Popular programming languages, industries, job roles, and salary trends 2024

Here are the 14 most popular programming languages as of 2024, with deeper insights into their history, usage, platforms, industries, and how they work:

1. Python

• History: Created by Guido van Rossum and released in 1991, Python was designed with readability and simplicity in mind. It emphasizes code readability using significant whitespace.
• Usage: Used in data science, machine learning, automation, web development, and scripting. Libraries like TensorFlow, Pandas, and Django have expanded its reach.
• Platforms: Cross-platform; works on Windows, macOS, Linux.
• Industry: Data analytics, AI/ML, web apps (e.g., Django, Flask), automation, scientific computing.
• How it Works: Python is an interpreted language, running on a Python virtual machine (CPython) with dynamic typing, which allows for flexible coding but can be slower than compiled languages.

2. JavaScript

• History: Created by Brendan Eich in 1995 during his time at Netscape, JavaScript was initially developed in 10 days to add interactivity to websites.
• Usage: Core to web development for building interactive websites, both on the client-side (with frameworks like React and Vue.js) and server-side (Node.js).
• Platforms: Runs in all web browsers; also used on servers, mobile apps (React Native), and desktop apps (Electron).
• Industry: Web development, mobile app development, game development (browser-based games).
• How it Works: JavaScript is an interpreted, event-driven language that runs in the browser’s engine (e.g., V8 in Chrome) or Node.js runtime, with a focus on asynchronous operations (e.g., event loops, promises, async/await).

3. Java

• History: Originally developed by James Gosling at Sun Microsystems and released in 1995, Java’s motto is “write once, run anywhere,” due to its portability via the Java Virtual Machine (JVM).
• Usage: Enterprise-level backend systems, Android app development, large-scale web applications.
• Platforms: Cross-platform via the JVM; used on servers, desktops, Android devices.
• Industry: Banking, finance, big data, Android apps, enterprise software.
• How it Works: Java is a statically-typed, compiled language that runs on the JVM. It emphasizes object-oriented programming (OOP) principles and memory management via garbage collection, which reduces memory leaks.

4. C#

• History: Developed by Microsoft and released in 2000 as part of its .NET initiative, C# was designed to rival Java and cater to enterprise needs within the Windows ecosystem.
• Usage: Enterprise software, Windows applications, game development (Unity), web apps with ASP.NET.
• Platforms: Cross-platform support with .NET Core; Windows, web servers, mobile (Xamarin), and game consoles.
• Industry: Game development, enterprise apps, web apps, cloud computing (Azure).
• How it Works: C# is a compiled, statically-typed language that supports multiple paradigms (OOP, functional programming). It runs on the Common Language Runtime (CLR) in .NET, which manages memory and execution.

5. C++

• History: An extension of the C programming language, C++ was created by Bjarne Stroustrup in 1985 to add object-oriented features to C.
• Usage: System software, game engines, performance-critical applications (e.g., Adobe products, high-frequency trading platforms).
• Platforms: Cross-platform; used on Windows, macOS, Linux, and embedded systems.
• Industry: Gaming, OS development (Windows, macOS components), aerospace, automotive, and high-performance computing.
• How it Works: C++ is a statically-typed, compiled language with a focus on performance. It provides direct memory access and control, allowing for manual memory management (e.g., pointers), which is crucial for systems programming.

6. Go (Golang)

• History: Created at Google by Robert Griesemer, Rob Pike, and Ken Thompson in 2009 to solve problems of scalability and concurrency in Google’s infrastructure.
• Usage: Cloud services, distributed systems, microservices, networking, and backend systems.
• Platforms: Cross-platform; runs on major cloud platforms (AWS, Google Cloud, Kubernetes).
• Industry: Cloud computing, DevOps, containerization (e.g., Docker), backend development.
• How it Works: Go is a statically-typed, compiled language that emphasizes simplicity and concurrency. Its goroutines enable efficient, lightweight thread management, which is ideal for scalable and concurrent applications.

