Top 6 Languages for Data Science

Author:- Careers of Tomorrow

A Harvard business review published in 2012 called data science as “the sexiest job of the 21st-century.” In 2019, it stands justified. 

With the growing use of machine learning and artificial intelligence, the field "data science" has gained popularity among the tech-savvy. Most directly, data science is an approach to uncover knowledge from data, either structured or unstructured, utilizing scientific procedures and algorithms. To be a pioneer in data science programming, one needs a good command of at least one supported language. 
Whether you are a beginner or an expert in the field of data science, some fundamental things you have to remember is to analyze data, apply programming tools, for example, sequence and selection on data, and to perform basic data visualizations.


The R programming language is popular as it is widely utilized for data analyzing by data miners and scientists. It is likewise popular among analysts to make statistics and other duties simpler. It has strong object-oriented programming facilities which give it an advantage over other languages. R has static graphics which makes it easier to make graphs and other numerical images. Some things you can do with R are making arrays, vectors, matrices. It is a good option in contrast to SAS and Matlab. R has turned into the most used language for organizations. Google and Facebook use R. 


Python is another widely used, simple multi-paradigm programming language. The best strength of it is its enormous number of libraries which can enable you to complete a variety of tasks, for example, graphical UI, multimedia, automation, databases, content, and image processing. Also, it is easy to learn and program. Both students and recruiters prefer Python.

Java is one of the oldest languages used by data scientists. In spite of the fact that has been quite a number of new languages being created, Java continues to stand strong. The uniqueness of Java lies in "write once, run anywhere.” When the code is compiled, the code can run on any Java-supporting platform. Hence, the uniqueness lies in portability; it is one of the incredible facets of the language. The Java virtual machine (JVM) tool allows computers to run Java programs and other language programs that compiled to Java bytecode. Looking at the recent developments in Java, there have been two incredible improvements: Lambda support (to reduce verbosity) and REPL support. Hence, it is a preferred language for budding data scientists.


Scala has a large UI. At first, it was intended to run on Java only. Now, every platform that supports Java can likewise run Scala. It is easy to use and built to be changed according to the requirements of the users. It is perfect to code high-level algorithms. 


Structured Query Language (SQL) is utilized to manage vast databases. Specifically, it is useful to manage structured data. Data scientists can add language skill to their skillset by learning SQL. The downside of this language is the lack of portability. 


Julia was created to address all the numerical and computational requirements, making it a perfect choice for data scientists. The exceptional feature of this language is the library is useful for floating point calculations and linear algebra.

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