Best programming languages for data scientists.

Nishita Gupta
Nishita Gupta March 10, 2023
Updated 2023/03/11 at 3:58 PM

If you are considering starting a data science career, the sooner you start coding, the better. Learning to code is a critical step for every aspiring data scientist. However, getting started in programming can be daunting, especially if you don’t have previous coding experience.

In this article, we will look at some of the top data science programming languages for 2022, and present the strengths and capabilities of each of them.

1. Python

Python is an open-source, general-purpose programming language with broad applicability not only in the data science industry but also in other domains, like web development and video game development.

2. R

Not yet as highly trending as Python according to the popularity indices, R is a top option for aspiring data scientists. Frequently portrayed in data science forums as the main competitor of Python, learning one of these two languages is a critical step to breaking into the field.

3. SQL

Much of the world’s data is stored in databases. SQL (Structured Query Language) is a domain-specific language that allows programmers to communicate with, edit, and extract data from databases. Having a working knowledge of databases and SQL is a must if you want to become a data scientist.

4. Java

Java is one of the most popular programming languages ​​in the world. It’s an open-source, object-oriented language, known for its first-class performance and efficiency. Endless technologies, software applications, and websites rely on the Java ecosystem. Although Java is a preferred choice when developing websites or building applications from scratch, in recent years, Java has gained a prominent role in the data science industry

5. C/C++


Considered two of the most optimized languages, being familiar with C and its close relative C++, can be very useful when it comes to addressing computationally intensive data science jobs.

C and C++ are comparatively faster than other programming languages, making them well-suited candidates for developing big data and machine learning applications.

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