7. Rust

• History: Developed by Mozilla and first released in 2010, Rust was designed to improve memory safety and concurrency while maintaining performance similar to C++.
• Usage: System-level programming, embedded systems, browser engines (e.g., Firefox’s Servo), and blockchain.
• Platforms: Cross-platform; focuses on systems and embedded environments.
• Industry: Browser development, blockchain (e.g., Solana), embedded devices, security-critical systems.
• How it Works: Rust is a statically-typed, compiled language that enforces memory safety at compile time without the need for a garbage collector. It uses a unique ownership system to manage memory and concurrency safely.

8. TypeScript

• History: Developed by Microsoft and first released in 2012, TypeScript adds static typing to JavaScript to address scalability in large applications.
• Usage: Large-scale web applications, frontend development, and Node.js-based server applications.
• Platforms: Cross-platform; used in the browser (transpiled to JavaScript), server (Node.js), and web frameworks (Angular).
• Industry: Web development, enterprise-level applications, full-stack development.
• How it Works: TypeScript is a superset of JavaScript that compiles to plain JavaScript. Its static typing helps catch errors at compile time, improving maintainability in larger codebases.

9. Kotlin

• History: Developed by JetBrains and officially released in 2011, Kotlin gained prominence when Google announced it as a preferred language for Android development in 2017.
• Usage: Android development, backend development, and cross-platform applications.
• Platforms: JVM-based; used on Android, backend servers, and multiplatform development (Kotlin/Native, Kotlin/JS).
• Industry: Mobile development, backend services, web development.
• How it Works: Kotlin is a statically-typed, compiled language that is fully interoperable with Java. It focuses on conciseness, safety (e.g., null safety), and modern features like coroutines for concurrency.

10. Swift

• History: Developed by Apple and first released in 2014, Swift was designed to replace Objective-C for iOS and macOS development, offering a safer, faster language.
• Usage: iOS and macOS app development, server-side development (via Swift on the server).
• Platforms: iOS, macOS, watchOS, tvOS, and Linux (for server-side Swift).
• Industry: Mobile apps, desktop apps for Apple platforms, server-side apps in Apple ecosystem.
• How it Works: Swift is a statically-typed, compiled language with a focus on safety (e.g., type inference, optionals) and performance. Its memory management is handled through Automatic Reference Counting (ARC).

11. Ruby

• History: Created by Yukihiro “Matz” Matsumoto in 1995, Ruby was designed with simplicity and productivity in mind, focusing on developer happiness.
• Usage: Web development, especially with the Ruby on Rails framework, and automation scripts.
• Platforms: Cross-platform; widely used in web servers and cloud environments.
• Industry: Web development, startups, e-commerce platforms (e.g., Shopify).
• How it Works: Ruby is an interpreted, dynamically-typed language that emphasizes simplicity and flexibility. Its object-oriented design makes it easy to build powerful abstractions.

12. PHP

• History: Created by Rasmus Lerdorf in 1994 as a set of Common Gateway Interface (CGI) scripts, PHP evolved into a full-fledged language for server-side scripting.
• Usage: Server-side web development, powering platforms like WordPress, Drupal, and large-scale CMS and e-commerce systems.
• Platforms: Web servers, primarily within the LAMP stack (Linux, Apache, MySQL, PHP).
• Industry: Web development, e-commerce (e.g., WooCommerce), content management systems.
• How it Works: PHP is an interpreted, dynamically-typed language. It’s embedded directly into HTML and executed on the server, making it a popular choice for dynamic websites.

13. R

• History: R was developed by Ross Ihaka and Robert Gentleman in 1993 as a free software environment for statistical computing and graphics, inspired by the S language.
• Usage: Statistical computing, data analysis, and data visualization in both academia and industry.
• Platforms: Cross-platform; widely used in data science environments.
• Industry: Data science, research, academia, bioinformatics, financial analysis.
• How it Works: R is an interpreted language designed specifically for data manipulation and statistical modeling. It has extensive libraries for visualization and analysis (e.g., ggplot2, dplyr).

