With your BSc in Computer Science achieved, you have a ton of technical knowledge in coding, systems architecture, and the general “whys” and “hows” of computing under your belt. Now, you face a dilemma, as you’re entering a field that over 150,000 people study for per year, meaning competition is rife.

That huge level of competition makes finding a new career difficult, as UK-based computer science graduates discovered in the mid-2010s when the saturation of the market led to an 11% unemployment rate. To counter that saturation, you may find the siren’s call of the business world tempts you toward continuing your studies to obtain an MBA.

So, the question is – can I do MBA after Computer Science?

This article offers the answers.

Understanding the MBA Degree

MBAs exist to equip students with the knowledge (both technical and practical) to succeed in the business world. For computer science graduates, that may mean giving them the networking and soft skills they need to turn their technical knowledge into career goldmines, or it could mean helping them to start their own companies in the computing field.

Most MBAs feature six core subjects:

• Finance – Focused on the numbers behind a business, this subject is all about learning how to balance profits, losses, and the general costs of running a business.
• Accounting – Building on the finance subject, accounting pulls students into the weeds when it comes to taxes, operating expenses, and running a healthy company.
• Leadership – Soft skills are just as important as hard skills to a business student, with leadership subjects focusing on how to inspire employees and foster teamwork.
• Economic Statistics – The subject that most closely relates to a computer science degree, economic statistics is all about processing, collecting, and interpreting technical data.
• Accountability/Ethics – With so many fields having strict compliance criteria (coupled with the ethical conundrums that arise in any business), this subject helps students navigate potential legal and ethical minefields.
• Marketing – Having a great product or service doesn’t always lead to business success. Marketing covers what you do to get what you have to offer into the public eye.

Beyond the six core subjects, many MBAs offer students an opportunity to specialize via additional courses in the areas that interest them most. For instance, you could take courses in entrepreneurship to bolster your leadership skills and ethical knowledge, or focus on accounting if you’re more interested in the behind-the-scenes workings of the business world.

As for career opportunities, you have a ton of paths you can follow (with your computer science degree offering more specialized career routes). Those with an MBA alone have options in the finance, executive management, and consulting fields, with more specialized roles in IT management available to those with computer science backgrounds.

Eligibility for MBA After BSc Computer Science

MBAs are attractive to prospective post-graduate students because they have fairly loose requirements, at least when compared to more specialized further studies. Most MBA courses require the following before they’ll accept a student:

• A Bachelor’s degree in any subject, as long as that degree comes from a recognized educational institution
• English language proficiency
  • This is often tested using either the TOEFL or IELTS tests
• A pair of recommendation letters, which can come from employers or past teachers
• Your statement of purpose defining why you want to study for an MBA
• A resume
• A Graduate Management Admissions Test (GMAT) score
  • You’ll receive a score between 200 and 800, with the aim being to exceed the average of 574.51

Interestingly, some universities offer MBAs in Computer Science, which are the ideal transitional courses for those who are wary of making the jump from a more technical field into something business-focused. Course requirements are similar to those for a standard MBA, though some universities also like to see that you have a couple of years of work experience before you apply.

Benefits of Pursuing an MBA After BSc Computer Science

So, the answer to “Can I do MBA after BSc Computer Science,” is a resounding “yes,” but we still haven’t confronted why that’s a good choice. Here are five reasons:

• Diversify your skill set – While your skill set after completing a computer science degree is extremely technical, you may not have many of the soft skills needed to operate in a business environment. Beyond teaching leadership, management, and teamwork, a good MBA program also helps you get to grips with the numbers behind a business.
• Expand career opportunities – There is no shortage of potential roles for computer science graduates, though the previously mentioned study data shows there are many thousands of people studying the same subject. With an MBA to complement your knowledge of computers, you open the door to career opportunities in management fields that would otherwise not be open to you.
• Enhance leadership and management skills – Computer science can often feel like a solitary pursuit, as you spend more time behind a keyboard than you do interacting with others. MBAs are great for those who need a helping hand with their communication skills. Plus, they’re ideal for teaching the organizational aspects of running (or managing) a business.
• Potential for higher salary and career growth – According to Indeed, the average salary in the computer science field is $103,719. Figures from Seattle University suggest those with MBAs can far exceed that average, with the figures it quotes from the industry journal Poets and Quants suggesting an average MBA salary of $140,924.

Challenges and Considerations

As loose as the academic requirements for being accepted to an MBA may be (at least compared to other subjects), there are still challenges to confront as a computer science graduate or student.

• The time and financial investments – Forbes reports the average cost of an MBA in the United States to be $61,800. When added to the cost of your BSc in Computer Science, it’s possible you’ll face near-six-figure debt upon graduating. Couple that monetary investment with the time taken to get your MBA (it’s a full-time course) and you may have to put more into your studies than you think.
• Balancing your technical and managerial skills – Computer science focuses on the technical side, which is only one part of an MBA. While the skills you have will come to the fore when you study accounting or economic statistics, the people-focused aspects of an MBA may be a challenge.
• Adjusting to a new academic environment – You’re switching focus from the computer screen to a more classroom-led learning environment. Some may find this a challenge, particularly if they appreciate the less social aspects of computer science.

