Highest Paying Programming Languages to Learn in 2024

In the rapidly evolving world of technology, mastering high paying programming languages is the key to unlocking lucrative career opportunities. Here are some of the high paying Programming Languages: 

Programming skills are essential for many different kinds of job roles in various sectors. Whether you are developing applications or websites, you will always need one or the other programming language. Data science and machine learning are two other domains that heavily depend on languages like Python, R, or Scala.

Knowing languages, such as Python opens up doors for you both as a data scientist and an application developer. However, if you are into web development, a language like JavaScript should be your first choice. Knowing popular programming languages also helps you get offered more lucrative salary packages.

Top High Paying Programming Languages and Their Uses

Let us learn about the top programming languages in the world as well as the various advantages and disadvantages they have. We will also check out some of their important use cases and applications.

1. Python

Renowned for its simplicity and versatility, Python continues to top the charts. With a vast array of applications, including data science, AI, and web development, Python developers are highly sought after.

Python is a general-purpose interpreted programming language that prioritizes code readability and flexibility above everything else. It is a high-level language and is based on the OOP (Object-oriented Programming) approach. Python allows easy scalability and has the capacity to support both small- and large-scale projects. It is one of the most high paying programing language.

The dynamically-typed language also supports functional, procedural, and other structured programming approaches. Python is extremely extendable with its huge set of libraries. While being easy to use, it also comes ready with various features with just its standard library.

There are features, such as Unicode support, garbage collection, and reference counting but the newer Python is not backwards-compatible with older versions like Python 0.9.0 or Python 2.0. After becoming a full-stack Python developer, you can build end-to-end artificial intelligence or AI-driven applications and deploy them on a server.

Python is one of the most preferred languages for data science and applied machine learning. The syntax in Python is quite close to English and thus, it makes it incredibly easy to use even for advanced scientific tasks and AI-driven implementations.

Python is free to use and is open-sourced. A certificate program, such as Hero Vired’s Integrated Program in Data Science, Machine Learning, and Artificial Intelligence can help you learn Python for data science. The program will also enable you to learn machine learning with Python.

Important Use Cases and Applications:

• Python is used for training AI with the help of machine learning and deep learning methodologies. There are platforms such as Keras and Tensorflow which make it even easier to develop AI projects with Python.
• Due to the availability of libraries such as Pandas, Matplotlib, Scikit-Learn, SciPy, and NumPy, Python can be used for numerous data science and scientific computing applications.
• Python can be also used for programming standard applications, however, the language is better suited for developing systems or administrative tools.
• Even though Python is not used much in mobile application development, it is used as a support language for version control, build management, and unit testing.

Advantages:

• Python offers an extensive library that allows users to easily use pre-built code for databases, web browsers, document generation, and many different tasks.
• The programming language is extremely easy to learn with syntax that is the closest to English which we use for daily communication.
• Python is extensible and you can use C++ or C to write code in some cases.
• Code from Python is also embeddable and can be added to the source code of other languages like C++.
• Data science using Python is simple and intuitive.
• The language requires less coding than alternatives, such as Java.
• Python welcomes IoT with platforms like Raspberry Pi and other smart devices.
• Python is extremely portable and code developed in Python can be run across any platform (WORA or Write Once Run Anywhere).
• Debugging is easy with Python as each statement is executed one by one.

Disadvantages:

• Python does take more time to run code as every statement is individually executed line by line.
• The language is excellent as a server-side language but is barely used for frontend or client-side applications.
• Python is not that great for mobile application development and browsers.
• There are some design restrictions in Python due to being dynamically typed and using duck-typing.
• Python is sometimes not as powerful as low-level languages, such as C++.
• Python’s database access layers are still not as sophisticated as ODBC (Open DataBase Connectivity) or JDBC (Java DataBase Connectivity).

2. JavaScript

As the backbone of web development, JavaScript remains a staple in the tech industry. With the surge in demand for web-based applications, skilled JavaScript developers enjoy attractive pay packages.

JavaScript is a high-level programming language used for both backend and frontend development. It is both a server- and client-side programming language, making it the perfect choice for web developers and even mobile application development. Most websites use JavaScript and JavaScript-based frameworks.

Making JavaScript even more useful and preferred by developers around the world are frameworks, such as Node.js, Ember.Js, React.js, Angular, and Vue.js. These frameworks make it easier for programmers to build web and native applications with ease and in an intuitive manner. Ember, React, and Node are important components of the MERN stack, one of the most popular technology stacks for full-stack developers.

A full-stack JavaScript developer generally relies on only JavaScript but is still able to build end-to-end services, such as social media platforms or messaging applications with only JS-based frameworks. It is one of the highest paying programing language.