14. SQL (Structured Query Language)

• History: Developed in the early 1970s at IBM, SQL has become the standard language for querying and managing relational databases.
• Usage: Managing, querying, and manipulating data in relational databases.
• Platforms: Runs on all major database systems (e.g., MySQL, PostgreSQL, Oracle, SQL Server).
• Industry: Database management, business intelligence, data warehousing, finance, healthcare.
• How it Works: SQL is a declarative language used for interacting with relational databases. It allows users to specify what data they want to retrieve or manipulate, leaving the database engine to determine how to perform the operation efficiently.

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These languages are chosen based on their significant impact on industries, platforms, and ecosystems. Each language continues to evolve as the demands for programming and technology grow.

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Let’s explore key differences between these languages in terms of performance, community, and scalability:

1. Performance

• C++:
  • High performance. Known for its ability to directly manage memory and system resources, which makes it a top choice for high-performance applications like gaming engines, OS development, and real-time systems. However, manual memory management can introduce complexity.
• Rust:
  • High performance. Provides performance close to C++ but with memory safety guaranteed at compile time, reducing risks of memory leaks and data races. Its ownership model makes it safer but can have a steeper learning curve.
• Go:
  • Good performance. Optimized for concurrency and scalable cloud infrastructure with minimal runtime overhead. It compiles quickly and is efficient for tasks involving concurrency, though not as fast as C++ or Rust for system-level tasks.
• Java:
  • Moderate to High performance. While slower than C++ due to JVM overhead, Java performs well for enterprise and Android applications. Its automatic garbage collection eases memory management, but may introduce latency spikes in real-time applications.
• Python:
  • Lower performance. Python is an interpreted language, which generally makes it slower than compiled languages like C++. However, Python’s performance can be improved with C extensions (e.g., NumPy, PyPy), especially in data science and machine learning.
• JavaScript (and TypeScript):
  • Moderate performance. JavaScript engines like V8 (Chrome) are optimized for web performance, but it’s still not on par with native languages. TypeScript compiles down to JavaScript and offers similar performance but better structure in large codebases.

2. Community

• Python:
  • Massive community. Python has one of the largest programming communities, especially in fields like data science, machine learning, and web development. The community is highly supportive with a wealth of libraries and resources.
• JavaScript:
  • Huge community. JavaScript has dominated web development for years, and its ecosystem has grown rapidly, especially with modern frameworks (React, Vue.js, Angular). The community is continuously innovating with tooling and frameworks.
• Java:
  • Large and mature community. Java has been a dominant force in enterprise applications for decades. It has a strong presence in large corporations, Android development, and backend systems.
• Go:
  • Growing community. Go has a rapidly growing community, particularly in cloud infrastructure, DevOps, and microservices. It’s gaining traction in large tech companies that focus on scalable infrastructure (e.g., Kubernetes).
• Rust:
  • Passionate and growing community. Rust’s community is smaller compared to JavaScript or Python but is highly passionate. It has a strong focus on system-level programming, safety, and performance.
• C++:
  • Long-standing community. C++ has been around since the 1980s, and while its community is stable, it has seen less growth compared to newer languages. It remains strong in system software, game development, and performance-critical areas.

3. Scalability

• Go:
  • Highly scalable. Go’s concurrency model (goroutines) is built for scalability, making it a popular choice for cloud infrastructure and distributed systems. It scales very well in terms of both performance and maintainability in large-scale cloud services.
• Java:
  • Very scalable. Java has a long history of being used in large-scale enterprise systems. Its JVM allows applications to scale across multiple environments, and tools like Spring Framework make it easier to build scalable services.
• Python:
  • Moderately scalable. Python is often considered less scalable than Java or Go due to its performance limitations. However, in fields like data science and machine learning, scalability is managed by leveraging Python with scalable infrastructure (e.g., AWS, Google Cloud) and optimized libraries.
• JavaScript (TypeScript):
  • Scalable in web applications. JavaScript scales well for frontend and backend web applications, thanks to frameworks like React, Angular, and Node.js. TypeScript adds additional scalability by enforcing type safety in large codebases, improving maintainability.
• Rust:
  • Scalable for systems programming. Rust scales well for performance-critical applications like blockchain, browser engines, and embedded systems. However, its complexity may be a hurdle in rapidly scaling web or cloud applications.
• C#:
  • Very scalable. C# scales well for enterprise and cloud-based applications, particularly in Microsoft environments. Tools like Azure and .NET Core make it easy to develop, deploy, and scale applications across platforms.