MBA Over Science – The Thomas Henson Story

After completing his Bachelor’s degree in computer information systems, Thomas Henson faced a choice – start a Master’s degree in science or study for his MBA. Having worked as a software engineer for six months following his graduation, he wanted to act fast to get his Masters’s done and dusted, opening up new career opportunities in the process.

Eventually, he chose an MBA and now works as a senior software engineer specializing in the Hortonworks Data Platform. On his personal blog, he shares why he chose an MBA over a Master’s degree in computer science, with his insights possibly helping others make their own choice:

• Listen to the people around you (especially teachers and mentors) and ask them why they’ve chosen their career and study paths.
• Compare programs (both comparing MBAs against one another and comparing MBAs to other post-graduate degrees) to see which courses serve your future ambitions best.
• Follow your passion (James loved accounting) as the most important thing is not necessarily the post-graduate course you take. The most important thing is that you finish.

Choosing the Right MBA Program

Finding the right MBA program means taking several factors into consideration, with the following four being the most important:

• Reputation and accreditation – The reputation of the institution you choose, as well as the accreditation it holds, plays a huge role in your decision. Think of your MBA as a recommendation. That recommendation doesn’t mean much if it comes from a random person in the street (i.e., an institution nobody knows), but it carries a lot of weight if it comes from somebody respected.
• Curriculum and specialization – As Thomas Henson points out, what drives you most is what will lead you to the right MBA. In his case, he loved accounting enough to make an MBA a possibility, and likely pursued specializations in that area. Ask yourself what you specifically aim to achieve with your MBA and look for courses that move you closer to that goal.
• Networking opportunities – As anybody in the business world will tell you, who you know is often as important as what you know. Look for a course that features respected lecturers and professors, as they have connections that you can exploit, and take advantage of any opportunities to go to networking events or join professional associations.
• Financial aid and scholarships – Your access to financial aid depends on your current financial position, meaning it isn’t always available. Scholarships may be more accessible, with major institutions like Harvard and Columbia Business School offering pathways into their courses for those who meet their scholarship requirements.

Speaking of Harvard and Columbia, it’s also a good idea to research some of the top business schools, especially given that the reputation of your school is as important as the degree you earn. Major players, at least in the United States, include:

• Harvard Business School
• Columbia Business School
• Wharton School of Business
• Yale School of Management
• Stanford Graduate School of Business

Become a Business-Minded Computer Buff

With the technical skills you earned from your BSc in Computer Science, you’ll be happy to find that the answer to “Can I do MBA after BSc Computer Science?” is “Yes.” Furthermore, it’s recommended as an MBA can equip you with soft skills, such as communication and leadership, that you may not receive from your computing studies. Ultimately, the combination of tech-centric and business skills opens the door to new career paths, with the average earnings of an MBA student outclassing those of computer science graduates.

Your choice comes down to your passion and the career you wish to pursue. If management doesn’t appeal to you, an MBA is likely a waste of time (and over $60,000), whereas those who want to apply their tech skills to the business world will get a lot more out of an MBA.

The future looks bright for the data science sector, with the U.S. Bureau of Labor Statistics stating that there were 113,300 jobs in the industry in 2021. Growth is also a major plus. The same resource estimates a 36% increase in data scientist roles between 2021 and 2031, which outpaces the national average considerably. Combine that with attractive salaries (Indeed says the average salary for a data scientist is $130,556) and you have an industry that’s ready and waiting for new talent.

That’s where you come in, as you’re exploring the possibilities in data science and need to find the appropriate educational tools to help you enter the field. A Master’s degree may be a good choice, leading to the obvious question – do you need a Master’s for data science?

The Value of a Masters in Data Science

There’s plenty of value to committing the time (and money) to earning your data science Master’s degree:

• In-depth knowledge and skills – A Master’s degree is a structured course that puts you in front of some of the leading minds in the field. You’ll develop very specific skills (most applying to the working world) and can access huge wellsprings of knowledge in the forms of your professors and their resources.
• Networking opportunities – Access to professors (and similar professionals) enables you to build connections with people who can give you a leg up when you enter the working world. You’ll also work with other students, with your peers offering as much potential for startup ideas and new roles as your professors.
• Increased job opportunities – With salaries in the $130,000 range, there’s clearly plenty of potential for a comfortable career pursuing a subject that you love. Having a Master’s degree in data science on your resume demonstrates that you’ve reached a certain skill threshold for employers, making them more likely to hire you.

Having said all of that, the answer to “do I need a Master’s for data science?” is “not necessarily.” There are actually some downsides to going down the formal studying route:

• The time commitment – Data science programs vary in length, though you can expect to commit at least 12 months of your life to your studies. Most courses require about two years of full-time study, which is a substantial time commitment given that you’ve already earned a degree and have job opportunities waiting.
• Your financial investment – A Master’s in data science can cost anywhere between about $10,000 for an online course to over $50,000 for courses from more prestigious institutions. For instance, Tufts University’s course requires a total investment of $54,304 if you wish to complete all of your credit hours.
• Opportunity cost – When opportunity beckons, committing two more years to your studies may lead to you missing out. Say a friend has a great idea for a startup, or you’re offered a role at a prestigious company after completing your undergraduate studies. Saying “no” to those opportunities may come back to bite you if they’re not waiting for you when you complete your Master’s degree.

