Python or R for Data Analysis: Which Should I Learn?
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.
If you’re getting started in data analysis, you’ll find that one of the most important skills is proficiency in a statistical 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 vs. R: What’s the difference?
Both Python and R are 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
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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 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. Start learning with R Books for Beginners
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 you’ll be working with. 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.
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. Get your Certificate in Data Scientist with Data Scientist Nanodegree
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.
Earn a Professional Certificate
Earning a Data Analyst Nanodegree Professional Certificate gives you a framework for learning a statistical programming language within the greater context of data analysis. The Analyst certificate teaches R, it 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.
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.