Machine learning, data science, and artificial intelligence are common terms in modern technology. These terms are often used interchangeably but incorrectly, which is understandable.

After all, hundreds of millions of people use the advantages of digital technologies. Yet only a small percentage of those users are experts in the field.

AI, data science, and machine learning represent valuable assets that can be used to great advantage in various industries. However, to use these tools properly, you need to understand what they are. Furthermore, knowing the difference between data science and machine learning, as well as how AI differs from both, can dispel the common misconceptions about these technologies.

Read on to gain a better understanding of the three crucial tech concepts.

Data Science

Data science can be viewed as the foundation of many modern technological solutions. It’s also the stage from which existing solutions can progress and evolve. Let’s define data science in more detail.

Definition and Explanation of Data Science

A scientific discipline with practical applications, data science represents a field of study dedicated to the development of data systems. If this definition sounds too broad, that’s because data science is a broad field by its nature.

Data structure is the primary concern of data science. To produce clean data and conduct analysis, scientists use a range of methods and tools, from manual to automated solutions.

Data science has another crucial task: defining problems that previously didn’t exist or slipped by unnoticed. Through this activity, data scientists can help predict unforeseen issues, improve existing digital tools, and promote the development of new ones.

Key Components of Data Science

Breaking down data science into key components, we get to three essential factors:

• Data collection
• Data analysis
• Predictive modeling

Data collection is pretty much what it sounds like – gathering of data. This aspect of data science also includes preprocessing, which is essentially preparation of raw data for further processing.

During data analysis, data scientists draw conclusions based on the gathered data. They search the data for patterns and potential flaws. The scientists do this to determine weak points and system deficiencies. In data visualization, scientists aim to communicate the conclusions of their investigation through graphics, charts, bullet points, and maps.

Finally, predictive modeling represents one of the ultimate uses of the analyzed data. Here, create models that can help them predict future trends. This component also illustrates the differentiation between data science vs. machine learning. Machine learning is often used in predictive modeling as a tool within the broader field of data science.

Applications and Use Cases of Data Science

Data science finds uses in marketing, banking, finance, logistics, HR, and trading, to name a few. Financial institutions and businesses take advantage of data science to assess and manage risks. The powerful assistance of data science often helps these organizations gain the upper hand in the market.

In marketing, data science can provide valuable information about customers, help marketing departments organize, and launch effective targeted campaigns. When it comes to human resources, extensive data gathering, and analysis allow HR departments to single out the best available talent and create accurate employee performance projections.

Artificial Intelligence (AI)

The term “artificial intelligence” has been somewhat warped by popular culture. Despite the varying interpretations, AI is a concrete technology with a clear definition and purpose, as well as numerous applications.

Definition and Explanation of AI

Artificial intelligence is sometimes called machine intelligence. In its essence, AI represents a machine simulation of human learning and decision-making processes.

AI gives machines the function of empirical learning, i.e., using experiences and observations to gain new knowledge. However, machines can’t acquire new experiences independently. They need to be fed relevant data for the AI process to work.

Furthermore, AI must be able to self-correct so that it can act as an active participant in improving its abilities.

Obviously, AI represents a rather complex technology. We’ll explain its key components in the following section.

Key Components of AI

A branch of computer science, AI includes several components that are either subsets of one another or work in tandem. These are machine learning, deep learning, natural language processing (NLP), computer vision, and robotics.

It’s no coincidence that machine learning popped up at the top spot here. It’s a crucial aspect of AI that does precisely what the name says: enables machines to learn.

We’ll discuss machine learning in a separate section.

Deep learning relates to machine learning. Its aim is essentially to simulate the human brain. To that end, the technology utilizes neural networks alongside complex algorithm structures that allow the machine to make independent decisions.

Natural language processing (NLP) allows machines to comprehend language similarly to humans. Language processing and understanding are the primary tasks of this AI branch.

Somewhat similar to NLP, computer vision allows machines to process visual input and extract useful data from it. And just as NLP enables a computer to understand language, computer vision facilitates a meaningful interpretation of visual information.

Finally, robotics are AI-controlled machines that can replace humans in dangerous or extremely complex tasks. As a branch of AI, robotics differs from robotic engineering, which focuses on the mechanical aspects of building machines.

Applications and Use Cases of AI

The variety of AI components makes the technology suitable for a wide range of applications. Machine and deep learning are extremely useful in data gathering. NLP has seen a massive uptick in popularity lately, especially with tools like ChatGPT and similar chatbots. And robotics has been around for decades, finding use in various industries and services, in addition to military and space applications.

Machine Learning

Machine learning is an AI branch that’s frequently used in data science. Defining what this aspect of AI does will largely clarify its relationship to data science and artificial intelligence.

Definition and Explanation of Machine Learning

Machine learning utilizes advanced algorithms to detect data patterns and interpret their meaning. The most important facets of machine learning include handling various data types, scalability, and high-level automation.

Like AI in general, machine learning also has a level of complexity to it, consisting of several key components.

Key Components of Machine Learning

The main aspects of machine learning are supervised, unsupervised, and reinforcement learning.

Supervised learning trains algorithms for data classification using labeled datasets. Simply put, the data is first labeled and then fed into the machine.

Unsupervised learning relies on algorithms that can make sense of unlabeled datasets. In other words, external intervention isn’t necessary here – the machine can analyze data patterns on its own.

Finally, reinforcement learning is the level of machine learning where the AI can learn to respond to input in an optimal way. The machine learns correct behavior through observation and environmental interactions without human assistance.

Applications and Use Cases of Machine Learning

As mentioned, machine learning is particularly useful in data science. The technology makes processing large volumes of data much easier while producing more accurate results. Supervised and particularly unsupervised learning are especially helpful here.

Reinforcement learning is most efficient in uncertain or unpredictable environments. It finds use in robotics, autonomous driving, and all situations where it’s impossible to pre-program machines with sufficient accuracy.

Perhaps most famously, reinforcement learning is behind AlphaGo, an AI program developed for the Go board game. The game is notorious for its complexity, having about 250 possible moves on each of 150 turns, which is how long a typical game lasts.

Alpha Go managed to defeat the human Go champion by getting better at the game through numerous previous matches.

Key Differences Between Data Science, AI, and Machine Learning

The differences between machine learning, data science, and artificial intelligence are evident in the scope, objectives, techniques, required skill sets, and application.

As a subset of AI and a frequent tool in data science, machine learning has a more closely defined scope. It’s structured differently to data science and artificial intelligence, both massive fields of study with far-reaching objectives.

The objectives of data science are pto gather and analyze data. Machine learning and AI can take that data and utilize it for problem-solving, decision-making, and to simulate the most complex traits of the human brain.

Machine learning has the ultimate goal of achieving high accuracy in pattern comprehension. On the other hand, the main task of AI in general is to ensure success, particularly in emulating specific facets of human behavior.

All three require specific skill sets. In the case of data science vs. machine learning, the sets don’t match. The former requires knowledge of SQL, ETL, and domains, while the latter calls for Python, math, and data-wrangling expertise.

Naturally, machine learning will have overlapping skill sets with AI, since it’s its subset.

Finally, in the application field, data science produces valuable data-driven insights, AI is largely used in virtual assistants, while machine learning powers search engine algorithms.

How Data Science, AI, and Machine Learning Complement Each Other

Data science helps AI and machine learning by providing accurate, valuable data. Machine learning is critical in processing data and functions as a primary component of AI. And artificial intelligence provides novel solutions on all fronts, allowing for more efficient automation and optimal processes.

Through the interaction of data science, AI, and machine learning, all three branches can develop further, bringing improvement to all related industries.

Understanding the Technology of the Future

Understanding the differences and common uses of data science, AI, and machine learning is essential for professionals in the field. However, it can also be valuable for businesses looking to leverage modern and future technologies.

