A Comprehensive Guide for Aspiring Coders

The primary way humans communicate with machines is through programming languages. This is how we tell a computer what we want it to do. Every software application is written using some form of programming language.

A programming language is a computer scientist’s primary tool. Every computer scientist must be an expert in at least three to four programming languages.

Developers have written hundreds of programming languages. Others have fallen out of favor and are no longer in common use. The languages that developers are using continue to evolve and change continually.

In subsequent chapters, we focus on several dozen of the most popular languages.

Understanding Programming Languages

The method humans typically use to interact with and give direct instructions to computers is programming languages. These serve as the tool to complete most tasks directly related to coding and software development. You can specify an algorithm to be followed, perform simple arithmetic and task scheduling, or as the primary tool used to build a custom application from scratch. Programming languages abstract away many of these difficult tasks and can simplify all that a computer might do into potentially a very short piece of written text, which is fundamental to computer science.

Programming languages can be divided into just two distinct categories:

• Compiled. These languages are ones that must be completely read and translated from start to finish before a single line of it can finally be run on a computer. C and C++ are examples. This process makes these languages theoretically harder to program and use for debugging, but they can yield many positive side effects. If these languages suffer a runtime error, then it’s usually due to a logical error or due to some intermittent mathematical calculation error. The key advantage to these languages is overloadable functions/parameters, speed of execution, and constraints, therefore, they are typically the language of choice for writing applications embedded into systems such as airplanes, cars, and TVs.
• Interpreted. They can be read line by line and translated by the parser on-the-fly during runtime. Examples of these languages are Python and JavaScript. They are generally easier to learn and debug (you can stop at breakpoints and go through the code more easily) but can suffer from issues specific to execution. Fast information processing and data I/O manipulation are not usually as suitable for these languages.

Overview of Popular Programming Languages

Considered the “assembly language of the web”. JavaScript in front-end coding to define the interactivity and dynamism of many front-end UI elements. It’s ability to manipulate the DOM allows us to define what does button click does, and that image expand, scrollbar functions, and more. JavaScript makes every YouTube experience an enjoyable one.

Over the years, design frameworks such as React, Angular and Vue.js, and performance libraries, have emerged that allow you to power bigger and more complex JavaScript applications. But at their core (front-end) is JavaScript.

Given the number of websites in the world today, this makes JavaScript the most popular programming language by installation base.

Python finds use cases in AI, data analysis, and web development, among others. Do data scientists prefer Python? Libraries such as Pandas and tasks that perform a quick and dirty data analysis and plot them make Python a great tool. More than that, if you’re interested in data analysis, you’re most likely a researcher or a student in research. You’re probably more interested in finding groundbreaking insights and discoveries than the programming language that gets you to that destination.

Python is also an efficient language for a broad spectrum of tasks. I’ve known friends whose research utilized Python, and have used it for building web applications myself. The sell is that, imagine that you already know Python, the various libraries that sit in the Python environment, and the structure of the Python language, would you not use it for other programming projects than spend time learning another one?

Games on your PC and PlayStation console, web browsers such as Microsoft’s Internet Explorer, computer-aided design systems, and virtual reality applications.

Choosing the Right Programming Language

If you’re learning to program so you can work as a programmer, there are several things you should take into account:

• Project requirements. You can’t use every programming language for every kind of project. If you’re doing a highly mathematical project—let’s say you’re working in data science—you’re most likely going to learn Python or R for your project. If you’re interested in web development, you’re going to lean toward JavaScript. As a beginner, having a wildly different project will sometimes guide you toward one language over another.
• Community support. Some programming languages have stronger community support than others. Big communities provide easier access to resources (more resources or more variables), more tutorials, more forums where I can get my troubleshooting questions answered, and more posts (resources) that contain the error message that I just received in my command line. If you’re really relying on support, it might make sense to stick to one of the bigger communities like Python or JavaScript because you’ll have more people whose expertise you can tap into.
• Job market demand. You don’t want to learn a programming language that no one in the job market is using or that there’s a really low demand for. An example of this might look like continuously seeing Python, Java, or JavaScript in job postings. Those are jobs that I definitely want, just basically a sense that there is a large job market out there for that language.
• Relative difficulty. The curve to get “good” or “proficient” will be much higher in some languages compared to others.

The Future of Programming Languages

Today’s tech industry is characterized by an explosive growth of new programming languages and frameworks. This trend has been necessitated by the need to do things better, but also differently. There are languages like Rust and Go that are intended to do what C and C++ do—systems programming, concurrent programming, and other tasks requiring detailed control of hardware and memory—but do those tasks with better performance and safety. On the other hand, there are modern frameworks for new languages and others, such as JavaScript’s frameworks React and Vue.js, which have simply made front-end development easier—and in some cases—changed the way we design and think about user interfaces completely. These trends provide a great deal of beneficial, almost necessary, specialization and innovation.

Continuing, how about the future of other technologies—which will necessarily influence the future of programming languages? More specifically, because other technologies are obviously changing at faster or slower relative speeds, the programming languages required to create great software will necessarily adapt to the same conditions. Inferences about the future of cloud computing, artificial intelligence, data science, and other technologies can be—and perhaps must be—made, based on a common (or perhaps new) programming language. Python has become the preferred language for these use cases, because of its simple syntax and programming methodologies (object orientation, composition), and its exceptional libraries. And Julia—the current, preferred programming language for high-performance, numerical-based, distributed, and similar computing—does something really fantastic. It is unique in that it changes the intended audience (for whom a general-purpose language is ideal), from that which is determined by a preferred paradigm, to that which is designed or destined.

The future of the enterprise software, therefore, looks similar to the immediate past, as well as the present, with two obvious differences: more programming languages and frameworks based on purpose-built software, and more programming languages based on common paradigms. Both use cases may (should) also change the audience for which these use cases are designed and developed. This trend—and by extension, the future of the software development profession—looks an awful lot more like (and probably will be more akin to) today’s mechanical engineering profession. And that is how it should be—almost must be.

Knowing different programming languages is a must for anyone who codes. Every language has its syntax, paradigms, and places where it’s used, so you need to know these things. Languages like Python are preferred for beginners and used a lot in data science because they’re simple and easy to use. On the other hand, languages like C++ and Java are generally understood to be difficult to use, they’re also unavoidable if you’re into system programming or working with large enterprise applications because they trade off user (developer) usability for better performance and scalability.

Understanding principles of different programming languages will increase your problem-solving skills. When you are in a certain situation, you will have different solutions. For example, let’s look at language like Haskell (a functional programming language) versus Java (an object-oriented programming language). Although you can accomplish the same goal in either language, the approach may cover the difference of principles.

Research some job posters for the programming language you will need because most of the job posters will include them. Where do you find you will be in the near future? Do you expect yourself to be a fine data analyst? Are you a front end, back end, full-stack developer? Or are you interested in hardware, and you want to become a system programmer? Everything listed out will help you catch the job easier when the chance comes.

Just FYI, learning the programming language and understanding principles is a principal strategy you will need in your career in development.