Data Science Programming Languages

Data Science Programming Languages

Data Science Programming Languages: List of data science programming languages that aspirants need to learn to improve their career.


Python is an extremely popular general purpose, dynamic, and is a widely used language within the data science community. It is commonly referred to as the easiest programming language to read and to learn. Since it combines quick improvement with the capacity to interface with high-performance algorithms written in Fortran or C, it has become the leading programming language for open data science.

With the advancement of technologies such as artificial intelligence, machine learning, and predictive analytics, the demand for experts with Python skills is rising significantly. It is widely used in web development, scientific computing, data mining, and others.

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is a very unique language and has some really interesting features which aren’t present in other languages. These features are very important for data science applications. Being a vector language, R can do many things at once, functions can be added to a single vector without putting it in a loop. As the power of R is being realized, it is finding use in a variety of other places, starting from financial studies to genetics and biology and medicine.

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Java is an extremely popular, general purpose language which runs on the Java Virtual Machine (JVM). Many numbers of organizations, particularly MNC organizations use this language to create backend systems and desktop/mobile/web applications. It is an Oracle-supported unique computing system that empowers portability between platforms.

Due to the demand for Java skills is raising, it has been called a pillar of the organization programming stack. Having said that a significant rise in demand, Java skills have been in demand especially for software architects, software engineers, and DevOps engineers.

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SQL (Structured Query Language) is a domain-specific language used in programming and designed for managing data held in a relational database management system. As the role of a data scientist is to turn raw data into actionable insights, therefore they primarily use SQL for data retrieval. To be an effective data scientist, they must know how to wrangle and extract data from the databases using SQL language.

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