As all three facets of modern tech develop, it will be important to keep an eye on emerging trends and watch for future developments.

Did you know you’re participating in a distributed computing system simply by reading this article? That’s right, the massive network that is the internet is an example of distributed computing, as is every application that uses the world wide web.

Distributed computing involves getting multiple computing units to work together to solve a single problem or perform a single task. Distributing the workload across multiple interconnected units leads to the formation of a super-computer that has the resources to deal with virtually any challenge.

Without this approach, large-scale operations involving computers would be all but impossible. Sure, this has significant implications for scientific research and big data processing. But it also hits close to home for an average internet user. No distributed computing means no massively multiplayer online games, e-commerce websites, or social media networks.

With all this in mind, let’s look at this valuable system in more detail and discuss its advantages, disadvantages, and applications.

Basics of Distributed Computing

Distributed computing aims to make an entire computer network operate as a single unit. Read on to find out how this is possible.

Components of a Distributed System

A distributed system has three primary components: nodes, communication channels, and middleware.

Nodes

The entire premise of distributed computing is breaking down one giant task into several smaller subtasks. And who deals with these subtasks? The answer is nodes. Each node (independent computing unit within a network) gets a subtask.

Communication Channels

For nodes to work together, they must be able to communicate. That’s where communication channels come into play.

Middleware

Middleware is the middleman between the underlying infrastructure of a distributed computing system and its applications. Both sides benefit from it, as it facilitates their communication and coordination.

Types of Distributed Systems

Coordinating the essential components of a distributed computing system in different ways results in different distributed system types.

Client-Server Systems

A client-server system consists of two endpoints: clients and servers. Clients are there to make requests. Armed with all the necessary data, servers are the ones that respond to these requests.

The internet, as a whole, is a client-server system. If you’d like a more specific example, think of how streaming platforms (Netflix, Disney+, Max) operate.

Peer-to-Peer Systems

Peer-to-peer systems take a more democratic approach than their client-server counterparts: they allocate equal responsibilities to each unit in the network. So, no unit holds all the power and each unit can act as a server or a client.

Content sharing through clients like BitTorrent, file streaming through apps like Popcorn Time, and blockchain networks like Bitcoin are some well-known examples of peer-to-peer systems.

Grid Computing

Coordinate a grid of geographically distributed resources (computers, networks, servers, etc.) that work together to complete a common task, and you get grid computing.

Whether belonging to multiple organizations or far away from each other, nothing will stop these resources from acting as a uniform computing system.

Cloud Computing

In cloud computing, centralized data centers store data that organizations can access on demand. These centers might be centralized, but each has a different function. That’s where the distributed system in cloud computing comes into play.

Thanks to the role of distributed computing in cloud computing, there’s no limit to the number of resources that can be shared and accessed.

Key Concepts in Distributed Computing

For a distributed computing system to operate efficiently, it must have specific qualities.

Scalability

If workload growth is an option, scalability is a necessity. Amp up the demand in a distributed computing system, and it responds by adding more nodes and consuming more resources.

Fault Tolerance

In a distributed computing system, nodes must rely on each other to complete the task at hand. But what happens if there’s a faulty node? Will the entire system crash? Fortunately, it won’t, and it has fault tolerance to thank.

Instead of crashing, a distributed computing system responds to a faulty node by switching to its working copy and continuing to operate as if nothing happened.

Consistency

A distributed computing system will go through many ups and downs. But through them all, it must uphold consistency across all nodes. Without consistency, a unified and up-to-date system is simply not possible.

Concurrency

Concurrency refers to the ability of a distributed computing system to execute numerous processes simultaneously.

Parallel computing and distributed computing have this quality in common, leading many to mix up these two models. But there’s a key difference between parallel and distributed computing in this regard. With the former, multiple processors or cores of a single computing unit perform the simultaneous processes. As for distributed computing, it relies on interconnected nodes that only act as a single unit for the same task.

Despite their differences, both parallel and distributed computing systems have a common enemy to concurrency: deadlocks (blocking of two or more processes). When a deadlock occurs, concurrency goes out of the window.

Advantages of Distributed Computing

There are numerous reasons why using distributed computing is a good idea:

• Improved performance. Access to multiple resources means performing at peak capacity, regardless of the workload.
• Resource sharing. Sharing resources between several workstations is your one-way ticket to efficiently completing computation tasks.
• Increased reliability and availability. Unlike single-system computing, distributed computing has no single point of failure. This means welcoming reliability, consistency, and availability and bidding farewell to hardware vulnerabilities and software failures.
• Scalability and flexibility. When it comes to distributed computing, there’s no such thing as too much workload. The system will simply add new nodes and carry on. No centralized system can match this level of scalability and flexibility.
• Cost-effectiveness. Delegating a task to several lower-end computing units is much more cost-effective than purchasing a single high-end unit.

Challenges in Distributed Computing

Although this offers numerous advantages, it’s not always smooth sailing with distributed systems. All involved parties are still trying to address the following challenges:

• Network latency and bandwidth limitations. Not all distributed systems can handle a massive amount of data on time. Even the slightest delay (latency) can affect the system’s overall performance. The same goes for bandwidth limitations (the amount of data that can be transmitted simultaneously).
• Security and privacy concerns. While sharing resources has numerous benefits, it also has a significant flaw: data security. If a system as open as a distributed computing system doesn’t prioritize security and privacy, it will be plagued by data breaches and similar cybersecurity threats.
• Data consistency and synchronization. A distributed computing system derives all its power from its numerous nodes. But coordinating all these nodes (various hardware, software, and network configurations) is no easy task. That’s why issues with data consistency and synchronization (concurrency) come as no surprise.
• System complexity and management. The bigger the distributed computing system, the more challenging it gets to manage it efficiently. It calls for more knowledge, skills, and money.
• Interoperability and standardization. Due to the heterogeneous nature of a distributed computing system, maintaining interoperability and standardization between the nodes is challenging, to say the least.

Applications of Distributed Computing

Nowadays, distributed computing is everywhere. Take a look at some of its most common applications, and you’ll know exactly what we mean:

• Scientific research and simulations. Distributed computing systems model and simulate complex scientific data in fields like healthcare and life sciences. (For example, accelerating patient diagnosis with the help of a large volume of complex images (CT scans, X-rays, and MRIs).
• Big data processing and analytics. Big data sets call for ample storage, memory, and computational power. And that’s precisely what distributed computing brings to the table.
• Content delivery networks. Delivering content on a global scale (social media, websites, e-commerce stores, etc.) is only possible with distributed computing.
• Online gaming and virtual environments. Are you fond of massively multiplayer online games (MMOs) and virtual reality (VR) avatars? Well, you have distributed computing to thank for them.
• Internet of Things (IoT) and smart devices. At its very core, IoT is a distributed system. It relies on a mixture of physical access points and internet services to transform any devices into smart devices that can communicate with each other.

Future Trends in Distributed Computing

Given the flexibility and usability of distributed computing, data scientists and programmers are constantly trying to advance this revolutionary technology. Check out some of the most promising trends in distributed computing:

• Edge computing and fog computing – Overcoming latency challenges
• Serverless computing and Function-as-a-Service (FaaS) – Providing only the necessary amount of service on demand
• Blockchain – Connecting computing resources of cryptocurrency miners worldwide
• Artificial intelligence and machine learning – Improving the speed and accuracy in training models and processing data
• Quantum computing and distributed systems – Scaling up quantum computers

Distributed Computing Is Paving the Way Forward

The ability to scale up computational processes opens up a world of possibilities for data scientists, programmers, and entrepreneurs worldwide. That’s why current challenges and obstacles to distributed computing aren’t particularly worrisome. With a little more research, the trustworthiness of distributed systems won’t be questioned anymore.

Most people feel much better when they organize their personal spaces. Whether that’s an office, living room, or bedroom, it feels good to have everything arranged. Besides giving you a sense of peace and satisfaction, a neatly-organized space ensures you can find everything you need with ease.

