Best Data Science Programming Languages in 2024

The demand for a data scientist is very high in every company that growing continuously. Data scientists help you to analyze your company data and also need to access that data for the growth of your company business. As well as data scientists need all the right resources and the best set of skills that allow them to achieve the best results with your data. As per the IBM report, the requirement for Data scientists will be increased up to 28 per cent by this year 2024 and more than 3 million job opportunities for data science specialists. Data science is a process that helps to analyze statistics, data processing, and their associated methods to identify and evaluate their actual data.

Data science includes theory and methods developed from a wide range of fields, including statistics, information science computer science, and Mathematical. Data science is increasing more importance with the development of computer learning. if you become a data analyst or data scientist, then you need to learn a minimum of one of the given programming languages. Data science is also an exciting field in which quantitative and mathematical skills and technical computational skills are combined with programming abilities. 

Data Science is also one of the most popular fields in which the need for production and improved performance outcomes is increasing. This article would cover a few of the top programming languages.

List of Programming Languages for Data Science

In this Section, we would bring you all attention to the most commonly used programming languages all the developers for Data Science. You may already be familiar with some of the common programming languages, and Here some given Language is totally new for your Users.

Python

Python is a very Popular Programming Language and Python is a very Simple Syntax free language as compared to other Programming Languages.

Developers mostly used this Programming Language in the Data Science field and Machine Learning.

Python language has an essential role among the best tools for data science and Python is also the popular alternative for a variety of activities in fields such as Computer Learning, learning techniques, Artificial Intelligence, Machine Learning, and many more. It’s indeed object-oriented, simple to use, and incredibly easy to configure due to its high usability of code.

Python Programming language’s large community of libraries and multi-purpose applications make it a truly multi-faceted choice. Many main features supported by Python language include:

• Python Support some very popular data science libraries like Scikit-Learn, Keras, TensorFlow, Matplotlib, etc.
• Python Language is Specifically designed for activities such as data processing, analysis, simulation and visualization, and Modelling.
• Python also Supports several options for the export and exchange of data
• Python Comes with a large community to get help & Support.

JavaScript 

JavaScript is multi-paradigm and also event-driven scripting & Programming language and this JavaScript is one of the top leading programming languages, Which is used in web development and this Scripting Language is used mostly all the Developer & Web Developer. With the help of JavaScript, developers can build beautiful and immersive websites, and JavaScript properties help developers to make it a perfect option for making amazing visualization.

A lot of developer uses this JavaScript for Data Science like handling repetitive tasks and processing real-time information & data. A number of valid reasons for JavaScript Scripting Language are:

• JavaScript Allows the development of visualization techniques for data processing.
• JavaScript Supports numerous modern Machine Learning libraries, such as TensorFlow.js, ConvNetJs, Brain.js, Meachinelearn.js, Math.js, Keras.js, and many more.
• This language is very simple to understand, learn & used scripting Language.

List of some JavaScript Libraries used for Data Science:

D3.js:

D3.js is a famous JavaScript library that is used for accessing data used for web standards. D3.js helps developers to bring their data back into existence by using Canvas, HTML, and SVG. ii dD3.js is a very powerful visualization tool and design tool with a data approach DOM management and it allowing you the full capability of browser plugins and the ability to develop the perfect user interface(UI) for your outcome.

TensorFlow.js:

TensorFlow.js is another JavaScript Library and this is also open-source and freely available JavaScript library and this is used for the Execution and deployment of machines learning and Artificial Intelligence models.

Math.js:

Math.js is also an open-source JavaScript and Node.js library. This features an extensive-expression interpreter with help for mathematical programming, It comes with a wide range of built functions and parameters, and provides a rise significantly for dealing with various data types such as numbers, real numbers, percentages, fractions, and matrices.

Java

Java is another programming language for Data Science, and developers used this Java language for Desktop and Android applications. Any of the largest corporations have long used that as their main development application of preference for secure development. Java has provided platforms like Hadoop, Hive, Spark, Scala, and Fink for Data Science.

