Top programming languages ​​for data science

A data scientist is a technical expert who uses mathematical and statistical techniques to manipulate, analyze, and extract information from data. However, data science requires the use of excellent programming languages. Here are the 10 best languages ​​in 2022.


Python is an open source data science programming language. This means that it is universal and applicable in other fields like web development and video game development as well.

Python has a rich ecosystem of libraries. Hence, it can perform all data science tasks. This includes all kinds of operations, from data pre-processing to visualization to statistical analysis. All manner of machine learning and deep learning model deployments add to this list.

Python consists of a simple and readable syntax. Therefore, it is considered one of the easiest programming languages ​​to learn and use. For this reason, it is also very suitable for beginners.


R is Python’s main competitor. However, he is not yet as trendy as he is. R is a data science programming language for aspiring data scientists. It is also open source but remains domain specific. It is a perfect language for data manipulation, processing and visualization. It is also ideal for statistical calculations and machine learning.

Learning R is essential, whether you’re starting out in data science or just want to learn a new skill.


SQL (Structured Query Language) is also a domain-specific data science programming language. For its part, it enables the communication, modification and extraction of data from databases. Knowledge of SQL enables working with multiple relational databases. This even includes popular systems like SQLite, MySQL and PostgreSQL. SQL is a versatile data science programming language. In addition, SQL consists of a declarative and simple syntax. Consequently, it is very easy to learn compared to other languages.

Granted, the choice is almost always between R and Python. But learning SQL also remains an essential option.


Java is ranked #2 in the PYPL Index and #3 in TIOBE. It is ultra efficient and undeniably effective. As a result, it is one of the most popular data science programming languages ​​in the world. It’s also open source, but more object-oriented. The Java ecosystem consists of endless technologies, software applications, and websites.

Java virtual machines provide a solid and efficient framework for popular big data tools like Hadoop or Spark. It has also flourished in the big data science industry in recent years.

Java is the ideal language for developing ETL tasks. It is also the most reliable for running tasks with large memory and complex requirements.


Established in 2011, Julia has already made an impact on the digital computing world. Compared to other languages, Julia is particularly efficient at data analysis. She is also called the heir of Python, by the way. This data science programming language has been distinguished by its early adoption by several reputable organizations. And most of them in the financial sector.

However, Julia is not yet mature enough to compete with the best data science languages. This is because it has a small community and doesn’t have as many libraries as its main competitors. His main disadvantage to this day remains his youth.


Scala is a data science programming language developed in 2004. It was specifically designed to be a clearer and less verbose version of Java. Scala is interoperable with Java because it can run in its virtual machine. This makes Scala perfect for distributed big data projects. Also, it has emerged as one of the best languages ​​for machine learning and big data. Scala is ranked 18th in the PYPL index and the 33ᵉ in TIOBE. In the data science context, however, it is mandatory to talk about it.

C and C++

C is a close relative of C++. Both are considered the most optimized. They are particularly useful when processing computationally intensive data science work. Their big advantage is their speed. Therefore, they easily adapt to the development of big data and machine learning applications. On the other hand, they have the disadvantage of being inherently low-level. However, learning is still a cheap option to tweak a profile.


JavaScript is the most popular data science programming language today. It is not only multiparadigmatic, but also versatile. JavaScript is known for its ability to create rich and interactive web pages.

Javascript is commonly used for web development. However, it has also gained notoriety in the data science industry. This language supports automatic libraries, deep learning and extremely powerful visualization tools.


Swift stands out from the crowd because it is a data science programming language designed for mobile devices. Apple created it to facilitate the creation of applications and to develop its ecosystem of applications. This can also increase customer loyalty. In addition, Swift is interoperable with Python. Also, one of its added benefits is that it is no longer limited to the iOS ecosystem. Also, it became open source to run on Linux.


Go (or GoLang) has become a renowned data science programming language for machine learning projects. It is both flexible and easy to understand. Developed by Google in 2009, it has C-like syntax and layouts. According to many developers, Go is the 21st century version of C. The downside of Go to date is its reduced community. However, it presents itself as an excellent ally for machine learning tasks.

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