The same goes for programs. They need data structures, i.e., ways of organizing data to ensure optimized processing, storage, and retrieval. Without data structures, it would be impossible to create efficient, functional programs, meaning the entire computer science field wouldn’t have its foundation.

Not all data structures are created equal. You have primitive and non-primitive structures, with the latter being divided into several subgroups. If you want to be a better programmer and write reliable and efficient codes, you need to understand the key differences between these structures.

In this introduction to data structures, we’ll cover their classifications, characteristics, and applications.

Primitive Data Structures

Let’s start our journey with the simplest data structures. Primitive data structures (simple data types) consist of characters that can’t be divided. They aren’t a collection of data and can store only one type of data, hence their name. Since primitive data structures can be operated (manipulated) directly according to machine instructions, they’re invaluable for the transmission of information between the programmer and the compiler.

There are four basic types of primitive data structures:

• Integers
• Floats
• Characters
• Booleans

Integers

Integers store positive and negative whole numbers (along with the number zero). As the name implies, integer data types use integers (no fractions or decimal points) to store precise information. If a value doesn’t belong to the numerical range integer data types support, the server won’t be able to store it.

The main advantages here are space-saving and simplicity. With these data types, you can perform arithmetic operations and store quantities and counts.

Floats

Floats are the opposite of integers. In this case, you have a “floating” number or a number that isn’t whole. They offer more precision but still have a high speed. Systems that have very small or extremely large numbers use floats.

Characters

Next, you have characters. As you may assume, character data types store characters. The characters can be a string of uppercase and/or lowercase single or multibyte letters, numbers, or other symbols that the code set “approves.”

Booleans

Booleans are the third type of data supported by computer programs (the other two are numbers and letters). In this case, the values are positive/negative or true/false. With this data type, you have a binary, either/or division, so you can use it to represent values as valid or invalid.

Linear Data Structures

Let’s move on to non-primitive data structures. The first on our agenda are linear data structures, i.e., those that feature data elements arranged sequentially. Every single element in these structures is connected to the previous and the following element, thus creating a unique linear arrangement.

Linear data structures have no hierarchy; they consist of a single level, meaning the elements can be retrieved in one run.

We can distinguish several types of linear data structures:

• Arrays
• Linked lists
• Stacks
• Queues

Arrays

Arrays are collections of data elements belonging to the same type. The elements are stored at adjoining locations, and each one can be accessed directly, thanks to the unique index number.

Arrays are the most basic data structures. If you want to conquer the data science field, you should learn the ins and outs of these structures.

They have many applications, from solving matrix problems to CPU scheduling, speech processing, online ticket booking systems, etc.

Linked Lists

Linked lists store elements in a list-like structure. However, the nodes aren’t stored at contiguous locations. Here, every node is connected (linked) to the subsequent node on the list with a link (reference).

One of the best real-life applications of linked lists is multiplayer games, where the lists are used to keep track of each player’s turn. You also use linked lists when viewing images and pressing right or left arrows to go to the next/previous image.

Stacks

The basic principles behind stacks are LIFO (last in, first out) or FILO (first in, last out). These data structures stick to a specific order of operations and entering and retrieving information can be done only from one end. Stacks can be implemented through linked lists or arrays and are parts of many algorithms.

With stacks, you can evaluate and convert arithmetic expressions, check parentheses, process function calls, undo/redo your actions in a word processor, and much more.

Queues

In these linear structures, the principle is FIFO (first in, first out). The data the program stores first will be the first to process. You could say queues work on a first-come, first-served basis. Unlike stacks, queues aren’t limited to entering and retrieving information from only one end. Queues can be implemented through arrays, linked lists, or stacks.

There are three types of queues:

• Simple
• Circular
• Priority

You use these data structures for job scheduling, CPU scheduling, multiple file downloading, and transferring data.

Non-Linear Data Structures

Non-linear and linear data structures are two diametrically opposite concepts. With non-linear structures, you don’t have elements arranged sequentially. This means there isn’t a single sequence that connects all elements. In this case, you have elements that can have multiple paths to each other. As you can imagine, implementing non-linear data structures is no walk in the park. But it’s worth it. These structures allow multi-level storage (hierarchy) and offer incredible memory efficiency.

Here are three types of non-linear data structures we’ll cover:

• Trees
• Graphs
• Hash tables

Trees

Naturally, trees have a tree-like structure. You start at the root node, which is divided into other nodes, and end up with leaf modes. Every node has one “parent” but can have multiple “children,” depending on the structure. All nodes contain some type of data.

Tree structures provide easier access to specific data and guarantee efficiency.

Three structures are often used in game development and indexing databases. You’ll also use them in machine learning, particularly decision analysis.

Graphs

The two most important elements of every graph are vertices (nodes) and edges. A graph is essentially a finite collection of vertices connected by edges. Although they may look simple, graphs can handle the most complex tasks. They’re used in operating systems and the World Wide Web.

You unconsciously use graphs with Google Maps. When you want to know the directions to a specific location, you enter it in the map. At that point, the location becomes the node, and the path that guides you is the edge.

Hash Tables

With hash tables, you store information in an associative manner. Every data value gets its unique index value, meaning you can quickly find exactly what you’re looking for.

This may sound complex, so let’s check out a real-life example. Think of a library with over 30,000 books. Every book gets a number, and the librarian uses this number when trying to locate it or learn more details about it.

That’s exactly how hash tables work. They make the search process and insertion much faster, which is why they have a wide array of applications.

Specialized Data Structures

When data structures can’t be classified as either linear or non-linear, they’re called specialized data structures. These structures have unique applications and principles and are used to represent specialized objects.

Here are three examples of these structures:

• Trie
• Bloom Filter
• Spatial Data

Trie

No, this isn’t a typo. “Trie” is derived from “retrieval,” so you can guess its purpose. A trie stores data which you can represent as graphs. It consists of nodes and edges, and every node contains a character that comes after the word formed by the parent node. This means that a key’s value is carried across the entire trie.

Bloom Filter

A bloom filter is a probabilistic data structure. You use it to analyze a set and investigate the presence of a specific element. In this case, “probabilistic” means that the filter can determine the absence but can result in false positives.

Spatial Data Structures

These structures organize data objects by position. As such, they have a key role in geographic systems, robotics, and computer graphics.

Choosing the Right Data Structure

Data structures can have many benefits, but only if you choose the right type for your needs. Here’s what to consider when selecting a data structure:

• Data size and complexity – Some data structures can’t handle large and/or complex data.
• Access patterns and frequency – Different structures have different ways of accessing data.
• Required data structure operations and their efficiency – Do you want to search, insert, sort, or delete data?
• Memory usage and constraints – Data structures have varying memory usages. Plus, every structure has limitations you’ll need to get acquainted with before selecting it.

Jump on the Data Structure Train

Data structures allow you to organize information and help you store and manage it. The mechanisms behind data structures make handling vast amounts of data much easier. Whether you want to visualize a real-world challenge or use structures in game development, image viewing, or computer sciences, they can be useful in various spheres.

As the data industry is evolving rapidly, if you want to stay in the loop with the latest trends, you need to be persistent and invest in your knowledge continuously.

From the local network you’re probably using to read this article to the entirety of the internet, you’re surrounded by computer networks wherever you go.

A computer network connects at least two computer systems using a medium. Sharing the same connection protocols, the computers within such networks can communicate with each other and exchange data, resources, and applications.

In an increasingly technological world, several types of computer network have become the thread that binds modern society. They differ in size (geographic area or the number of computers), purpose, and connection modes (wired or wireless). But they all have one thing in common: they’ve fueled the communication revolution worldwide.

This article will explore the intricacies of these different network types, delving into their features, advantages, and disadvantages.

