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.
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.
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!
Python is a general-purpose programming language, while R is a statistical programming language. This means that Python is more versatile and can be used for a wider range of tasks, such as web development, data manipulation, and machine learning.
Python currently supports 15.7 million worldwide developers while R supports fewer than 1.4 million. This makes Python the most popular programming language out of the two. The only programming language that outpaces Python is JavaScript, which has 17.4 million developers.
By learning R, you'll equip yourself with the skills to tackle complex data challenges and drive innovation in your field. Whether you're a student looking to launch your career or a professional aiming to upskill, R could be your key to success in the evolving world of data science.
Yes. Python is a popular and flexible language that's used professionally in a wide variety of contexts. We teach Python for data science and machine learning, but you can also apply your skills in other areas. Python is used in finance, web development, software engineering, game development, and more.
R can't be used in production code because of its focus on research, while Python, a general-purpose language, can be used both for prototyping and as a product itself. Python also runs faster than R, despite its GIL problems.
Consider your goals! Python is generally easier to learn for beginners and offers broader use. If your focus is heavily on statistics and data visualization, R's specialized strengths might be a better fit. Let's have a closer look on the fact that why should we choose python or R.
According to a report by TIOBE, Python remains the number one most in-demand programming language. It is also one of the most in-demand tech skills, with several companies using ML and AI to run critical operations.
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.
Performing statistical analysis in R is a valuable skill for aspiring data analysts to learn in 2024. R provides a wide range of functions and packages that make it easier to prepare data and perform complex analyses.
Although the time it takes to learn R depends on several factors, most individuals can become familiar with this coding language in about four to six weeks. You can receive comprehensive R programming training through Noble Desktop's in-person or live online courses.
Two of the most obvious choices for data scientists are Python and R, given their versatility and ubiquity. Of course, working with data also means working with databases, so SQL is another essential programming language. Thankfully, it's a relatively straightforward language once you've learned Python and R.
In 2024, Python stands as more than a programming language; it's a gateway to automation, technological exploration, and innovation. It's user-friendly, versatile, and a skill that opens doors in countless fields.
With dedication, students can typically learn Python for data science fundamentals in about six months, preparing them for roles as data scientists, data engineers, software engineers, and more.
If you're doing machine learning, Python is still light-years ahead of R. If you're working with software engineers, they're going to be much happier working with you if you use Python. If you're looking for a job, you're going to be much more employable if you're already comfortable with Python.
Python has a far more comprehensive approach and is also suitable for programming software and deep learning. Scope and popularity: More and more people are using R outside of academia, but the language does still have its roots in science. Python is used by significantly more developers.
Python performs poorly in statistical analysis compared to R due to a lack of statistical packages. Sometimes developers may face runtime errors due to the dynamically typed nature. The flexible data type in Python consumes a lot of memory, causing tasks requiring heavy memory to suffer.
R: R is mostly used by data scientists as it is used only for data analysis. But compared to Python, it has been outraced. As finance involves the calculation and analysis of data R would be best for you. Python: Python is being used in almost all industries for data science, machine learning, and developing.
R's popularity can be attributed in part to its extensive selection of visualization libraries. Consequently, there is currently a high demand for professionals who specialize in Data Visualization using R programming.
Whereas, R is limited to statistics and analysis. Many data scientists and software developers select python over R because of its: Readability: Python is extremely easy to read and understand. Popularity: One of the most popular open-source programming languages for data scientists.
In conclusion, both R and Python have their strengths and weaknesses for economic and econometric analysis. R has a more specialized focus on statistical analysis, making it an excellent choice for those who need to perform more complex econometric analyses.
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