Python or R for Data Analysis: Which Should You Learn?

Key takeaways

Data analysts often use Python or R for cleaning, manipulating, analyzing, and visualizing data.

• Python is a high-level, general-purpose programming language for data analysis, while R is a statistical programming language for data visualization. 

• R works well for creating graphics and building statistical models, while Python tends to excel in handling massive amounts of data, building deep learning models, and non-statistical tasks like web scraping. 

• You can learn both Python and R as a beginner through online courses, self-guided learning, or guided projects. 

Explore how to use Python and R for data analysis, including how they differ from one another, how to choose the right one for you, and ways to learn them. If you’re ready to start learning a programming language, enroll 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, predictive modeling using RStudio, and data visualization using R Shiny in as little as three months. Upon completion, you’ll have earned a career credential that demonstrates your expertise.

Python vs. R for data analysis: What’s the difference? 

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. 

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

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 converts into a low-level language, or machine code, that your computer can recognize and run.

Read more: Low-Level vs. High-Level Programming Languages

 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 

How do I choose between R vs. Python for data analysis?

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.

1. R vs. Python: Which is easier to learn?

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. 

2. 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 of which direction to take your learning.

3. R vs. Python: 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.

4. 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.

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 one of the most 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.

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. Guided Projects on Coursera offer a hands-on introduction in under two hours without having to buy or download any software. 

With Data Visualization using dplyr and ggplot2 in R, you can explore how to use different R packages for data visualization. With Python 101: Develop Your First Python Program, which takes just two hours to finish, you can write a simple game application as you learn the different data types in Python.

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. 

Further explore R, Python, and data analysis with free resources

Considering a career change? Join Career Chat on LinkedIn to keep up with industry trends and job opportunities. As you learn about R and Python for data analysis, consider these helpful resources:

• Broaden your knowledge: Data Analysis Terms and Definitions

• Bookmark for later: Python Syntax Cheat Sheet

• Hear from an expert: 7 Questions With a Data Analytics Professor

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