A full-stack web developer course like Hero Vired’s Certificate Program in Full Stack Development with Cloud for Web and Mobile can help you learn the MERN stack. Due to the internet, JavaScript is one of the most preferred programming languages for web developers. With the help of NativeScript, you can even use JavaScript along with native APIs to develop applications for iOS or Android.

JavaScript is mostly a just-in-time programming language that has dynamic typing. Like other good scripting languages, it is a multi-paradigm language that supports both imperative and functional programming styles. TypeScript is an alternative scripting language to JavaScript and has similar libraries, such as Angular instead of AngularJS.

Important Use Cases and Applications:

• JavaScript is used for Android and iOS application development.
• The scripting language is widely used alongside various frameworks for building all kinds of websites and web applications.
• JavaScript has recently also started being used as a server-side language a lot more.

Advantages:

• JavaScript is faster in compilation than languages like Java.
• The language allows the client-side to get connected to the server extremely fast.
• JavaScript has multiple top frameworks, such as NodeJS that allow one to become a backend developer.
• It is also cheaper to develop dynamic content with JavaScript due to its simplistic structure and availability of pre-defined code and features.
• JavaScript can function with other programming languages seamlessly, thus allowing programmers to embed JavaScript code anywhere.
• It offers faster data validation on the browser and rich interfaces for developers to build dynamic web pages.

Disadvantages:

• JavaScript cannot assure client-side security as code is inspectable by end-users.
• Different browsers interpret JavaScript differently, however, this can be avoided using frameworks as the pages will get optimized.
• Debugging is not as effective as in editors for languages, such as C and C++.
• JavaScript only supports single inheritances and the scripting language cannot function with multiple inheritances.

3. R

R is a statistical programming language created for statisticians and statistical tasks. The programming language is known for its graphical computing capabilities when it comes to plotting and graphing. It is also one of the most preferred languages for data science along with Python.

Due to being statistically inclined, you can use R for statistical learning and even various advanced AI-driven implementations. R is great for data analysis, statistical modeling, data mining, and many other mathematical functions. It is the most commonly used language for statistical research and database-oriented statistical computing.

RStudio is the most common IDE or integrated development environment for R, however, the official R software environment comes open-sourced inside the GNU package. Like Python, R can also be used in Jupyter Notebook. It is one of the highest paying programing language.

Important Use Cases and Applications:

• R is used extensively for data science and isolated tasks, such as document extraction or database processing.
• The language is great for statistical learning and other applications for enabling AI.
• R is also used in various different kinds of scientific and non-scientific research (like literary research).

Advantages:

• It offers pre-built support for data wrangling.
• R has a huge array of packages (10,000+).
• It allows high-quality graphing and plotting, making the language one of the best for graphics.
• R is extremely compatible with other programming languages such as Python and C++.
• It offers cross-platform support.

Disadvantages:

• R shares its origin with a weak programming language, due to which its base package does not support dynamic graphics.
• The language utilizes more memory as compared to Python.
• R does not have any security features and there are several restrictions with this language.
• The language is harder to learn and is meant for only statisticians.
• R is also much slower than Python or even MATLAB.

4. C and C++

C++ was developed as an extension to C, another extremely popular and powerful programming language. Modern C++ is much more advanced than older versions and now can be considered to be a multi-paradigm language (object-oriented, functional, generic, etc.). It is arguable if the language is a low- or mid-level language, however, it has enormous capabilities in working with system architecture, networks, and operating systems.

The current versions of C++ also offer low-level memory manipulation. It is a compiled language and almost every top firm, such as IBM, Microsoft, Intel, or Oracle has its dedicated C++ compilers. C++ is built to cater to the development of powerful software and architecture of servers, databases, and even games.

C is also a general-purpose language like C++ and is quite close to machine language, making it one of the preferred choices for architectural programming or working with networks and operating systems. It supports structured programming and is statically typed. C provides self-optimizing constructs, recursion, and lexical variable scope.

Important Use Cases and Applications:

• C++ is used to make desktop software and applications (Even C is still in use).
• The language is used to make operating systems, such as macOS.
• Browsers like Mozilla Firefox have been built using C++.
• C++ can also be used alongside game engines to build games.

Advantages:

• C++ is portable and works across multiple operating systems.
• The language supports concepts, such as polymorphism, encapsulation, inheritance, classes, and other OOP features.
• C++ offers total control over memory management as many developers consider this to be incredibly beneficial.
• There is low-level memory manipulation due to being closely related to machine language.
• C++ is extremely compatible with C, which has been used for many powerful applications and software as well.
• C++ programs are also known for being extremely scalable.

Disadvantages:

• There is an absence of an automatic garbage collector in C++.
• Using pointers is relatively hard for some programmers and it also uses up more memory.
• C++ does not have support for built-in threads.

5. Java

Java’s robustness and widespread adoption in enterprise-level projects ensure its consistent demand. Skilled Java programmers are rewarded with competitive salaries.

