What are the Top Data Science Programming Languages this Year?

When starting in the field of Data Science, being knowledgeable in different programming languages is of massive importance. All problems that an organisation faces cannot be solved with a single language. The demand for languages such as Python started in the 2010s and from there the demand for the use of multiple languages has increased.


"The study and work in data science is not the hype of recent years." Data analytics provides access to non-obvious insights and they can be used for any purpose. Because of this specialists in Data Science are thought to be in one of the most promising careers of the coming decades.



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The following languages can be considered some of the most important in the world of Data Science:


1. Python

Python can be considered the best language to enter the world of Data Science and it is not restricted to the analysis of data alone. It is considered a universal programming language that enables users to create any project. It is also a great option for beginners as it is intuitive and clear. A total of 66% of Data Scientists are using Python every day and a total 84% of those Data Scientists say that they use Python as their primary programming language.


2. Java

Java is a widely used high-performance backend Programming Language. Therefore, it is a great choice for writing algorithms in Machine Learning. It is also considered to be widely applicable for IoT and Big Data. Java is very attentive to security, which is an extremely important advantage when working with sensitive data.


3. Scala

Scala is a combination of functional and object-oriented programming language. Because of this, Scala is one of the most suitable programming languages for Big Data. One of the most impressive features of Scala is its capabilities to work on parallel processes when running big data arrays. Another good point of Scala is that it is created so that data science users can work on specific operations.


4. R

R is not only a programming language but a complete platform for statistical analysis. It allows its users to work on operations that include mathematical modelling and data processing. R is one of the most important programming languages in the field of Data Science, there are more than 2 million users worldwide of the R language and is used by more than 70% of the total data miners.


5. SQL

SQL (Structured Query Language) is a very important tool when working with a huge amount of Big Data. One of the most crucial advantages is standardisation. It provides direct access to data which is possible due to its high speed. Other advantages of SQL are the flexibility and simplicity of the technology. Primarily, SQL is applied for data management in applications both offline and online. Meaning that the efficiency of SQL for Data Science is dependent on the project specifications.





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