Local Area Network (LAN)

Local Area Network (LAN) is a widely used computer network type that covers the smallest geographical area (a few miles) among the three main types of computer network (LAN, MAN, and WAN).

A LAN usually relies on wired connections since they are faster than their wireless counterparts. With a LAN, you don’t have to worry about external regulatory oversight. A LAN is a privately owned network.

Looking into the infrastructure of a LAN, you’ll typically find several devices (switches, routers, adapters, etc.), many network cables (Ethernet, fiber optic, etc.), and specific internet protocols (Ethernet, TCP/IP, Wi-Fi, etc.).

As with all types of computer network, a LAN has its fair share of advantages and disadvantages.

Users who opt for a LAN usually do so due to the following reasons:

• Setting up and managing a LAN is easy.
• A LAN provides fast data and message transfer.
• Even inexpensive hardware (hard disks, DVD-ROMs, etc.) can share a LAN.
• A LAN is more secure and offers increased fault tolerance than a WAN.
• All LAN users can share a single internet connection.

As for the drawbacks, these are some of the more concerning ones:

• A LAN is highly limited in geographical coverage. (Any growth requires costly infrastructure upgrades.)
• As more users connect to the network, it might get congested.
• A LAN doesn’t offer a high degree of privacy. (The admin can see the data files of each user.)

Regardless of these disadvantages, many people worldwide use a LAN. In computer networks, no other type is as prevalent. Look at virtually any home, office building, school, laboratory, hospital, and similar facilities, and you’ll probably spot a LAN.

Wide Area Network (WAN)

Do you want to experience a Wide Area Network (WAN) firsthand? Since you’re reading this article, you’ve already done so. That’s right. The internet is one of the biggest WANs in the world.

So, it goes without saying that a WAN is a computer network that spans a large geographical area. Of course, the internet is an outstanding example; most WANs are confined within the borders of a country or even limited to an enterprise.

Considering that a WAN needs to cover a considerable distance, it isn’t surprising it relies on connections like satellite links to transmit the data. Other components of a WAN include standard network devices (routers, modems, etc.) and network protocols (TCP/IP, MPLS, etc.).

The ability of a WAN to cover a large geographical area is one of its most significant advantages. But it’s certainly not the only one.

• A WAN offers remote access to shared software and other resources.
• Numerous users and applications can use a WAN simultaneously.
• A WAN facilitates easy communication between computers within the same network.
• With WAN, all data is centralized (no need to purchase separate backup servers, emails, etc.).

Of course, as with other types of computer network, there are some disadvantages to note.

• Setting up and maintaining a WAN is costly and challenging.
• Due to the higher distance, there can be some issues with the slower data transfer and delays.
• The use of multiple technologies can create security issues for the network. (A firewall, antivirus software, and other preventative security measures are a must.)

By now, you probably won’t be surprised that the most common uses of a WAN are dictated by its impressive size.

You’ll typically find WANs connecting multiple LANs, branches of the same institution (government, business, finance, education, etc.), and the residents of a city or a country (public networks, mobile broadband, fiber internet services, etc.).

Metropolitan Area Network (MAN)

A Metropolitan Area Network (MAN) interconnects different LANs to cover a larger geographical area (usually a town or a city). To put this into perspective, a MAN covers more than a LAN but less than a WAN.

A MAN offers high-speed connectivity and mainly relies on optical fibers. “Moderate” is the word that best describes a MAN’s data transfer rate and propagation delay.

You’ll need standard network devices like routers and switches to establish this network. As for transmission media, a MAN primarily relies on fiber optic cables and microwave links. The last component to consider is network protocols, which are also pretty standard (TCP/IP, Ethernet, etc.)

There are several reasons why internet users opt for a MAN in computer networks:

• A MAN can be used as an Internet Service Provider (ISP).
• Through a MAN, you can gain greater access to WANs.
• A dual connectivity bus allows simultaneous data transfer both ways.

Unfortunately, this network type isn’t without its flaws.

• A MAN can be expensive to set up and maintain. (For instance, it requires numerous cables.)
• The more users use a MAN, the more congestion and performance issues can ensue.
• Ensuring cybersecurity on this network is no easy task.

Despite these disadvantages, many government agencies fully trust MANs to connect to the citizens and private industries. The same goes for public services like high-speed DSL lines and cable TV networks within a city.

Personal Area Network (PAN)

The name of this network type will probably hint at how this network operates right away. In other words, a Personal Area Network (PAN) is a computer network centered around a single person. As such, it typically connects a person’s personal devices (computer, mobile phone, tablet, etc.) to the internet or a digital network.

With such focused use, geographical limits shouldn’t be surprising. A PAN covers only about 33 feet of area. To expand the reach of this low-range network, users employ wireless technologies (Wi-Fi, Bluetooth, etc.)

With these network connections and the personal devices that use the network out of the way, the only remaining components of a PAN are the network protocols it uses (TCP/IP, Bluetooth, etc.).

Users create these handy networks primarily due to their convenience. Easy setup, straightforward communications, no wires or cables … what’s not to like? Throw energy efficiency into the mix, and you’ll understand the appeal of PANs.

Of course, something as quick and easy as a PAN doesn’t go hand in hand with large-scale data transfers. Considering the limited coverage area and bandwidth, you can bid farewell to high-speed communication and handling large amounts of data.

Then again, look at the most common uses of PANs, and you’ll see that these are hardly needed. PANs come in handy for connecting personal devices, establishing an offline network at home, and connecting devices (cameras, locks, speakers, etc.) within a smart home setup.

Wireless Local Area Network (WLAN)

You’ll notice only one letter difference between WLAN and LAN. This means that this network operates similarly to a LAN, but the “W” indicates that it does so wirelessly. It extends the LAN’s reach, making a Wireless Local Area Network (WLAN) ideal for users who hate dealing with cables yet want a speedy and reliable network.

A WLAN owes its seamless operation to network connections like radio frequency and Wi-Fi. Other components that you should know about include network devices (wireless routers, access points, etc.) and network protocols (TCP/IP, Wi-Fi, etc.).

Flexible. Reliable. Robust. Mobile. Simple. Those are just some adjectives that accurately describe WLANs and make them such an appealing network type.

Of course, there are also a few disadvantages to note, especially when comparing WLANs to LANs.

WLANs offer less capacity, security, and quality than their wired counterparts. They’re also more expensive to install and vulnerable to various interferences (physical objects obstructing the signal, other WLAN networks, electronic devices, etc.).

Like LANs, you will likely see WLANs in households, office buildings, schools, and similar locations.

Virtual Private Network (VPN)

If you’re an avid internet user, you’ve probably encountered this scenario: you want to use public Wi-Fi but fear the consequences and stream specific content. Or this one may be familiar: you want to use apps, but they’re unavailable in your country. The solution for both cases is a VPN.

A Virtual Private Network, or VPN for short, uses tunneling protocols to create a private network over a less secure public network. You’ll probably have to pay to access a premium virtual connection, but this investment is well worth it.

A VPN provider typically offers servers worldwide, each a valuable component of a VPN. Besides the encrypted tunneling protocols, some VPNs use the internet itself to establish a private connection. As for network protocols, you’ll mostly see TCP/IP, SSL, and similar types.

The importance of security and privacy on the internet can’t be understated. So, a VPN’s ability to offer you these is undoubtedly its biggest advantage. Users are also fond of VPNs for unlocking geo-blocked content and eliminating pesky targeted ads.

Following in the footsteps of other types of computer network, a VPN also has a few notable flaws. Not all devices will support this network. Even when they do, privacy and security aren’t 100% guaranteed. Just think of how fast new cybersecurity threats emerge, and you’ll understand why.

Of course, these downsides don’t prevent numerous users from reaching for VPNs to secure remote access to the internet or gain access to apps hosted on proprietary networks. Users also use these networks to bypass censorship in their country or browse the internet anonymously.

