programming languages for data science

Top programming languages for data science language to learn

A programming language is considered a lawful language containing a set of instructions, commands, and syntax that is used to produce software programs. These languages are used in computer programs to implement algorithms and have multiple applications. There are various types of programming languages available for data science. Python Assignment help Data scientists need to study and master one language because it is necessary to recognize different data science functions. Below we have discussed some of the important programming languages for data science:-


Python is considered the best programming language for data science because it offers various features like data modeling, analytical capacities, and easy readability. Another explanation for Python’s immense popularity in data science is its robust support for data science and analytics from the library. Many Python libraries provide a variety of data processing and analysis features, techniques, and processes. These libraries focus on image and text content processing libraries, data analysis, neural networks, data visualization, and so on.

For instance, Pandas is a great library of Python data processing and data

handling tools, NumPy for numerical computation, SciPy for numerical computation, Matplotlib for data visualization.


When it comes to Data Science, it is undesirable not to address R. we can say that R is considered as the best language for Data Science because statisticians develop this language for statisticians! It is still very popular with an involved community and many widely accessible cutting-edge libraries (despite having strong competition from Python!). Numerous R libraries include a variety of data collection and analysis features, techniques, and processes. These libraries focus on image and text content retrieval libraries, data editing, simulation of data, web crawling, artificial intelligence, and so on. For e.g., dplyr is a very common library for data manipulation, and ggplot2 is a visual data library.


SQL stands for Structured Query Language, it is a language primarily created for the storage and extraction of data contained in the management system of the relational database. For data science, this language is incredibly important since it mainly deals with data. Data scientists’ key task is to turn the data into actionable observations, so when necessary, they need SQL to extract the data to and from the database. Many common SQL databases, such as SQLite, MySQL, Postgres, Oracle, and Microsoft SQL Server, can be used by data scientists. In specific, BigQuery is a data warehouse capable of handling

data processing over petabytes of data and enabling super-fast SQL queries.


MATLAB is a very common mathematical operation programming language that inevitably makes it essential for Data Science. And that’s because Data Science struggles with mathematics a lot as well. MATLAB is so famous Because it facilitates computational models, image processing, and statistical analysis. it consists of various mathematical functions for linear algebra, statistics, optimization, filtering, differential equations, Fourier analysis, numerical integration, etc., that are helpful in data science. Concerning all of this, MATLAB offers built-in graphics that can be used to construct data visualizations with several graphs.


Java is considered the oldest programming language, which is quite significant in data science as well. There are several data science tools written in Java, such as Hadoop, Spark, and Hive. Since Hadoop is operating on a Java virtual machine, it is essential to understand Java when using Hadoop. In addition, several data science libraries and software are also present in Java, such as Weka, Java-ML, Deeplearning4j, etc.


Scala is considered a Java extension programming language created on the Java Virtual Machine (JVM). But it can interact with Java quickly. The main reason Scala is so good for Data Science is that it can be used to handle vast volumes of data along with Apache Spark. Scala is the go-to language when the user needs to handle the data. Several data science frameworks developed on top of Hadoop work on Scala or Java because they are written in certain languages. There is one drawback of Scala that it is difficult to learn, and there weren’t many more support groups for the online community as it is a specialized language.


As compared to some other programming languages, Perl can perform data requests very easily because it uses compact arrays that do not require a high degree of attention from the programmer. It is also very identical to Python, so in Data Science, it is a valuable programming language. Perl 6 is currently regarded as the ‘big-data lite’ with several other big corporations working with it for Data Science, such as Boeing, Siemens, etc. In quantitative areas such as economics, bioinformatics, mathematical analysis, etc., Perl is also useful.


Here in this article, we have listed the best programming languages which can be used for data science. Every programming languages has its own significance and there is not any particular language that can be termed a “correct language” for Data Science

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