Summary

1. Performance:
   • C++ and Rust lead in performance for system-level applications, while Go excels in concurrent, cloud-based environments. Python and JavaScript trade some performance for flexibility and ease of development.
2. Community:
   • Python and JavaScript have the largest, most active communities, while Rust and Go have passionate, growing user bases. Java and C++ benefit from mature, stable communities with a wealth of historical knowledge.
3. Scalability:
   • Go, Java, and C# are the top choices for building scalable backend and enterprise systems. JavaScript scales well in the web space, while Python scales in specialized areas like data science through optimized libraries.

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Here’s an overview of the industries and job roles in demand for each of these popular programming languages:

1. Python

• Industries:
  • Data science and analytics
  • Artificial Intelligence (AI) and Machine Learning (ML)
  • Web development (Django, Flask)
  • Finance and fintech (for analytics and automation)
  • Academia and research
• Job Roles:
  • Data Scientist
  • Machine Learning Engineer
  • Backend Web Developer
  • Automation Engineer
  • Financial Analyst (using data science techniques)
• Why in Demand: Python’s simplicity and powerful libraries like Pandas, NumPy, and TensorFlow make it a go-to for data-driven industries. It’s widely used in automation and backend web development.

2. JavaScript

• Industries:
  • Web development (frontend and backend)
  • E-commerce (React, Angular for user interfaces)
  • Mobile app development (React Native)
  • Digital media and publishing
  • Gaming (browser-based games)
• Job Roles:
  • Frontend Web Developer
  • Full Stack Developer
  • UI/UX Engineer
  • Mobile App Developer (React Native)
  • Game Developer (browser games)
• Why in Demand: JavaScript powers the web. Its ubiquity in both frontend (web browsers) and backend (Node.js) development makes it indispensable for web-based applications.

3. Java

• Industries:
  • Finance and banking (backend systems)
  • Enterprise software (ERP, CRM)
  • Android app development
  • E-commerce (scalable backend services)
  • Big data (Hadoop, Spark)
• Job Roles:
  • Backend Developer
  • Android Developer
  • Enterprise Application Developer
  • Big Data Engineer
  • DevOps Engineer
• Why in Demand: Java’s scalability, stability, and security features make it a top choice for large enterprises, especially in banking and financial systems. It’s also the primary language for Android development.

4. C#

• Industries:
  • Enterprise software development (Microsoft ecosystems)
  • Game development (Unity engine)
  • Cloud computing (Azure)
  • Healthcare IT (hospital management systems)
  • Financial services
• Job Roles:
  • Enterprise Software Developer
  • Game Developer (Unity)
  • Cloud Engineer (Azure)
  • Full Stack Developer (ASP.NET)
  • Systems Developer
• Why in Demand: C# is integral to Microsoft’s ecosystem and is widely used in enterprise settings. The Unity engine makes it a key language in the gaming industry.

5. C++

• Industries:
  • Game development (AAA game engines)
  • System software (operating systems, compilers)
  • Embedded systems (IoT devices, automotive software)
  • High-frequency trading platforms (finance)
  • Aerospace and defense
• Job Roles:
  • Systems Software Engineer
  • Game Developer
  • Embedded Systems Engineer
  • Quantitative Developer (finance)
  • Robotics Engineer
• Why in Demand: C++ offers high performance and control over system resources, making it crucial in performance-critical industries like gaming, embedded systems, and finance.

