When it comes to learning data science, a question we hear a lot is, “Should I learn R or Python first?” If you’re at the very beginning of your journey, you might be wondering the same thing.
At a high level, R is a programming language designed specifically for working with data. Python is a general-purpose programming language, used widely for data science and for building software and web applications.
It’s not uncommon for data professionals to be well-versed in both languages — using R for some tasks, and Python for others. But if you’re just starting out on your journey, focusing on one language can help you learn the data science skills you’ll need to pursue a career in data, or to see a project through. Plus, once you’ve picked up one language, you’ll be able to pick up other languages more easily.
In this article, we’ll take a look at R and Python in more detail, to help you decide which programming language is right for you.
What is R?
R is a powerful statistical programming language built for data analysis and data science. It’s great for exploring patterns and trends within your data, building statistical models, and creating beautiful data visualizations.
Most people learn R to work with data, instead of for building software applications. Because it’s designed with this purpose in mind, the data structures and variable types in R are easy to use for data manipulation and analysis. Plus, R comes with many built-in data science functions, so you don’t have to worry about installing libraries when you’re just getting started.
As you get more accustomed to working with R, you’ll want to familiarize yourself with packages like tidyverse, dplyr, ggplot2, and caret. Packages are pieces of code that help you do all sorts of things with your data, such as organizing your data, creating beautiful graphics, training machine learning models, and more. Working with existing packages means you don’t have to write these data science functions from scratch.
Getting started with R
Interested in learning more about R? We suggest checking out our free Learn R course, where you’ll learn the fundamentals of data science, while picking up basic programming concepts in R. You can also dive in and learn how to manipulate large data sets, build statistical models, create beautiful visualizations, and explore machine learning with our Analyze Data with R Skill Path.
What is Python?
Python is a versatile, general-purpose programming language, praised for being concise and easy to read. It’s great for extracting large amounts of data from the web, building machine learning algorithms, and integrating data science tasks into larger software projects.
Python plays an important role in data science, web development, and a variety of software applications. Many people choose to learn Python for data science because they already know the language, or have used Python for a previous project. But even if you’re new to programming, Python is a beginner-friendly language that’s easy to learn once you get set up.
Setting up Python can take some time. To start doing any data science, you’ll need to download separate packages. Some key packages to know are pandas and Numpy for manipulating data, Matplotlib and seaborn for visualizing data, and SciPy, scikit-learn and statsmodels for hypothesis testing and model fitting. With many libraries being created for data science, Python has become a growing language within the data science world.
Getting started with Python
Interested in learning more about Python? We suggest checking out our Learn Python 3 course, where you’ll learn the most up-to-date version of Python, while picking up foundational programming concepts. We also recommend the Analyze data with Python Skill Path, where you’ll dive into statistics, data manipulation, data visualization, and hypothesis testing; and Learn Data Visualization with Python is a great next step.
Or you could try our free course Getting Started with Python for Data Science to get a taste of what it’s like to be a data scientist. We’ll show you how to use Python and industry-standard tools like Jupyter Notebook to analyze real datasets for answers to real data science questions. And if you want to pursue a career in data, try our Data Scientist: Analytics Specialist career path or Data Scientist: Machine Learning Specialist career path. If you’re not sure which path is right for you, check out this article on the difference between Data Analysts and Data Scientists.
To learn more about other data science languages, head over to our article on choosing a data science language.
Whichever language you end up choosing, we’re excited for you to start your journey in the world of data!
