Most Popular Programming Languages For Data Science

A programming language is every developer’s strength. A new programming language or an upgrade to a current language comes up every now and then that promises to produce quicker and more efficient results. Developers will also find themselves involved with an extensive range of languages of programming, choosing which one to pick for their next project.

All languages are not identical, and what fits for one task or a specification may not work for another in several cases. Data Science is one of the growing fields in which productivity requirements and high-performance outcomes are rapidly rising. A few of such highest level Cloud Computing Assignment Help will be included in this report.

In this article we are going to discuss the different popular programming languages for data science. 



As it has statistical analysis capability, simple usability, and data modeling capabilities, Python is considered to be one of the common data science programming languages. In addition, it provides various libraries promoting data science. These libraries are utilized for various purposes, such as NumPy for numerical computations, Matplotlib for simulation of data, Pandas for data handling and interpretation of data, and much more. Python supports various paradigms as an object-oriented programming language. In addition, there are many factors why data scientists need to study the data science python, and they are:

  • Python is a high-level programming
  • It is an open-source programming language,
  • Python is interpreted



Another programming language used for numeric computations is Matlab, which makes it crucial for Data Science. For mathematical simulation, data analysis, and image processing, Matlab was often used. In computer science,  Fourier analysis, statistics, linear algebra, numerical integration, differential equations,  optimization, sorting,  and many more, numbers of mathematical functions, such as sqrt(x), are being used. In addition, Matlab has built-in graphics that, with the aid of different plots, are useful for displaying data visualizations. There are several explanations why students have to study data science Matlab, and these factors are:

  • Rich libraries,
  • Best for matrix calculations,
  • Only have lines of code, and much more.



Scala relates to a language which is scalable. It is a programming language that is an extension to Java since it was developed on a Java Virtual Machine. It has therefore been shown that it is readily incorporated with Java programming language. The fundamental explanation why Scala language is one of the best programming languages for data science is that it uses Apache Spark to help handle a large amount of data. Therefore, it will be right to suggest that if you have a huge volume of data to tackle, just go with this programming. For data science, there are many reasons to study Scala, and these are:


  • This programming language is a multi-paradigm, general-purpose, and high-level language.
  • Being an object-oriented programming language, it helps different functional programming procedures.
  • Scala carries unique features of different languages, such as Java, C++, C, and much more, making it more fertile and useful for data scientists.



Julia programming language is another preferred language for data science as it runs faster than R, Python, MatLab, and JavaScript. Apart from this, this language is a comprehensive language for numeric analysis as it has different mathematical libraries and various data manipulation mechanisms. Julia can simply integrate with many other programming languages, such as MatLab, Python, Matlab, Fortran, C++, and much more. Therefore below, we have given some of the reasons to learn Julia language for data science, and these are:


  • This programming language is an open-source language, which means any source codes are available easily.
  • Julia requires less lines of code that makes it resilient to use.
  • Julia has the “just-in-time” compiler characteristic that makes it much quicker to execute extensive data.


Structured Query Language

It is explicitly designed for the data contained inside the relational DBMS to be accessed and handled. The primary goal of data scientists is to turn knowledge into an operation that can be accomplished. This is why they need SQL to obtain the data from the databases. Postgres,  SQLite, Microsoft SQL Server, Oracle, MySQL,  and many more are many esteemed databases. There is a major function of the SQL in data science, and data scientist utilize it because:

  • It is very powerful at managing, modernizing, and querying relational databases.
  • Because of declarative syntax, Structured programming language is an effortlessly readable language.
  • Structured programming language modules, like SQLAlchemy, supports SQL to be quickly integrated with different languages.



There are many other programming languages for Data science but we have listed some of the top used languages. Every programming language has their unique speciality which makes it the best choice for data science. Some have Rich libraries while others are high level and open source programming languages. It’s up to you which one suits you. In case if we miss any information please let us know in the comment section.

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