JVM stands for Java Virtual Machine and JVM is a common alternative for developers to writing code for integrated systems, data mining, and deep learning in a business environment. Some main advantages provided by Java are:

• Java offers so many IDE for RAD (Rapid Application Development).
• Java is used in Data Analysis, Natural Language Processing, Deep Learning, Data Mining, and also more.
• Java allows flawless scaling to create large applications that run.
• Java helps to deliver fast results.

R Language

R is another programming Language and an open-source software environment and specifically, it is developed to manage the mathematical and graphical aspects of data science. Clustering, Time series data, quantitative testing, and some linear and non-linear modelling are only some of the various predictive computing and data analytic options given by R. And Third-party applications such as Jupyter and RStudio that allow interaction with R. R Programming Language provide excellent flexibility, It also allowing other programming languages to change data structures in R language without much more effort, due to its solid object-oriented design. There are some other advantages of the R programming language are:

• R Programming Language offers effective data processing and advanced tools for data analysis.
• R language provides a wide variety of options for developing outstanding data analysis charts.
• R is also Allows the application of essential elements to reliable community-built modules.
• Requires an effective contributor network.

C/C++

C Programming Language is a very old language, and a lot of the new programming languages that used C / C++ as their source code, like R. C / C++ provide a strong knowledge of the principles of programming. While C / C++ is one of the most difficult languages for Data Science new learners and due to its low-level complexity, it’s also progressively being used to create applications that user can are be using for Data Science.

• C/C++ is the ability to produce better, stronger-optimized outcomes while the relevant algorithms are often written in C/C++.
• C/C++ is Comparatively fastest compared to programming languages because of its powerful features.

MATLAB

MATLAB is a mathematical programming environment developed to do advanced mathematical expressions and MATLAB deals with a range of software that helps you perform tasks like matrix creation, data and feature visualization, and many more. With the help of this MATLAB language, users can quickly solve the most complex mathematical or statistical questions or Problems. It is commonly used in universities for the learning of linear algebra analysis or mathematical methods. There are some more benefits of MATLAB Programming Language are:

• MATLAB allows the development of an algorithm and UI Development 
• Its Comes with an efficient variety of mathematical features
• MATLAB offers in-built design for developing and visualizing essential data.
• It allows easy usability features

Scala

Scala is another very high-level programming language, which works on the JVM (Java Virtual Machine) and It makes interacting with the Java language easy. Scala language can be used efficiently with Sparks to manage large quantities of data. Its underlying support for interoperability gives Scala a good option for developing high-performance and very effective data science frameworks, like Hadoop. There are some other features of this Programming language are:

• Is reliable, Scalable and It can be Delivering outcomes and it much fastest in certain cases.
• It comes with more than 170000 libraries to expand Scala functionality.
• Scala is Supported on different IDEs, like IntelliJ IDEA, Vim, Atom, VS Code, Sublime Text, and many more.
• Provides an excellent community support Environment.

Julia 

Julia Language is a progressively largely defined and multipurpose and usable programming language that provides an effective solution for the mathematical solution and quantitative scientific study. This Julia language is used as a high-level programming language, and if required then Julia is also used as a low-level programming language. Julia Language has been used in a lot of big companies for performing different business tasks, like risk analysis, space mission planning, and time-series analysis. Some other notable characteristics of Julia are:

• It helps in providing a good result 
• It supports in-built package management 
• Julia offers data analysis, highly complex data set processes, and powerful Deep Learning methods.
• It also Helps of parallel processing & computing

Conclusion

In this article, we covered a few Top listed programming languages for Data Science. These languages do have their advantages, and it also provides better and fast outcomes as compared to others. This same domain of data science is extremely High and It can often require a different range of tools for specific activities or Tasks. If you are becoming a Data Scientist, then you should need to start to learn the programming languages listed above, since they are currently the most famous on-demand languages.