6. Go (Golang)

• Industries:
  • Cloud infrastructure (Kubernetes, Docker)
  • Microservices and distributed systems
  • Fintech and payment processing
  • DevOps and SRE (Site Reliability Engineering)
  • Networking and cybersecurity tools
• Job Roles:
  • Cloud Engineer
  • Backend Developer
  • Site Reliability Engineer (SRE)
  • DevOps Engineer
  • Systems Programmer
• Why in Demand: Go’s simplicity, concurrency features, and performance make it ideal for scalable cloud services and infrastructure projects like Kubernetes. It’s popular in companies building microservices architectures.

7. Rust

• Industries:
  • Systems programming (operating systems, browsers)
  • Blockchain technology (e.g., Solana)
  • Embedded systems
  • WebAssembly (high-performance web apps)
  • Security-critical software (cryptography, secure systems)
• Job Roles:
  • Systems Programmer
  • Blockchain Developer
  • Embedded Systems Engineer
  • Security Software Engineer
  • WebAssembly Developer
• Why in Demand: Rust’s memory safety and performance make it a prime choice for systems programming, blockchain development, and security-critical industries.

8. TypeScript

• Industries:
  • Web development (large-scale applications)
  • Enterprise software (frontend frameworks like Angular)
  • Mobile app development (React Native, Ionic)
  • E-commerce (scalable frontend systems)
  • Fintech (secure and scalable frontend solutions)
• Job Roles:
  • Frontend Developer
  • Full Stack Developer
  • UI/UX Engineer
  • Mobile Developer (React Native)
  • Frontend Architect
• Why in Demand: TypeScript’s type safety makes it ideal for large-scale applications where maintainability and reliability are crucial. It’s becoming a standard for modern frontend development.

9. Kotlin

• Industries:
  • Android app development
  • Backend development (Ktor, Spring Boot)
  • Fintech and banking apps
  • Startups and SaaS platforms
  • Cross-platform mobile development (Kotlin Multiplatform)
• Job Roles:
  • Android Developer
  • Backend Developer
  • Mobile App Developer
  • Software Engineer
  • DevOps Engineer
• Why in Demand: Kotlin’s modern syntax and full interoperability with Java make it the preferred language for Android development. It’s also gaining traction in server-side applications.

10. Swift

• Industries:
  • Mobile app development (iOS, watchOS)
  • Software development for Apple ecosystems (macOS, tvOS)
  • Fintech (mobile payment apps)
  • Healthcare (iOS medical apps)
  • Augmented Reality (ARKit, RealityKit)
• Job Roles:
  • iOS Developer
  • Mobile App Developer
  • macOS Developer
  • AR Developer
  • Software Engineer
• Why in Demand: Swift’s performance and safety features make it essential for iOS and macOS app development. It’s also widely used in AR/VR applications on Apple platforms.

11. Ruby

• Industries:
  • Web development (especially startups and e-commerce)
  • Fintech (payment gateways, financial applications)
  • Content management systems (CMS)
  • SaaS platforms (rapid development with Ruby on Rails)
  • Automation tools
• Job Roles:
  • Web Developer
  • Backend Developer
  • Full Stack Developer
  • DevOps Engineer
  • Automation Engineer
• Why in Demand: Ruby’s focus on simplicity and productivity makes it a favorite for startups and rapid prototyping. Ruby on Rails is still widely used for web development.

12. PHP

• Industries:
  • Web development (CMS, e-commerce platforms)
  • Content management (WordPress, Drupal)
  • E-commerce (Magento, WooCommerce)
  • Media and publishing (news websites, blogs)
  • Education (LMS systems like Moodle)
• Job Roles:
  • Web Developer
  • Backend Developer
  • CMS Developer
  • Full Stack Developer
  • DevOps Engineer
• Why in Demand: PHP remains crucial for web development, especially for content-heavy websites and e-commerce. WordPress alone powers over 40% of the web, ensuring PHP’s continued relevance.

13. R

• Industries:
  • Data science and analytics
  • Research and academia (statistics, bioinformatics)
  • Financial analysis (quantitative analysis)
  • Healthcare (biostatistics, epidemiology)
  • Marketing analytics (consumer behavior analysis)
• Job Roles:
  • Data Scientist
  • Statistician
  • Research Analyst
  • Financial Analyst
  • Bioinformatician
• Why in Demand: R’s specialization in statistical computing and data analysis makes it popular in research, academia, and industries dealing with large datasets, especially in healthcare and finance.

