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What is the Future of Programming Languages ?

Developers have been using programming languages for decades to build ingenious new tools and streamline the coding process. It is worked out pretty well for us. Advancements in programming languages and software development overall have helped us push the boundaries of what is possible. Businesses have gotten more efficient; humans have gotten more productive and have become more entertained than ever before. And the basic elements of economic infrastructure such as the secure exchange of money, have been fundamentally reinvented.

Programming languages work to simplify the coding process for developers and take coding to a more abstract level. You can think of them as an interface that allows software developers to communicate with the computers form that both parties can understand. Instead of relying on binary digits, programmers can use words, functions or other, more readable entries. It makes the programming process much simpler it makes it simple to find errors when necessary. Because of this, programming languages also make programming much more accessible to a larger number of people.

Top Programming Languages of the Future

1. R

R is the dynamic, array-based, multi-paradigm programming language launched in 1993. It’s compatible with all the major operating systems such as Linux, macOS, and Windows. The unique aspect for R is that it doubles up as an environment for statistical computing and graphics. The R environment is an integrated suite of software tools designed for data manipulation, graphical display and statistical calculation.

R is highly extensible or offers for the host of statistical (linear/non-linear modelling, time-series analysis, regression, clustering, classification and more) and graphical methods. Being the multi-paradigm language, R supports object-oriented, imperative, functional and procedural programming paradigms. Like Python, R comes with the impressive range of libraries such as ggplot, leaflet, tidyr and more. R’s scalable qualities and capacities for the statistics and data visualization are what makes it a favourite for developers and Data Scientists.

2. Python

Python takes the top rank in all its crowning glory. Python is immensely popular among developers and Data Science experts because of its simplicity or versatility. Simple learning curve and English-like syntax, Python is a beginner-friendly language. Moreover, Python has excellent library support for the wide range for coding requirements. For instance, Python libraries such as Django or Flask are ideal for web development, whereas Keras, TensorFlow, and SciPy are best for the Data Science applications. You can use Python to develop web and desktop applications, GUI-based on desktop applications, network servers, ML models, and more. Python’s broad ecosystem for libraries, modules and frameworks makes application development super easy. It’s the backbone of many industry giants such as Twitter, Facebook, Pinterest, SurveyMonkey and more. 

3. C# 

Developed by Microsoft, C# is a general, flexible, and open-source, object-oriented programming (OOP) language. It’s primarily used for game development, backend development, web or desktop applications and mobile application development for the Windows phones.

C# is widely preferred ease-of-use and well-built architecture. It has dedicated .NET Core machine learning platform – ML.NET – to cater for the Machine Learning development needs. There is also Accord.NET and ML-Agents that allows .NET developers to develop unique ML applications. 

4. Java

Java is the unrivalled organization king when it comes to server-side application development. It’s the class-based, object-oriented programming language used for building software products, mobile applications, web applications, web servers, games and application servers. Java has long been the leading choice for Android app development. At present, Java is also quite popular among Big Data Engineers who use it to develop Big Data ecosystems. Java libraries such as JavaML, JSTAT, Deeplearning4j, Weka, Mahout, ADAMS, and RapidMiner, are much better for the ML development. Big names such as Uber, Google, Airbnb, Netflix, Amazon and Spotify use Java of their tech stack. 

5. TypeScript

TypeScript is the compiled OOP language designed to extend the capabilities of JavaScript by adding types. Types allow defining and describing object shapes; thereby providing better documentation and allowing TypeScript for validate your code. You can use TypeScript to build large-scale applications with the clearly defined syntax and minimal errors. Since 2012, Developed by Microsoft, TypeScript isn’t only one of the fastest-growing languages in the world. 

TypeScript is gaining massive popularity in the developer circle since it simplifies JavaScript code, making it easier for developers for read and debug. Also, it includes many development tools for JavaScript IDEs and practices. Many companies have started by using TypeScript for ML applications via Kalimdor, the browser-based Machine Learning library written in TypeScript. 

6. C++

C++ is a general-purpose used, cross-platform programming language developed by Bjarne Stroustrup as an extension for C. Finally, C++ combines the features of OOP, imperative and generic programming languages. It offers developers for high level of control over memory and system resources. Speed and efficiency are the two core features of C++ that allow developers to design and build high-performance applications. 

C++ is used for the developing operating systems, embedded systems and Graphical User Interfaces (GUIs). Since it is portable, C++ can help for build applications that can adapt to multiple platforms or environments. For Machine Learning, C++ has designated ML repositories such as TensorFlow, Turi Create and LightGBM. 

7. Kotlin

Kotlin is based on the statically-typed, general-purpose programming language. It’s design fully interoperable with Java. Since Kotlin’s primary target is the JVM, it also compiles to JavaScript and native code. The seamless interoperation between Kotlin and Java makes Android application development much faster and more efficient.Kotlin has relatively easy-to-learn structure and apart from Android development, it can be used for the web development, desktop app development and server-side development. Kotlin is concise, safe, and tool-friendly (bundle it with any Java IDE and even build via the command line).

8. JavaScript

JavaScript is the high-level, multi-paradigm, web scripting languages. It’s supports object-oriented, imperative, and declarative programming styles. JavaScript includes application programming interfaces (APIs) for text, regular expressions, dates, and standard data structures. It’s one of the three core technologies (alongside HTML or CSS) powering the World Wide Web.  JavaScript shares similarities with Java regarding of their naming convention, syntax and standard libraries, they are innately different. It syncs well with Java and is very simple to implement to its HTML integration. JavaScript has many libraries for developing ML models including TensorFlow.js, Brain.js, machinelearn.js, Synaptic, ConvNetJS, Mind and Neuro.js.