The Importance of Studying Algorithms — Your Competitive Edge

Nowadays, few can deny the value that computers deliver to virtually every aspect of our personal and professional lives. Between cloud-based networks, social media platforms, email servers, multi-user programs, mobile applications and other internet-based systems, we're seemingly always connected to our friends, coworkers, peers, family members and everyone else around us.

Many of these applications are based on programming, and make our lives easier without requiring that we understand the coding and the AI logic behind them. Suffice to say, programming is behind much of the convenience that technology offers today.

Programming languages have made things easy for us both consumers and as professional employees. As we fulfill recurring and systematic tasks, thanks to the codes behind our applications, we often forget the dedication involved in creating, and maintaining, the same applications we use at work and at home. Each of the many programming languages used today has a specific purpose. Two major types of programming languages — structured programming languages and object-oriented programming languages — represent much of the programming present across today's popular systems.

As discussed, both structured programming languages and object-oriented programming languages are commonly deployed by coders. In structured programming languages, a set of instructions and rules dictates an application's structure and function. The language's logic also plays a large role in the programming language's use. After all, coders create entire operations based on a program's logic. By contrast, object-oriented programming, offers a reflection of the real-time existence of different circumstances.

We can also divide these languages into compiled and interpreted languages. The interpreted language always draws upon another program to interpret it, while the other one needs a compiler to compile and execute it. We use the internet to access everything we need, but to access the internet there are programs and algorithms written in different programming languages to make the access possible.

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What Is an Algorithm?

You must have heard that for every problem, there is a solution. An algorithm is a solution to our programming challenges. It contains the steps in a sequence that we need to solve a problem. To a programmer, an algorithm is more than a solution to a problem; instead, algorithms provide more efficient paths to completing tasks, understanding complicated data sets, even importing data into new platforms. In sort, algorithms help programmers both solve problems and establish the most efficient route toward success.

Why Is It Important To Study Algorithms?

Algorithms remain critically important in today's technology-first world, even though many consumers will never see an algorithm in context. If programming itself is a building, then algorithms represent the pillars — supportive structures that allow algorithms to function well. Without the support, the buildings couldn't exist.

Still, why would we bother with algorithms? Shouldn't we just interface directly with the program?

Let's answer this question with an example.

Let's suppose we are building something, and we have the result in mind. Even though we're not an expert in building this structure, we're still confident in our construction abilities. And we've brought all of the tools and building materials necessary to complete the project.

After you finish building your structure, it looks exactly like you were expecting it would. However, it doesn't fulfill the purpose you built it for. Instead, it fulfills a completely different purpose, and functions differently than you expected. How can you find a use for this program? Better yet, how can you tailor the product so that it still fulfills its intended purpose?

This is exactly the purpose of an algorithm: to provide meaning to a program, whether or not it fulfills its originally intended purpose. As algorithms are used across a wide variety of applications today, studying them can provide you with more insight into how they function, and how they can improve the efficiency of the programs you already use.

To better showcase the value that algorithms offer, let's examine a few of their applications:

• Runtime analysis — Speed often separates the best algorithms from the rest. Most often, the speed of an algorithm depends not only only th efficiency of the algorithm itself, but also the platform you're running it on. The complexity of your algorithm can also affect its speed. Even a small difference in efficiency for one part of your algorithm can make a huge difference, particularly when your algorithm repeats itself hundreds of times — or when your algorithm takes, hours, days or longer to complete.

• Google search algorithms — Today, it might be difficult to find a consumer not already familiar with Google and its search engine. After all, Google current owns more than 91% of the global search engine market. That's more than two trillion searches each year! Who does not know about Google and its search engine? There are millions of searches every day on Google's search engine, and it handles all of them in the most optimized way. Have we ever imagined how it can manage data this complex? As you may have guessed, a variety of different algorithms allow Google's search engine, and virtually every other search engine currently live today, to run smoothly and connect users with the information they're looking for.

