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

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, analysing, and visualising data, you’re looking at either Python or R.

When it comes to learning Python or R, you’ll find a number of similarities and differences, with no one-size-fits-all solution. Discover some of the features of both languages and how to choose which is right for you.

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 users consider both relatively easy programming languages to learn, especially for beginners. So, which should you choose to learn (or learn first)? Take a look at the following broad overview of each language to help answer the question.

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 recognise and run.

What is R?

R is a software environment and statistical programming language built for statistical computing and data visualisation. R’s numerous abilities tend to fall into three broad categories:

• Manipulating data

• Statistical analysis

• Visualising 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 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.

Learning curve

Many consider both Python and R 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, 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.  

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.

Strengths and weaknesses

While both Python and R can accomplish many of the same data tasks, they each have unique strengths. If you know you’ll be spending lots of time on certain data tasks, you might want to prioritise the language that excels at those tasks.

Career path

Think about how learning a programming language fits in with your long-term career goals. If you’re passionate about the statistical calculation and data visualisation 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 start

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. 

In Using probability distributions for real-world problems in R, you can learn how to apply probability distributions in R and various approaches to creating visualisations of these distributions to deal with real-world problems. With Python 101: Develop Your First Python Program, which takes only two hours to finish, you can develop a simple game and discover various Python data types such as String, List, Integer, and Float.

Practise your Python skills with tutorials

Discover step-by-step guides for troubleshooting Python basics like syntax, if-else statements, and exceptions, as well as 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. You can choose from many different classes. On Coursera, the most popular options amongst learners are Programming for Everybody (Getting Started with Python) from the University of Michigan and Data Visualisation and Dashboarding with R Specialisation 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 visualisation. 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. You can complete both in less than six months.

Frequently asked questions (FAQ)