14. SQL

• Industries:
  • Database management and administration
  • Business intelligence and data warehousing
  • Financial services (transaction processing)
  • Healthcare (electronic health records)
  • Retail and e-commerce (inventory management)
• Job Roles:
  • Database Administrator (DBA)
  • Data Analyst
  • Business Intelligence Developer
  • Data Engineer
  • SQL Developer
• Why in Demand: SQL remains fundamental for database management across nearly every industry. It’s essential for working with relational databases, making it a must-know for data-driven businesses.

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These industries and roles reflect the growing demand for each language based on its strengths, with Python and JavaScript leading in versatility, Java and C# dominating enterprise software, and Go and Rust carving niches in cloud computing and system programming.

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Here’s a broad overview of salary ranges for the various roles associated with each programming language, based on 2024 data. These ranges will vary depending on the region, company size, and experience level (e.g., junior, mid-level, senior).

1. Python

• Data Scientist:
  • US: $95,000 – $160,000
  • Europe: €50,000 – €120,000
  • Asia: $35,000 – $80,000
• Machine Learning Engineer:
  • US: $110,000 – $180,000
  • Europe: €55,000 – €130,000
  • Asia: $40,000 – $90,000
• Backend Web Developer:
  • US: $85,000 – $140,000
  • Europe: €45,000 – €100,000
  • Asia: $30,000 – $70,000

2. JavaScript

• Frontend Web Developer:
  • US: $80,000 – $130,000
  • Europe: €40,000 – €90,000
  • Asia: $30,000 – $60,000
• Full Stack Developer:
  • US: $90,000 – $150,000
  • Europe: €50,000 – €110,000
  • Asia: $35,000 – $80,000
• UI/UX Engineer:
  • US: $75,000 – $120,000
  • Europe: €45,000 – €85,000
  • Asia: $25,000 – $60,000

3. Java

• Backend Developer:
  • US: $90,000 – $145,000
  • Europe: €50,000 – €100,000
  • Asia: $35,000 – $75,000
• Android Developer:
  • US: $85,000 – $140,000
  • Europe: €45,000 – €95,000
  • Asia: $30,000 – $70,000
• DevOps Engineer:
  • US: $100,000 – $160,000
  • Europe: €60,000 – €110,000
  • Asia: $40,000 – $90,000

4. C#

• Enterprise Software Developer:
  • US: $90,000 – $145,000
  • Europe: €50,000 – €100,000
  • Asia: $30,000 – $75,000
• Game Developer (Unity):
  • US: $70,000 – $120,000
  • Europe: €40,000 – €85,000
  • Asia: $25,000 – $60,000
• Cloud Engineer (Azure):
  • US: $100,000 – $150,000
  • Europe: €60,000 – €110,000
  • Asia: $40,000 – $80,000

5. C++

• Systems Software Engineer:
  • US: $100,000 – $160,000
  • Europe: €55,000 – €110,000
  • Asia: $40,000 – $90,000
• Game Developer:
  • US: $80,000 – $135,000
  • Europe: €45,000 – €95,000
  • Asia: $30,000 – $65,000
• Embedded Systems Engineer:
  • US: $85,000 – $140,000
  • Europe: €45,000 – €90,000
  • Asia: $30,000 – $70,000

6. Go (Golang)

• Cloud Engineer:
  • US: $100,000 – $165,000
  • Europe: €55,000 – €120,000
  • Asia: $40,000 – $90,000
• Site Reliability Engineer (SRE):
  • US: $110,000 – $180,000
  • Europe: €60,000 – €130,000
  • Asia: $45,000 – $95,000
• Backend Developer:
  • US: $95,000 – $150,000
  • Europe: €50,000 – €100,000
  • Asia: $35,000 – $80,000