Java is a traditional programming language for developers that is object-oriented. Java has been in use for the last few decades for developing various mobile applications, desktop software, and even applets inside web environments. It is a class-based high-level programming language.

Java runs on any platform that supports it without the need for recompilation. Similarly, Java applications can run on any JVM or Java Virtual Machine. Java shares some syntax with C++ and C but does not have as many low-level facilities as the two low-level languages. It is a general-purpose language following the WORA principle of writing once and compiling anywhere.

Java was one of the most used programming languages of all time and based on estimates, it will continue to be in use for a long time. Java has no similarities with JavaScript and these two are completely different languages.

Important Use Cases and Applications:

• Java is used alongside operating systems to provide a runtime environment for Java applications.
• The language had been used to develop many popular software and games.
• Java is used across multiple systems to support many platform architectures.

Advantages:

• Java promotes standard programs and code reusability.
• Java is platform-independent.

Disadvantages:

• There are different GUI or graphical user interfaces across various platforms.
• Memory management in Java is not as great as C++ or C.

6. Scala

Scala is a popular general-purpose programming language for data science and machine learning. It is a statically-typed multi-paradigm language supporting both OOP and functional programming. Scala was developed to be compact, portable, and concise. It is one of the highest paying programing language.

Important Use Cases and Applications:

• Scala is used for various platforms that fetch data from servers and conduct AI-driven functions before being rendered on the client side.
• The language is used extensively for data science.

Advantages:

• Scala has excellent IDEs available for it.
• The language is extremely fun to use while being highly functional at the same time.

Disadvantages:

• Scala is a hybrid between object-oriented programming and functional programming approaches, thus making type information sometimes harder to understand.
• Scala has a very small developer community compared to Python.

7. Ruby

Ruby is another high-level programming language that is useful for both data science and web development. It is an interpreted multi-paradigm language that focuses on simplicity and utility. Ruby considers everything including legacy data types to be objects, thus making it easy for developers to work with various data structures and origins.

Important Use Cases and Applications:

• Ruby on Rails, a framework for Ruby, it is extremely useful for web development.
• Ruby can be used as a stand-alone programming language for data science.

Advantages:

• Rails is a full-stack framework that allows you to develop full web applications on your own.
• Ruby can be used as both a frontend and backend language with the Rails framework.

Disadvantages:

• Ruby is known to be quite slow during runtime as compared to some other programming languages.
• It has a slow boot speed.

8. Kotlin

Kotlin is a programming language built for interoperability with Java. It is also statically typed and can be used as a cross-platform programming language. Due to this, one can make native Android applications with the help of Kotlin.

Important Use Cases and Applications:

• Developers can use Kotlin for building Android applications.
• Kotlin can also be used as an alternative for Java.

Advantages:

• Kotlin’s syntax is more concise due to type inference.
• It is known for its easy maintainability.

Disadvantages:

• Kotlin is sometimes more complex to use due to fluctuations during compilation.

9. Go

Go is a statically-typed programming language initially developed at Google. This compiled language is quite similar to C and offers garbage collection, concurrency, memory management, and structural typing. Go programing language, or Golang, has gained momentum due to its efficiency in building scalable systems. As cloud computing and distributed systems become dominant, Go developers are handsomely compensated.

Important Use Cases and Applications:

• Go is being used for various web applications and websites.
• The language is also used as an alternative to C for server setup and networking.

Advantages:

• Go is lightweight.
• The programming language offers memory safety and garbage collection.

Disadvantages:

• Go is not effective for error handling.
• It has extremely hard dependency management.

10. Ruby on Rails:

Being a high paying language this elegant and developer-friendly framework is preferred for building web applications. Its demand continues to rise, making Ruby on Rails developers a well-paid group.

But what are the factors influencing language demand and salaries?

Factors Influencing Language Demand and Salaries

Several factors contribute to the demand and salaries of programming languages.

Market trends, industry demand, and the prevalence of specific languages in emerging technologies influence their worth. 

Additionally, languages with niche applications or high complexity often command higher compensation. 

Read more about DevOps & Cloud Engineering and Programming Languages Every Developer Should Learn.

• Python: A Python developer earns Rs 4,65,827 per annum on average.
• JavaScript: A JavaScript developer earns around Rs 6,17,578 per annum.
• R: A data analyst with R skills earns Rs 5.07,937 per annum (average).
• C++: A C++ developer earns Rs 5,12,517 per annum on average.
• C: The average salary earned by a C developer is Rs 5,73,861 per annum.
• Java: A Java developer earns around Rs 4,80,000 per annum.
• Scala: The average salary bagged by a Scala developer is Rs 7,25,739 per annum.
• Ruby: A Ruby developer earns Rs 9,68,747 per annum on average.
• Kotlin: A Kotlin developer earns Rs 7,20,000 per annum (average).

Source: Glassdoor