Data science is a trending topic these days. As a data scientist, it's important to know programming languages such as Python and R to work on projects. Familiarity with both languages is critical for complex data analysis. With some overlap in use cases, these languages are loaded with handy features for analyzing data: linear regression, logistic regression, decision trees, clustering, neural networks, and more.

It can be tricky to decide whether you should learn and use Python or R for your data science work. Some prefer learning Python since it is modular and easy to read, while others prefer R since it is more convenient for analysis purposes.

 

But the truth is that it totally depends on the specifics of your situation and what you want to accomplish. The best way to learn a new skill is to choose one that most interests you. 

In this blog, I will discuss a comparison of Python and R and explain why Python is a better choice for beginners.

Python Vs R: What's the difference?

R and Python are the most powerful data science-oriented programming languages.

R is generally used for statistical analysis, while Python provides a more comprehensive approach to data science. Python and R are very similar in many aspects, such as their syntax, libraries, and capabilities. The real difference comes when you delve into the conceptual underpinnings of each language and then try to apply what you have learned. 

 

If you're looking to get into the data science game, an IBM-accredited Data analytics course is the best way to get a solid foundation in the field. 

 

R programming:

I'm a big fan of R and love using it to explore and analyze data from different sources. R is a domain-specific language used for data analysis and statistical computation. Statisticians have a special syntax for this, and it's an important aspect of academic and research data science. As a programming language, R provides objects, operators, and functions that enable users to explore, model, and visualize data. In short, R is especially used for analyzing data, but it can not be used for general-purpose web development.

 

Some of the R ecosystems include:

 

• RStudio

• R packages, reproducible R codes, and functions

• CRAN (the Comprehensive R Archive Network) 

 

How is R used in data science?

 R is a programming language that focuses on the statistical and graphical capabilities of the language. Learning R for data science will teach you how to conduct statistical studies and create data visualizations using the programming language. It is also simple to clean, import, and analyze data thanks to the statistical capabilities provided by R. It is a language that has gained a lot of popularity among data scientists and statisticians, but it really isn't the best for analyzing data. 

Python

Python is one of the fastest-growing and easiest programming languages worldwide. It is an object-oriented programming language that provides stability and modularity to projects of any size. It provides a flexible approach to web development and data science and is intuitive to non-programmers. In data science, Python may be used to analyze data, create simulations, build models and make visualizations. 

Some of the most widely-used Python libraries are:

 

• NumPy (Numerical analysis)

• Pandas (data analysis) 

• Sci-kit (Predictive analysis)

• Matplotlib (Object-oriented API for embedding plots) 

• Keras (Deep learning and AI) 

Here are the core reasons why I prefer Python: 

• Python is beginner-friendly.

Python's logical and approachable syntax makes it easier to determine the purpose of strings of code and is less formal than other programming languages. This emphasis on code readability decreases the learning curve and helps beginners learn programming languages.

 

• Python is scalable.

 Another reason why I love Python is that it is faster than R and can scale alongside projects. It provides the efficient workflows required to get people working in production, developing pipelines, or executing large-scale production off the ground. Python's production readiness is built on the foundation of its speed and scalability. Building full-scale machine learning pipelines for insights that keep up with the speed of business is made possible. Also, the modularity of the language makes it possible to build something flexible. 

• Python is multipurpose.

Python's use is not just confined to the data science community. It is also used by developers to create all kinds of applications. Python also works well with web-based applications and supports a variety of data types, including SQL. In addition, it is simple to find different datasets for every given project or to generate your own utilizing Python ecosystem products.

The problem is that many people become hesitant while beginning to program. Whereas the hardest part is starting over. Once you take the first step and enter the first line of code, eventually, you will find it easy to learn.

 

So which one is easier to learn first? – My opinion

There is no wrong decision when it comes to learning Python or R. Both are in-demand skills that will enable you to complete any data science project you encounter.

However, your decision on whether to start learning R or Python first will be influenced by the following factors: 

• Your Career objectives

• Your strategy for communicating your findings (results) 

• The amount of energy and time you intend to devote.

 

To conclude, If you're passionate about statistical computation and data visualization, R might be a good fit for you. Python, on the other hand, is a better choice if you want to become a data scientist and work with big data, artificial intelligence, and deep learning techniques.

 

Final thoughts

Data science is arguably the hottest skill on the market right now, and there is an abundance of open data positions out there. It's simple to see why it has such a strong fascination. Python is a high-level programming language that is modular, easy to learn, object-oriented, and prioritizes readability. R is another language widely used in statistics and data science but has a steep learning curve. If you are interested in beginning a career as a data scientist or data analyst, a  Data science course with placement is an excellent approach to strengthen your foundation through videos, assessments, interactive labs, and job-ready projects in less than a few months. Here, based on your interest you can opt for python or R specializations. So it’s not about any tricks or strategies that R or Python, which one should you choose- it depends on contextual requirements. 

Publication: 16/11/2022 10:09