7. Rust

• Systems Programmer:
  • US: $105,000 – $170,000
  • Europe: €60,000 – €120,000
  • Asia: $40,000 – $85,000
• Blockchain Developer:
  • US: $110,000 – $185,000
  • Europe: €65,000 – €130,000
  • Asia: $45,000 – $100,000
• Embedded Systems Engineer:
  • US: $90,000 – $150,000
  • Europe: €50,000 – €95,000
  • Asia: $35,000 – $75,000

8. TypeScript

• Frontend Developer:
  • US: $85,000 – $140,000
  • Europe: €45,000 – €95,000
  • Asia: $30,000 – $70,000
• Full Stack Developer:
  • US: $90,000 – $150,000
  • Europe: €50,000 – €110,000
  • Asia: $35,000 – $80,000
• Frontend Architect:
  • US: $110,000 – $180,000
  • Europe: €65,000 – €120,000
  • Asia: $45,000 – $90,000

9. Kotlin

• Android Developer:
  • US: $85,000 – $135,000
  • Europe: €45,000 – €95,000
  • Asia: $30,000 – $65,000
• Backend Developer:
  • US: $90,000 – $145,000
  • Europe: €50,000 – €100,000
  • Asia: $35,000 – $80,000
• Mobile App Developer:
  • US: $85,000 – $130,000
  • Europe: €45,000 – €90,000
  • Asia: $30,000 – $70,000

10. Swift

• iOS Developer:
  • US: $95,000 – $150,000
  • Europe: €50,000 – €105,000
  • Asia: $40,000 – $80,000
• AR Developer (Apple):
  • US: $100,000 – $160,000
  • Europe: €60,000 – €120,000
  • Asia: $45,000 – $90,000
• macOS Developer:
  • US: $90,000 – $145,000
  • Europe: €50,000 – €100,000
  • Asia: $35,000 – $75,000

11. Ruby

• Web Developer:
  • US: $80,000 – $130,000
  • Europe: €45,000 – €90,000
  • Asia: $30,000 – $65,000
• Backend Developer:
  • US: $85,000 – $140,000
  • Europe: €50,000 – €95,000
  • Asia: $35,000 – $75,000
• Full Stack Developer:
  • US: $90,000 – $145,000
  • Europe: €50,000 – €105,000
  • Asia: $35,000 – $80,000

12. PHP

• Web Developer:
  • US: $70,000 – $120,000
  • Europe: €40,000 – €85,000
  • Asia: $25,000 – $60,000
• Backend Developer:
  • US: $75,000 – $125,000
  • Europe: €45,000 – €90,000
  • Asia: $30,000 – $65,000
• CMS Developer:
  • US: $65,000 – $110,000
  • Europe: €40,000 – €80,000
  • Asia: $25,000 – $55,000

13. R

• Data Scientist:
  • US: $95,000 – $150,000
  • Europe: €50,000 – €100,000
  • Asia: $40,000 – $80,000
• Research Analyst:
  • US: $85,000 – $135,000
  • Europe: €45,000 – €90,000
  • Asia: $30,000 – $65,000
• Statistician:
  • US: $80,000 – $125,000
  • Europe: €45,000 – €85,000
  • Asia: $30,000 – $60,000

14. SQL

• Database Administrator (DBA):
  • US: $80,000 – $135,000
  • Europe: €45,000 – €95,000
  • Asia: $35,000 – $75,000
• Data Analyst:
  • US: $70,000 – $120,000
  • Europe: €40,000 – €85,000
  • Asia: $30,000 – $60,000
• Business Intelligence Developer:
  • US: $85,000 – $140,000
  • Europe: €50,000 – €100,000
  • Asia: $35,000 – $75,000

Key Takeaways

• US-based roles tend to offer the highest salaries across all languages and positions, especially in tech hubs like Silicon Valley, New York, and Seattle.
• European salaries are generally lower than the US but vary significantly by country, with Germany, the UK, and Switzerland offering the highest pay.
• Asia-based salaries are lower compared to the US and Europe, but there’s a wide range within countries like India, China, and Japan. Tech salaries in Singapore and Hong Kong are higher than in other Asian countries.