• Online transactions — Online transactions, like many other real-time internet features, rely heavily on advanced financial algorithms. Today, it's more convenient than ever to invest in funds, pay bills, even exchange money with peers, online. Behind each of these capabilities is one or more algorithms. Whether it's a bank or other financial institution, mobile application or online platform, organizations today depend on algorithms to improve the digital flow of funds, while keeping your information — and your money — secure.

These and other applications make algorithms the perfect catalyst for a wide variety of digital processes.

How Can Studying Algorithms Give You an Edge?

An algorithm is the building block and the logic behind a program. In other words, it is the step-by-step guide on how a program can fulfill a process. An algorithm is a computational model that adopts input and provides output after processing. Quite obviously, both developers and programmers need to fully understand the algorithms they use.

Studying algorithms gives tech professionals an obvious edge over individuals who don't understand how algorithms work and how to deploy them strategically. One way that you can deepen your understanding of algorithm characteristics is through a web development, or software engineering bootcamp, to accelerate your web-based skillset.

Here are several reasons to consider studying algorithms:

• Improve the efficiency of your programs — There are so many different ways that programmers write code. Obviously, some code-writing methods are more efficient than others. To a large extent, the efficiency of the code parallels how effective the logic behind the code is. Sometimes, the same logic that makes one algorithm dynamic can slow another algorithm down.

The best algorithms typically share at least one feature: positive results. Well-written algorithms produce positive results at their outcome, particularly for data experts combing extensive data sets. That same algorithm should help professions accelerate processes for greater efficiency, whether they're combing data for trends or code for execution.

• Properly utilize resources — Ultimately, algorithms are deployed to achieve a specific, data-driven result. Sometimes, that result is only to aggregate data a certain way. In other ways, an algorithm can help platform users achieve a particular result. Regardless of the intended result, your algorithm(s) should have the capability to correctly utilize resources on the way to the intended result. Typically, those resources include network bandwidth, available devices, programmers/developers and actual users. Your resource count should also account for available device memory and storage; depending on the size of the algorithms you use, you might need to consider improving available memory for execution.

• Solve problems more effectively — When programmers and developers develop algorithms, they need to understand the entire process. The complete algorithm engineering process is often separated into phases, from input to output. This way, tech professionals can tackle smaller projects, instead of staring down larger deadlines and moving parts. A longer project runway, comprised of manageable deadlines, makes complex problems solvable without major delays. We need to understand the complete process, from input to output. The complete process is divided into various smaller ones that can be managed more easily.

In simple words, problem-solving becomes easier behind the right project management strategy.

• Achieve clarity— The right algorithm makes it easier for coders to understand a process, just like it can make it easier for users to complete a task. When you take the time to study algorithms, particularly algorithm creation, you'll begin to understand the major differences between a well-created algorithm and one that can actually slow users down.

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Types of Algorithms

As you can imagine, different types of algorithms are used for different applications. For example, an algorithm that tailors a search engine will likely look completely different than an algorithm a data scientist uses to extract data from a set of spreadsheets.

To choose the algorithm type that best accommodates your project, you'll need to first understand the different types of algorithms available to developers and programmers. LEt's examine some of the more common types of algorithms below.

• Recursive Algorithm — When it comes to algorithm types, recursive algorithms keep things comparatively simple. Recursive algorithms are among the simplest algorithms in use today; applied the right way, they're still extremely powerful. Recursive algorithms continue to cycle until the pre-established problem has a resolution. The recursive process is dynamic, but can consume a substantial amount of memory in the process. After all, a continual algorithm cycle has no defined memory limits; it will continue until a resolution is established. Still, recursive algorithms are often preferred by programmers and developers because of their automated, continuous nature.

• Divide and Conquer – The SubProblems Algorithm — The "divide and conquer" algorithm approach is based on a subprogram model. Unsurprisingly, this algorithm type separates a problem into a series of distinct challenges, and solves one at a time. The results of each subproblem is then combined with the next, in order to establish a comprehensive resolution to an identified problem. This type of algorithm is often characterized by its two distinct features: the "divide" portion and the "conquer" portion. First, this algorithm type often divides a single issue into several problems to be solved. After this process is complete, the algorithm will begin diagnosing and solving the first problem, then progressing to the next. After the algorithm has satisfied the issue, it will combine the resolutions identified for each section into a single answer.