Connecting Beyond Boundaries

Whether running a global corporation or wanting to connect your smartphone to the internet, there’s a perfect network among the above-mentioned types of computer network. Understanding the unique features of each network and their specific advantages and disadvantages will help you make the right choice and enjoy seamless connections wherever you are. Compare the facts from this guide to your specific needs, and you’ll pick the perfect network every time.

Algorithms are the essence of data mining and machine learning – the two processes 60% of organizations utilize to streamline their operations. Businesses can choose from several algorithms to polish their workflows, but the decision tree algorithm might be the most common.

This algorithm is all about simplicity. It branches out in multiple directions, just like trees, and determines whether something is true or false. In turn, data scientists and machine learning professionals can further dissect the data and help key stakeholders answer various questions.

This only scratches the surface of this algorithm – but it’s time to delve deeper into the concept. Let’s take a closer look at the decision tree machine learning algorithm, its components, types, and applications.

What Is Decision Tree Machine Learning?

The decision tree algorithm in data mining and machine learning may sound relatively simple due to its similarities with standard trees. But like with conventional trees, which consist of leaves, branches, roots, and many other elements, there’s a lot to uncover with this algorithm. We’ll start by defining this concept and listing the main components.

Definition of Decision Tree

If you’re a college student, you learn in two ways – supervised and unsupervised. The same division can be found in algorithms, and the decision tree belongs to the former category. It’s a supervised algorithm you can use to regress or classify data. It relies on training data to predict values or outcomes.

Components of Decision Tree

What’s the first thing you notice when you look at a tree? If you’re like most people, it’s probably the leaves and branches.

The decision tree algorithm has the same elements. Add nodes to the equation, and you have the entire structure of this algorithm right in front of you.

• Nodes – There are several types of nodes in decision trees. The root node is the parent of all nodes, which represents the overriding message. Chance nodes tell you the probability of a certain outcome, whereas decision nodes determine the decisions you should make.
• Branches – Branches connect nodes. Like rivers flowing between two cities, they show your data flow from questions to answers.
• Leaves – Leaves are also known as end nodes. These elements indicate the outcome of your algorithm. No more nodes can spring out of these nodes. They are the cornerstone of effective decision-making.

Types of Decision Trees

When you go to a park, you may notice various tree species: birch, pine, oak, and acacia. By the same token, there are multiple types of decision tree algorithms:

• Classification Trees – These decision trees map observations about particular data by classifying them into smaller groups. The chunks allow machine learning specialists to predict certain values.
• Regression Trees – According to IBM, regression decision trees can help anticipate events by looking at input variables.

Decision Tree Algorithm in Data Mining

Knowing the definition, types, and components of decision trees is useful, but it doesn’t give you a complete picture of this concept. So, buckle your seatbelt and get ready for an in-depth overview of this algorithm.

Overview of Decision Tree Algorithms

Just as there are hierarchies in your family or business, there are hierarchies in any decision tree in data mining. Top-down arrangements start with a problem you need to solve and break it down into smaller chunks until you reach a solution. Bottom-up alternatives sort of wing it – they enable data to flow with some supervision and guide the user to results.

Popular Decision Tree Algorithms

• ID3 (Iterative Dichotomiser 3) – Developed by Ross Quinlan, the ID3 is a versatile algorithm that can solve a multitude of issues. It’s a greedy algorithm (yes, it’s OK to be greedy sometimes), meaning it selects attributes that maximize information output.
• 5 – This is another algorithm created by Ross Quinlan. It generates outcomes according to previously provided data samples. The best thing about this algorithm is that it works great with incomplete information.
• CART (Classification and Regression Trees) – This algorithm drills down on predictions. It describes how you can predict target values based on other, related information.
• CHAID (Chi-squared Automatic Interaction Detection) – If you want to check out how your variables interact with one another, you can use this algorithm. CHAID determines how variables mingle and explain particular outcomes.

Key Concepts in Decision Tree Algorithms

No discussion about decision tree algorithms is complete without looking at the most significant concept from this area:

Entropy

As previously mentioned, decision trees are like trees in many ways. Conventional trees branch out in random directions. Decision trees share this randomness, which is where entropy comes in.

Entropy tells you the degree of randomness (or surprise) of the information in your decision tree.

Information Gain

A decision tree isn’t the same before and after splitting a root node into other nodes. You can use information gain to determine how much it’s changed. This metric indicates how much your data has improved since your last split. It tells you what to do next to make better decisions.

Gini Index

Mistakes can happen, even in the most carefully designed decision tree algorithms. However, you might be able to prevent errors if you calculate their probability.

Enter the Gini index (Gini impurity). It establishes the likelihood of misclassifying an instance when choosing it randomly.

Pruning

You don’t need every branch on your apple or pear tree to get a great yield. Likewise, not all data is necessary for a decision tree algorithm. Pruning is a compression technique that allows you to get rid of this redundant information that keeps you from classifying useful data.

Building a Decision Tree in Data Mining

Growing a tree is straightforward – you plant a seed and water it until it is fully formed. Creating a decision tree is simpler than some other algorithms, but quite a few steps are involved nevertheless.

Data Preparation

Data preparation might be the most important step in creating a decision tree. It’s comprised of three critical operations:

Data Cleaning

Data cleaning is the process of removing unwanted or unnecessary information from your decision trees. It’s similar to pruning, but unlike pruning, it’s essential to the performance of your algorithm. It’s also comprised of several steps, such as normalization, standardization, and imputation.

Feature Selection

Time is money, which especially applies to decision trees. That’s why you need to incorporate feature selection into your building process. It boils down to choosing only those features that are relevant to your data set, depending on the original issue.

Data Splitting

The procedure of splitting your tree nodes into sub-nodes is known as data splitting. Once you split data, you get two data points. One evaluates your information, while the other trains it, which brings us to the next step.

Training the Decision Tree

Now it’s time to train your decision tree. In other words, you need to teach your model how to make predictions by selecting an algorithm, setting parameters, and fitting your model.

Selecting the Best Algorithm

There’s no one-size-fits-all solution when designing decision trees. Users select an algorithm that works best for their application. For example, the Random Forest algorithm is the go-to choice for many companies because it can combine multiple decision trees.

Setting Parameters

How far your tree goes is just one of the parameters you need to set. You also need to choose between entropy and Gini values, set the number of samples when splitting nodes, establish your randomness, and adjust many other aspects.

Fitting the Model

If you’ve fitted your model properly, your data will be more accurate. The outcomes need to match the labeled data closely (but not too close to avoid overfitting) if you want relevant insights to improve your decision-making.

Evaluating the Decision Tree

Don’t put your feet up just yet. Your decision tree might be up and running, but how well does it perform? There are two ways to answer this question: cross-validation and performance metrics.

Cross-Validation

Cross-validation is one of the most common ways of gauging the efficacy of your decision trees. It compares your model to training data, allowing you to determine how well your system generalizes.

Performance Metrics

Several metrics can be used to assess the performance of your decision trees:

Accuracy

This is the proximity of your measurements to the requested values. If your model is accurate, it matches the values established in the training data.

Precision

By contrast, precision tells you how close your output values are to each other. In other words, it shows you how harmonized individual values are.

Recall

Recall is the number of data samples in the desired class. This class is also known as the positive class. Naturally, you want your recall to be as high as possible.

F1 Score

F1 score is the median value of your precision and recall. Most professionals consider an F1 of over 0.9 a very good score. Scores between 0.8 and 0.5 are OK, but anything less than 0.5 is bad. If you get a poor score, it means your data sets are imprecise and imbalanced.

Visualizing the Decision Tree

The final step is to visualize your decision tree. In this stage, you shed light on your findings and make them digestible for non-technical team members using charts or other common methods.

