options to get started
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.
Try both through Guided Projects
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.
Practice your Python skills independently with tutorials
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.
Take a course
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.
Earn a Professional Certificate
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.
Frequently asked questions (FAQ)
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.
Updated on
Written by:
C
Coursera Staff
Editorial Team
Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...
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.
