Top 10 Programming Languages for Data Science 

Top 10 Programming Languages for Data Science 

Coding is (and will continue to be) a hot skill in the rapidly expanding cyberspace of the twenty-first century. Coding is an important skill in the toolbox of a data scientist for custom analysis and data visualisation. According to a CrowdFlower report conducted by Packt Publishing Ltd. to identify the top money-making skills in data science and BI, the top five programming languages are Python, SQL, R, Java, and JavaScript. The top programming languages for data science listed in this article were chosen based on their popularity in the data science community rather than any specific criteria. Whether you are a novice or a seasoned data scientist, learning all of these programming languages is a good way to gain a solid understanding of all data science concepts. 

• Java 

Oracle’s Java has emerged as the programming language of choice for developers all over the world. The most significant advantage is that once compiled, it can be executed across platforms, eliminating the need for language-specific compilers. Java is also at the top of the popularity charts on tech websites like Mashable and ITworld. 

Java’s unwavering popularity is also evidenced by the fact that it was named the most in-demand programming language on Wall Street in 2015 by a financial career survey. 

• C

Denis Ritchie’s indelible contribution to the world. C has a solidity to it that has kept it relevant 43 years after its introduction. C++, Java, JavaScript, C#, Objective-C, PHP, Python, Perl, and many other modern programming languages have been inherited in some way from C. It is gradually losing its direct application in today’s environment. But if you are looking for a language that will prepare you to feel comfortable learning any subsequent programming language, this is a great place to start. 

It has a wide range of applications in our daily lives, including software (MS-Word), embedded systems of electronic devices such as car dashboards, television firmware, aeroplane applications, and so on. This is one of the reasons C is still used as an introductory language in undergraduate computer/electrical/electronic engineering courses. 

• Scala 

Scala is a fusion of object-oriented and functional programming languages that aid in the development of robust and scalable data science applications. Scala programming language assists data scientists in writing short and expressive code while delivering high-performance and type-safe applications that are impressive and valuable as organisations strive to work with an increasing amount of real-time data. It for data science necessitates a little more abstraction and thinking ability. However, once a data scientist becomes acquainted with its high-level functional features, productivity skyrockets. Scala’s scalability and number crunching capabilities have elevated it to the ranks of the best programming languages for data science. 

• Python 

Python’s unique selling point is its readability and compactness. It allows programmers to express the same ideas in shorter code chunks. Python is easy to learn for developers with diverse programming backgrounds and experience (object-oriented, imperative, functional, procedural). It enables easier scalability, making it suitable for both small-scale and large-scale applications.

Python is used to create modern applications such as Pinterest and Instagram. It is rapidly gaining academic traction and is now one of the most commonly taught programming languages in schools. Enroll in the best Python training institute in Delhi to become a python expert.

• SAS

SAS (Statistical Analysis System) is the commercial analytics market leader, with the largest share in private organisations. It has gained popularity in the data science community due to its extensive statistical functions and user-friendly GUI, which allows data scientists to learn quickly. 

SAS is an easy-to-learn programming language for people who already know SQL. Because it provides simple options such as PROC SQL. In terms of job trends, SAS holds approximately 70% of the market share, compared to Python and R programming languages, which each hold 20% of the market share. 

• R Programming Language 

R is a significant departure from the languages we’ve discussed thus far. It is not a replacement for any of the languages we’ve already covered. R is essentially a statistical computing and graphics language. Given the way data is generated in the twenty-first century. R has become the preferred language for data analysts and scientists all over the world. R was ranked No. 6 in the IEEE’s Top 10 Programming Languages of 2015, and with the growing influence of Big Data and the emergence of the Internet of Things, you can be confident that it will remain a hot skill for many years to come, and beyond. 

• MATLAB 

MATLAB is a must-know programming language for data scientists, especially when working with matrices. Although MATLAB is not an open-source language. It is widely used in academic courses due to its suitability for mathematical modelling and data acquisition. The MATLAB programming language is useful for data science tasks such as linear algebraic computations, simulations, and matrix computations. The LAPACK and BLAS libraries for matrix multiplication in MATLAB have been highly optimised to improve performance. However, MATLAB places limitations on code portability (the ability to run code on another computer).

• JavaScript 

In the twenty-first century, the line between a website and a web application is becoming increasingly blurred. Dynamic, interactive websites and web pages are no longer the exceptions. For optimal bandwidth utilisation, a significant portion of scripts (particularly those related to validations) must be run on the client-side rather than clogging the bandwidth by sending the inputs to the server, executing the scripts, and transferring the results back to the client – thereby adding to the traffic. JavaScript, which is unrelated to the programming language Java, allows developers to run client-side scripts, interact with users in real-time, control the browser, and communicate asynchronously with the server. 

• GO

GO is a newcomer to the world of data science, but its popularity is growing due to its ease of use. Golang is an open-source language based on C. It was created at Google by a group of engineers who were frustrated with the use of C++. GO was not designed specifically for statistical computing, but it has gained widespread acceptance for data programming due to its speed and familiarity. Data scientists can use routine programmes written in other programming languages. Such as Python, to make function calls that the system cannot handle. 

• SQL 

For decades, Structured Query Language (SQL) has been at the heart of data storage and retrieval. It is used to extract useful information from a sea of data. It’s difficult to imagine a modern application that doesn’t use a database to store massive amounts of data on the backend. SQL is a programming language that allows you to interact with a database. When used with sufficient expertise, SQL can significantly reduce the turnaround time for online requests and queries by extracting and processing only the relevant portion of data rather than entire database tables. Microsoft and Oracle have their versions, but conceptually, teaching one should be the same as learning the other. 

This list of ten data science programming languages is not intended to be exhaustive or all-inclusive. While compiling the list, we used a beginner’s frame of mind as a reference point. And we attempted to come up with a list of 10 elements. That would provide a beginner with the necessary depth and breadth for developing big data. If you want to take Data Science Training courses in Delhi, you can apply to Uncodemy TechStack, ExcelR, or Madrid Software Training etc.