Applications of Decision Tree Machine Learning in Data Mining

The interest in machine learning is on the rise. One of the reasons is that you can apply decision trees in virtually any field:

• Customer Segmentation – Decision trees let you divide customers according to age, gender, or other factors.
• Fraud Detection – Decision trees can easily find fraudulent transactions.
• Medical Diagnosis – This algorithm allows you to classify conditions and other medical data with ease using decision trees.
• Risk Assessment – You can use the system to figure out how much money you stand to lose if you pursue a certain path.
• Recommender Systems – Decision trees help customers find their next product through classification.

Advantages and Disadvantages of Decision Tree Machine Learning

Advantages:

• Easy to Understand and Interpret – Decision trees make decisions almost in the same manner as humans.
• Handles Both Numerical and Categorical Data – The ability to handle different types of data makes them highly versatile.
• Requires Minimal Data Preprocessing – Preparing data for your algorithms doesn’t take much.

Disadvantages:

• Prone to Overfitting – Decision trees often fail to generalize.
• Sensitive to Small Changes in Data – Changing one data point can wreak havoc on the rest of the algorithm.
• May Not Work Well with Large Datasets – Naïve Bayes and some other algorithms outperform decision trees when it comes to large datasets.

Possibilities are Endless With Decision Trees

The decision tree machine learning algorithm is a simple yet powerful algorithm for classifying or regressing data. The convenient structure is perfect for decision-making, as it organizes information in an accessible format. As such, it’s ideal for making data-driven decisions.

If you want to learn more about this fascinating topic, don’t stop your exploration here. Decision tree courses and other resources can bring you one step closer to applying decision trees to your work.

Think for a second about employees in diamond mines. Their job can often seem like trying to find a needle in a haystack. But once they find what they’re looking for, the feeling of accomplishment is overwhelming.

The situation is similar with data mining. Granted, you’re not on the hunt for diamonds (although that wouldn’t be so bad). The concept’s name may suggest otherwise, but data mining isn’t about extracting data. What you’re mining are patterns; you analyze datasets and try to see whether there’s a trend.

Data mining doesn’t involve you reading thousands of pages. This process is automatic (or at least semi-automatic). The patterns discovered with data mining are often seen as input data, meaning it’s used for further analysis and research. Data mining has become a vital part of machine learning and artificial intelligence as a whole. If you think this is too abstract and complex, you should know that data mining has found its purpose for every company. Investigating trends, prices, sales, and customer behavior is important for any business that sells products or services.

In this article, we’ll cover different data mining techniques and explain the entire process in more detail.

Data Mining Techniques

Here are the most popular data mining techniques.

Classification

As you can assume, this technique classifies something (datasets). Through classification, you can organize vast datasets into clear categories and turn them into classifiers (models) for further analysis.

Clustering

In this case, data is divided into clusters according to a certain criterion. Each cluster should contain similar data points that differ from data points in other clusters.

If we look at clustering from the perspective of artificial intelligence, we say it’s an unsupervised algorithm. This means that human involvement isn’t necessary for the algorithm to discover common features and group data points according to them.

Association Rule Learning

This technique discovers interesting connections and associations in large datasets. It’s pretty common in sales, where companies use it to explore customers’ behaviors and relationships between different products.

Regression

This technique is based on the principle that the past can help you understand the future. It explores patterns in past data to make assumptions about the future and make new observations.

Anomaly Detection

This is pretty self-explanatory. Here, datasets are analyzed to identify “ugly ducklings,” i.e., unusual patterns or patterns that deviate from the standard.

Sequential Pattern Mining

With this technique, you’re also on the hunt for patterns. The “sequential” indicates that you’re analyzing data where the values are in a sequence.

Text Mining

Text mining involves analyzing unstructured text, turning it into a structured format, and checking for patterns.

Sentiment Analysis

This data mining technique is also called opinion mining, and it’s very different from the methods discussed above. This complex technique involves natural language processing, linguistics, and speech analysis and wants to discover the emotional tone in a text.

Data Mining Process

Regardless of the technique you’re using, the data process consists of several stages that ensure accuracy, efficiency, and reliability.

Data Collection

As mentioned, data mining isn’t actually about identifying data but about exploring patterns within the data. To do that, you obviously need a dataset you want to analyze. The data needs to be relevant, otherwise you won’t get accurate results.

Data Preprocessing

Whether you’re analyzing a small or large dataset, the data within it could be in different formats or have inconsistencies or errors. If you want to analyze it properly, you need to ensure the data is uniform and organized, meaning you need to preprocess it.

This stage involves several processes:

• Data cleaning
• Data transformation
• Data reduction

Once you complete them, your data will be prepared for analysis.

Data Analysis

You’ve come to the “main” part of the data mining process, which consists of two elements:

• Model building
• Model evaluation

Model building represents determining the most efficient ways to analyze the data and identify patterns. Think of it this way: you’re asking questions, and the model should be able to provide the correct answers.

The next step is model evaluation, where you’ll step back and think about the model. Is it the right fit for your data, and does it meet your criteria?

Interpretation and Visualization

The journey doesn’t end after the analysis. Now it’s time to review the results and come to relevant conclusions. You’ll also need to present these conclusions in the best way possible, especially if you conducted the analysis for someone else. You want to ensure that the end-user understands what was done and what was discovered in the process.

Deployment and Integration

You’ve conducted the analysis, interpreted the results, and now you understand what needs to be changed. You’ll use the knowledge you’ve gained to elicit changes.

For example, you’ve analyzed your customers’ behaviors to understand why the sales of a specific product dropped. The results showed that people under the age of 30 don’t buy it as often as they used to. Now, you face two choices: You can either advertise the product and focus on the particular age group or attract even more people over the age of 30 if that makes more sense.

Applications of Data Mining

The concept of data mining may sound too abstract. However, it’s all around us. The process has proven invaluable in many spheres, from sales to healthcare and finance.

Here are the most common applications of data mining.

Customer Relationship Management

Your customers are the most important part of your business. After all, if it weren’t for them, your company wouldn’t have anyone to sell the products/services to. Yes, the quality of your products is one way to attract and keep your customers. But quality won’t be enough if you don’t value your customers.

Whether they’re buying a product for the first or the 100th time, your customers want to know you want to keep them. Some ways to do so are discounts, sales, and loyalty programs. Coming up with the best strategy can be challenging to say the least, especially if you have many customers belonging to different age groups, gender, and spending habits. With data mining, you can group your customers according to specific criteria and offer them deals that suit them perfectly.

Fraud Detection

In this case, you analyze data not to find patterns but to find something that stands out. This is what banks do to ensure no unwanted guests are accessing your account. But you can also see this fraud detection in the business world. Many companies use it to identify and remove fake accounts.

Market Basket Analysis

With data mining, you can get answers to an important question: “Which items are often bought together?” If this is on your mind, data mining can help. You can perform the association technique to discover the patterns (for example, milk and cereal) and use this valuable intel to offer your customers top-notch recommendations.

Healthcare and Medical Research

The healthcare industry has benefited immensely from data mining. The process is used to improve decision-making, generate conclusions, and check whether a treatment is working. Thanks to data mining, diagnoses have become more precise, and patients get more quality services.

As medical research and drug testing are large parts of moving the entire industry forward, data mining found its role here, too. It’s used to keep track of and reduce the risk of side effects of different medications and assist in administration.

Social Media Analysis

This is definitely one of the most lucrative applications. Social media platforms rely on it to pick up more information about their users to offer them relevant content. Thanks to this, people who use the same network will often see completely different posts. Let’s say you love dogs and often watch videos about them. The social network you’re on will recognize this and offer you even more dog videos. If you’re a cat person and avoid dog videos at all costs, the algorithm will “understand” this and offer you more videos starring cats.

Finance and Banking

Data mining analyzes markets to discover hidden patterns and make accurate predictions. The process is also used to check a company’s health and see what can be improved.

In banking, data mining is used to detect unusual transactions and prevent unauthorized access and theft. It can analyze clients and determine whether they’re suitable for loans (whether they can pay them back).