• Dynamic Programming Algorithm — If you face a complex problem that needs a resolution, the Dynamic Programming Algorithm can almost always help. Efficient and effective, this type of algorithm functions by referencing past results present in its memory. The Dynamic Programming Algorithm model can actually use its experience in past processes to inform the challenges it faces in the present. This type of algorithm will use a bottom-up approach (where the last challenge is addressed first) or a top-down approach (where the very first subproblem is given priority).

• Greedy Algorithm — This type of algorithm only considers the present, without regard for the process's outcome. All decisions made by a Greedy Algorithm are based solely on the best possible outcome in the moment, regardless of what future algorithm results might hold. It is an algorithm that considers the present only as it does not care about the future. All the decisions made in this algorithm and the result is only based on the current best scenario regardless of whatever happens in the future.

There are five total components of this algorithm:

• The list from which we try to find the solution to the problem;
• The function that identifies the best-suited candidate for the problem;
• The function that identifies whether the chosen candidate will succeed;
• The assignment of value to a potential solution;
• The solution function that identifies when we have found our solution to the problem.

These components help programmers and developers implement Greedy Algorithms with precision.

• The Brute Force Algorithm — In the list of complex programming algorithms, the Brute Force Algorithm isn't the most complicated. Still, talented programmers and developers can solve nearly every problem with this algorithm, as long as it's deployed correctly. The Brute Force Algorithm attempts to find a resolution to a challenge from step one, optimizing all of its efforts toward an identified end goal. As soon as a programmer or developer begins the iteration process, the Brute Force Algorithm attempts to achieve a resolution in the shortest possible time.

• Backtracking Algorithm — Backtracking is an algorithm that is a combination of some previously mentioned algorithms, as it is an advanced version of the Brute Force Algorithm. In the Backtracking Algorithm, developers and engineers consider all possible solutions from several different angles. Out of the many possible solutions, the Backtracking Algorithm selects one possible route to success and works to resolve the challenge. If the algorithm is unable to achieve a solution, it will "backtrack" to earlier stages of its process, and attempt to find another resolution — this time, by using an entirely different approach. Sometimes, this means that the algorithm may need to backtrack more than once, even a great number of times, until it finds a route through the data or the code that allows for success.

With the Backtracking algorithm, developers and programmers can generate any kind of binary string, solve the N-Queens problem and the graph coloring problem, and address a wide variety of other complications that users and tech experts might face.

Today, we undoubtedly live in the era of cloud computing and machine learning. Machine learning is a domain of information technology that requires advanced algorithms for immediate and long-term success.

Let's examine how machine learning algorithms can provide developers, programmers and any other algorithm users with the edge we'll need to outpace industry competition.

Machine Learning Algorithms and Your Competitive Edge

In the artificial intelligence field, machine learning represents one of the next evolutionary advancements. If machine learning has hands, those hands are algorithms — capable of fulfilling the most complex processes across a wide variety of different data sets. Without algorithms, machine learning and AI are largely powerless to automate tasks and make life easier for consumers.

We know from the research that the future belongs to data, and much of that data is collected, and then filtered for analysis, by organizations. Regardless of the size of the data sets, or the length a process might take to finish, today's algorithms are more than strong enough to handle the load. Organizations that focus on machine learning and its algorithms will see a better future than others, largely because data analysis can quickly mean the difference between data-driven success and market ignorance.

Another major application of machine learning algorithms is prevalent across modern search engines. Today, the best search engines are known for their capacity to pair users with the exact data they're really looking for, even if the search itself is characterized by any misspellings or minor mistakes. Many organizations are leveraging similar algorithms on their own websites, to assist with any searches that take place inside the website itself. For example, if a potential consumer searches for a specific product on a website using their search bar, that company wants to make sure that the consumer arrives at the correct page. To help make the search experience smoother and more meaningful, these companies can use one or more algorithms specifically engineered for website search.

Without the right algorithm, programs are little more than a set of directions. Algorithms can give programs life, and greatly assist in improving the logic of a specific program — particularly if that program might not completely fulfill its intended goal the first time through.