Python or R: Which programming language is better for data science?

A little background on R

R is a programming language and analysis tool developed by Ross Ihaka and Robert Gentleman and first introduced in 1993. At the same time, it is also a free open source software with a rich statistical and graphical technology library.

R is one of the most used tools by analysts, statisticians, and researchers for retrieving, cleaning, analyzing, visualizing, and presenting data. It is used by many industries such as IT, banking, healthcare, and finance. R.

Purpose

• Data scientists can use the R programming language to collect data, perform statistical analysis, and produce visualizations.
• It can be used for graphical representation.
• R can be used for both machine learning and deep learning.
• It can also provide a sophisticated statistical tool for financial operations and calculations, R and its libraries can implement moving averages, stock market modeling and financial KDD.
• It also implements statistical methods such as linear and nonlinear modeling.

Statistical Computing: R is the most widely used programming language among statisticians. It helps statisticians with manipulation, collection, cleaning and analysis. It also has charting capabilities and produces interesting visuals from any recording.

Machine Learning: It includes libraries for some basic machine learning tasks, such as linear and nonlinear regression, decision trees, etc. You can use R to create machine learning algorithms in finance, retail, marketing, and healthcare.

A little background about Python

It is a well-known computer language and a widely used, interpreted, object-oriented programming language. Invented by Guido van Rossum and first released on February 20, 1991. It can be used for a variety of programming and software development in addition to web development, and can be used to create a complete end-to-end process.

Use

• It can be used for BDA management and complex mathematical calculations.
• It can connect to the database system, or read and edit files.
• It is suitable for software development, business applications, audio, video, back-end networking, mobile application development, etc.
• It enables analysts to generate Excel reports in less time.

Analysis: Python is very convenient for analysis. For example, if a database contains millions of rows and columns, extracting information from this data can be difficult and time-consuming. This is where libraries like Pandas, NumPy, and SciPy come in to get the job done quickly.

Extraction: Because data is not always available, we need to get it from the network. In this case, you can use the libraries Scrapy and Beautiful Soup to extract information from the Internet.

Graphical representation: Seaborn and Matplotlib libraries can create charts, pie charts, and other visual content.

Machine Learning: It also has a machine learning library. Scikit-Learn and PyBrain are one of these libraries that provide some fast machine learning and statistical modeling tools such as classification, regression, and clustering through an interface.

Benefits of Python

• Availability: Works on multiple systems (Windows, Mac, Linux, Raspberry Pi, etc.).
• Simple and easy: The syntax or words and symbols required for a computer program to work are intuitive and direct. They are actually English terms so it is readable. Relative to other technologies like C, Java, and C#, code execution time is reduced, so developers and software engineers can work longer.
• Libraries: They are a set of pre-assembled code that can be reused to reduce coding time. This saves you from having to write code from scratch.
• Flexibility: Compared to other languages ​​such as Java, it provides flexibility and can solve problems that would otherwise be impossible to solve. It turns out that it is scalable.

Now that we have explored these two programming languages ​​from various angles, the question “Which language is better for data science?” arises.

Choose Python or R?

The biggest difference between these two languages ​​is the way they handle situations. Both open source languages ​​receive large community support, and they are constantly expanding their libraries and tools.

However, a question you should ask yourself is, "What do you want to focus more on? Machine learning or statistical learning?"

Machine learning is a discipline of artificial intelligence, while statistics Learning is a branch of statistics. R is a statistical language, so it is a good fit in statistics. Anyone with a formal background in statistics can program in R because it is easy to understand. And Python is the best choice for machine learning. Large-scale applications are the focus of machine learning. Python seemed like an ideal choice because of its flexibility and scalability for use in production environments, especially when analytics must be connected to web applications.

Trend Analysis and Salary Comparison

As shown in the figure below, Python or R are the most popular search terms in the world. Trend-wise, Python has been more popular than R over the past decade.

According to PayScale.com, the average annual salary for Python developers in the United States is $79,395, while the average annual salary for R programs is $68,554 (as of this article’s publication).

Summary

Python is a powerful and adaptable programming language that can be used on a wide range of computers Scientific applications. R is a popular language for building analytics. In fact, both languages ​​have certain advantages and significance in the field of data science.

However, before you choose which language to use, you should ask yourself the following questions:

• Are you interested in learning machines and artificial intelligence or statistical learning and analysis? ?
• What are the most popular tools in your field?
• Do you want to become an analyst with a deeper understanding of data visualization, or use it to integrate web applications?
• How long are you willing to spend to master a programming language?

All in all, it is never a bad idea to learn these two languages, because "the skills are not overwhelming" will only benefit you as a computer science engineer.

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