Challenges and Ethical Considerations of Data Mining

While it has many benefits, data mining faces different challenges:

• Privacy concerns – During the data mining process, sensitive and private information about users can come to light, thus jeopardizing their privacy.
• Data security – The world’s hungry for knowledge, and more and more data is getting collected and analyzed. There’s always a risk of data breaches that could affect millions of people worldwide.
• Bias and discrimination – Like humans, algorithms can be biased, but only if the sample data leads them toward such behavior. You can prevent this with precise data collection and preprocessing.
• Legal and regulatory compliance – Data mining needs to be conducted according to the letter of the law. If that’s not the case, the users’ privacy and your company’s reputation are at stake.

Track Trends With Data Mining

If you feel lost and have no idea what your next step should be, data mining can be your life support. With it, you can make informed decisions that will drive your company forward.

Considering its benefits, data mining will continue to be an invaluable tool in many niches.

When you’re faced with a task, you often wish you had the help of a friend. As they say, two heads are better than one, and collaboration can be the key to solving a problem or overcoming a challenge. With computer networks, we can say two nodes are better than one. These unique environments consist of at least two interconnected nodes that share and exchange data and resources, for which they use specific rules called “communications protocols.” Every node has its position within the network and a name and address to identify it.

The possibilities of computer networks are difficult to grasp. They make transferring files and communicating with others on the same network a breeze. The networks also boost storage capacity and provide you with more leeway to meet your goals.

One node can be powerful, but a computer network with several nodes can be like a super-computer capable of completing challenging tasks in record times.

In this introduction to computer networks, we’ll discuss the different types in detail. We’ll also tackle their applications and components and talk more about network topologies, protocols, and security.

Components of a Computer Network

Let’s start with computer network basics. A computer network is comprised of components that it can’t function without. These components can be divided into hardware and software. The easiest way to remember the difference between the two is to know that software is something “invisible,” i.e., stored inside a device. Hardware components are physical objects we can touch.

Hardware Components

• Network interface cards (NICs) – This is the magic part that connects a computer to a network or another computer. There are wired and wireless NICs. Wired NICs are inside the motherboard and connect to cables to transfer data, while wireless NICs have an antenna that connects to a network.
• Switches – A switch is a type of mediator. It’s the component that connects several devices to a network. This is what you’ll use to send a direct message to a specific device instead of the entire network.
• Routers – This is the device that uses an internet connection to connect to a local area network (LAN). It’s like a traffic officer who controls and directs data packets to networks.
• Hubs – This handy component divides a network connection into multiple computers. This is the distribution center that receives information requests from a computer and places the information to the entire network.
• Cables and connectors – Different types of cables and connectors are required to keep the network operating.

Software Components

• Network operating system (NOS) – A NOS is usually installed on the server. It creates an adequate environment for sharing and transmitting files, applications, and databases between computers.
• Network protocols – Computers interpret network protocols as guidelines for data communication.
• Network services – They serve as bridges that connect users to the apps or data on a specific network.

Types of Computer Networks

Local Area Network (LAN)

This is a small, limited-capacity network you’ll typically see in small companies, schools, labs, or homes. LANs can also be used as test networks for troubleshooting or modeling.

The main advantage of a local area network is convenience. Besides being easy to set up, a LAN is affordable and offers decent speed. The obvious drawback is its limited size.

Wide Area Network (WAN)

In many aspects, a WAN is similar to a LAN. The crucial difference is the size. As its name indicates, a WAN can cover a large space and can “accept” more users. If you have a large company and want to connect your in-office and remote employees, data centers, and suppliers, you need a WAN.

These networks cover huge areas and stretch across the globe. We can say that the internet is a type of a WAN, which gives you a good idea of how much space it covers.

The bigger size comes at a cost. Wide area networks are more complex to set up and manage and cost more money to operate.

Metropolitan Area Network (MAN)

A metropolitan area network is just like a local area network but on a much bigger scale. This network covers entire cities. A MAN is the golden middle; it’s bigger than a LAN but smaller than a WAN. Cable TV networks are the perfect representatives of metropolitan area networks.

A MAN has a decent size and good security and provides the perfect foundation for a larger network. It’s efficient, cost-effective, and relatively easy to work with.

As far as the drawbacks go, you should know that setting up the network can be complex and require the help of professional technicians. Plus, a MAN can suffer from slower speed, especially during peak hours.

Personal Area Network (PAN)

If you want to connect your technology devices and know nobody else will be using your network, a PAN is the way to go. This network is smaller than a LAN and can interconnect devices in your proximity (the average range is about 33 feet).

A PAN is simple to install and use and doesn’t have components that can take up extra space. Plus, the network is convenient, as you can move it around without losing connection. Some drawbacks are the limited range and slower data transfer.

These days, you encounter PANs on a daily basis: smartphones, gaming consoles, wireless keyboards, and TV remotes are well-known examples.

Network Topologies

Network topologies represent ways in which elements of a computer network are arranged and related to each other. Here are the five basic types:

• Bus topology – In this case, all network devices and computers connect to only one cable.
• Star topology – Here, all eyes are on the hub, as that is where all devices “meet.” In this topology, you don’t have a direct connection between the devices; the hub acts as a mediator.
• Ring topology – Device connections create a ring; the last device is connected to the first, thus forming a circle.
• Mesh topology – In this topology, all devices belonging to a network are interconnected, making data sharing a breeze.
• Hybrid topology – As you can assume, this is a mix of two or more topologies.

Network Protocols

Network protocols determine how a device connected to a network communicates and exchanges information. There are the five most common types:

• Transmission Control Protocol/Internet Protocol (TCP/IP) – A communication protocol that interconnects devices to a network and lets them send/receive data.
• Hypertext Transfer Protocol (HTTP) – This application layer protocol transfers hypertext and lets users communicate data across the World Wide Web (www).
• File Transfer Protocol (FTP) – It’s used for transferring files (documents, multimedia, texts, programs, etc.)
• Simple Mail Transfer Protocol (SMTP) – It transmits electronic mails (e-mails).
• Domain Name System (DNS) – It converts domain names to IP addresses through which computers and devices are identified on a network.

Network Security

Computer networks are often used to transfer and share sensitive data. Without adequate network security, this data could end up in the wrong hands, not to mention that numerous threats could jeopardize the network’s health.

Here are the types of threats you should be on the lookout for:

• Viruses and malware – These can make your network “sick.” When they penetrate a system, viruses and malware replicate themselves, eliminating the “good” code.
• Unauthorized access – These are guests who want to come into your house, but you don’t want to let them in.
• Denial of service attacks – These dangerous attacks have only one goal: making the network inaccessible to the users (you). If you’re running a business, these attacks will also prevent your customers from accessing the website, which can harm your company’s reputation and revenue.

What can you do to keep your network safe? These are the best security measures:

• Firewalls – A firewall acts as your network’s surveillance system. It uses specific security rules as guidelines for monitoring the traffic and spotting untrusted networks.
• Intrusion detection systems – These systems also monitor your network and report suspicious activity to the administrator or collect the information centrally.
• Encryption – This is the process of converting regular text to ciphertext. Such text is virtually unusable to everyone except authorized personnel who have the key to access the original data.
• Virtual private networks (VPNs) – These networks are like magical portals that guarantee safe and private connections thanks to encrypted tunnels. They mask your IP address, meaning nobody can tell your real location.
• Regular updates and patches – These add top-notch security features to your network and remove outdated features at the same time. By not updating your network, you make it more vulnerable to threats.

Reap the Benefits of Computer Networks

Whether you need a network for a few personal devices or want to connect with hundreds of employees and suppliers, computer networks have many uses and benefits. They take data sharing, efficiency, and accessibility to a new level.

If you want your computer network to function flawlessly, you need to take good care of it, no matter its size. This means staying in the loop about the latest industry trends. We can expect to see more AI in computer networking, as it will only make them even more beneficial.

Data is the heartbeat of the digital realm. And when something is so important, you want to ensure you deal with it properly. That’s where data structures come into play.

But what is data structure exactly?

In the simplest terms, a data structure is a way of organizing data on a computing machine so that you can access and update it as quickly and efficiently as possible. For those looking for a more detailed data structure definition, we must add processing, retrieving, and storing data to the purposes of this specialized format.

With this in mind, the importance of data structures becomes quite clear. Neither humans nor machines could access or use digital data without these structures.

But using data structures isn’t enough on its own. You must also use the right data structure for your needs.

This article will guide you through the most common types of data structures, explain the relationship between data structures and algorithms, and showcase some real-world applications of these structures.

Armed with this invaluable knowledge, choosing the right data structure will be a breeze.

Types of Data Structures

Like data, data structures have specific characteristics, features, and applications. These are the factors that primarily dictate which data structure should be used in which scenario. Below are the most common types of data structures and their applications.

Primitive Data Structures

Take one look at the name of this data type, and its structure won’t surprise you. Primitive data structures are to data what cells are to a human body – building blocks. As such, they hold a single value and are typically built into programming languages. Whether you check data structures in C or data structures in Java, these are the types of data structures you’ll find.

• Integer (signed or unsigned) – Representing whole numbers
• Float (floating-point numbers) – Representing real numbers with decimal precision
• Character – Representing integer values as symbols
• Boolean – Storing true or false logical values

Non-Primitive Data Structures

Combine primitive data structures, and you get non-primitive data structures. These structures can be further divided into two types.

Linear Data Structures

As the name implies, a linear data structure arranges the data elements linearly (sequentially). In this structure, each element is attached to its predecessor and successor.

The most commonly used linear data structures (and their real-life applications) include the following:

• In arrays, multiple elements of the same type are stored together in the same location. As a result, they can all be processed relatively quickly. (library management systems, ticket booking systems, mobile phone contacts, etc.)
• Linked lists. With linked lists, elements aren’t stored at adjacent memory locations. Instead, the elements are linked with pointers indicating the next element in the sequence. (music playlists, social media feeds, etc.)
• These data structures follow the Last-In-First-Out (LIFO) sequencing order. As a result, you can only enter or retrieve data from one stack end (browsing history, undo operations in word processors, etc.)
• Queues follow the First-In-First-Out (FIFO) sequencing order (website traffic, printer task scheduling, video queues, etc.)

Non-Linear Data Structures

A non-linear data structure also has a pretty self-explanatory name. The elements aren’t placed linearly. This also means you can’t traverse all of them in a single run.

• Trees are tree-like (no surprise there!) hierarchical data structures. These structures consist of nodes, each filled with specific data (routers in computer networks, database indexing, etc.)
• Combine vertices (or nodes) and edges, and you get a graph. These data structures are used to solve the most challenging programming problems (modeling, computation flow, etc.)

Advanced Data Structures

Venture beyond primitive data structures (building blocks for data structures) and basic non-primitive data structures (building blocks for more sophisticated applications), and you’ll reach advanced data structures.

• Hash tables. These advanced data structures use hash functions to store data associatively (through key-value pairs). Using the associated values, you can quickly access the desired data (dictionaries, browser searching, etc.)
• Heaps are specialized tree-like data structures that satisfy the heap property (every tree element is larger than its descendant.)
• Tries store strings that can be organized in a visual graph and retrieved when necessary (auto-complete function, spell checkers, etc.)

Algorithms for Data Structures

There is a common misconception that data structures and algorithms in Java and other programming languages are one and the same. In reality, algorithms are steps used to structure data and solve other problems. Check out our overview of some basic algorithms for data structures.

Searching Algorithms

Searching algorithms are used to locate specific elements within data structures. Whether you’re searching for specific data structures in C++ or another programming language, you can use two types of algorithms:

• Linear search: starts from one end and checks each sequential element until the desired element is located
• Binary search: looks for the desired element in the middle of a sorted list of items (If the elements aren’t sorted, you must do that before a binary search.)

Sorting Algorithms

Whenever you need to arrange elements in a specific order, you’ll need sorting algorithms.

• Bubble sort: Compares two adjacent elements and swaps them if they’re in the wrong order
• Selection sort: Sorts lists by identifying the smallest element and placing it at the beginning of the unsorted list
• Insertion sort: Inserts the unsorted element in the correct position straight away
• Merge sort: Divides unsorted lists into smaller sections and orders each separately (the so-called divide-and-conquer principle)
• Quick sort: Also relies on the divide-and-conquer principle but employs a pivot element to partition the list (elements smaller than the pivot element go back, while larger ones are kept on the right)

Tree Traversal Algorithms

To traverse a tree means to visit its every node. Since trees aren’t linear data structures, there’s more than one way to traverse them.

• Pre-order traversal: Visits the root node first (the topmost node in a tree), followed by the left and finally the right subtree
• In-order traversal: Starts with the left subtree, moves to the root node, and ends with the right subtree
• Post-order traversal: Visits the nodes in the following order: left subtree, right subtree, the root node

Graph Traversal Algorithms

Graph traversal algorithms traverse all the vertices (or nodes) and edges in a graph. You can choose between two:

• Depth-first search – Focuses on visiting all the vertices or nodes of a graph data structure located one above the other
• Breadth-first search – Traverses the adjacent nodes of a graph before moving outwards

Applications of Data Structures

Data structures are critical for managing data. So, no wonder their extensive list of applications keeps growing virtually every day. Check out some of the most popular applications data structures have nowadays.

Data Organization and Storage

With this application, data structures return to their roots: they’re used to arrange and store data most efficiently.

Database Management Systems

Database management systems are software programs used to define, store, manipulate, and protect data in a single location. These systems have several components, each relying on data structures to handle records to some extent.

Let’s take a library management system as an example. Data structures are used every step of the way, from indexing books (based on the author’s name, the book’s title, genre, etc.) to storing e-books.

File Systems

File systems use specific data structures to represent information, allocate it to the memory, and manage it afterward.

Data Retrieval and Processing

With data structures, data isn’t stored and then forgotten. It can also be retrieved and processed as necessary.

Search Engines

Search engines (Google, Bing, Yahoo, etc.) are arguably the most widely used applications of data structures. Thanks to structures like tries and hash tables, search engines can successfully index web pages and retrieve the information internet users seek.

Data Compression

Data compression aims to accurately represent data using the smallest storage amount possible. But without data structures, there wouldn’t be data compression algorithms.

Data Encryption

Data encryption is crucial for preserving data confidentiality. And do you know what’s crucial for supporting cryptography algorithms? That’s right, data structures. Once the data is encrypted, data structures like hash tables also aid with value key storage.

Problem Solving and Optimization

At their core, data structures are designed for optimizing data and solving specific problems (both simple and complex). Throw their composition into the mix, and you’ll understand why these structures have been embraced by fields that heavily rely on mathematics and algorithms for problem-solving.

Artificial Intelligence

Artificial intelligence (AI) is all about data. For machines to be able to use this data, it must be properly stored and organized. Enter data structures.

Arrays, linked lists, queues, graphs, and stacks are just some structures used to store data for AI purposes.

Machine Learning

Data structures used for machine learning (MI) are pretty similar to other computer science fields, including AI. In machine learning, data structures (both linear and non-linear) are used to solve complex mathematical problems, manipulate data, and implement ML models.

Network Routing

Network routing refers to establishing paths through one or more internet networks. Various routing algorithms are used for this purpose and most heavily rely on data structures to find the best patch for the incoming data packet.

Data Structures: The Backbone of Efficiency

Data structures are critical in our data-driven world. They allow straightforward data representation, access, and manipulation, even in giant databases. For this reason, learning about data structures and algorithms further can open up a world of possibilities for a career in data science and related fields.