Alternatives to a Masters in Data Science

If spending time and money on earning a Master’s degree isn’t to your liking, there are some alternative ways to develop data science skills.

Self-Learning and Online Resources

With the web offering a world of information at your fingertips, self-learning is a viable option (assuming you get something to show for it). Options include the following:

• Online courses and tutorials – The ability to learn at your own pace, rather than being tied into a multi-year degree, is the key benefit of online courses and tutorials. Some prestigious universities (including MIT and Harvard) even offer more bite-sized ways to get into data science. Reputation (both for the course and its providers) can be a problem, though, as some employers prefer candidates with more formal educations.
• Books and articles – The seemingly old-school method of book learning can take you far when it comes to learning about the ins and outs of data science. While published books help with theory, articles can keep you abreast of the latest developments in the field. Unfortunately, listing a bunch of books and articles that you’ve read on a resume isn’t the same as having a formal qualification.
• Data science competitions – Several organizations (such as Kaggle) offer data science competitions designed to test your skills. In addition to giving you the opportunity to wield your growing skillset, these competitions come with the dual benefits of prestige and prizes.

Bootcamps and Certificate Programs

Like the previously mentioned competitions, bootcamps offer intensive tests of your data science skills, with the added bonus of a job waiting for you at the end (in some cases). Think of them like cramming for an exam – you do a lot in a short time (often a few months) to get a reward at the end.

The prospect of landing a job after completing a bootcamp is great, but the study methods aren’t for everybody. If you thrive in a slower-paced environment, particularly one that allows you to expand your skillset gradually, an intensive bootcamp may be intimidating and counter to your educational needs.

Gaining Experience Through Internships and Entry-Level Positions

Any recent graduate who’s seen a job listing that asks for a degree and several years of experience can tell you how much employers value hands-on experience. That’s as true in data science as it is in any other field, which is where internships come in. An internship is an unpaid position (often with a prestigious company) that’s ideal for learning the workplace ropes and forming connections with people who can help you advance your career.

If an internship sounds right for you, consider these tips that may make them easier to find:

• Check the job posting platforms – The likes of Indeed and LinkedIn are great places to find companies (and the people within them) who may offer internships. There are also intern-dedicated websites, such as internships.com, which focus specifically on this type of employment.
• Meet the basic requirements – Most internships don’t require you to have formal qualifications, such as a Master’s degree, to apply. But by the same token, companies won’t accept you for a data science internship if you have no experience with computers. A solid understanding of major programming and scripting languages, such as Java, SQL, and C++, gives you a major head start. You’ve also got a better chance of landing a role if you enrolled in an undergraduate program (or have completed one) in computer science, math, or a similar field.
• Check individual business websites – Not all companies run to LinkedIn or job posting sites when they advertise vacant positions. Some put those roles on their own websites, meaning a little more in-depth searching can pay off. Create a list of companies that you believe you’d enjoy working for and check their business websites to see if they’re offering internships via their sites.

Factors to Consider When Deciding if a Masters Is Necessary

You know that the answer to “Do you need a Master’s for data science?” is “no,” but there are downsides to the alternatives. Being able to prove your skills on a resume is a must, which the self-learning route doesn’t always provide, and some alternatives may be too fast-paced for those who want to take their time getting to grips with the subject. When making your choice, the following four factors should play into your decision-making

Personal Goals and Career Aspirations

The opportunity cost factor often comes into play here, as you may find that some entry-level roles for computer science graduates can “teach you as you go” when it comes to data science. Still, you may not want to feel like you’re stuck in a lower role for several years when you could advance faster with a Master’s under your belt. So, consider charting your ideal career course, with the positions that best align with your goals, to figure out if you’ll need a Master’s to get you to where you want to go.

Current Level of Education and Experience

Some of the options for getting into data science aren’t available to those with limited experience. For example, anybody can make their start with books and articles, which have no barrier to entry. But many internships require demonstrable proof that you understand various programming and scripting languages, with some also asking to see evidence of formal education. As for a Master’s degree, you’ll need a BSc in computer science (or an equivalent degree) to walk down that path.

Financial Considerations

Money makes the educational wheel turn, at least when it comes to formal education. As mentioned, a Master’s in data science can set you back up to $50,000, which may sting (and even be unfeasible) if you already have student loans to pay off for an undergraduate degree. Online courses are more cost-effective (and offer certification), while bootcamps and competitions can either pay you for learning or set you up in a career if you succeed.

Time Commitment and Flexibility

The simple question here is how long do you want to wait to start your career in data science? The patient person can afford to spend a couple of years earning their Master’s degree, and will benefit from having formal and respectable proof of their skills when they’re done. But if you want to get started right now, internships combined with more flexible online courses may provide a faster route to your goal.

A Master’s Degree – Do You Need It to Master Data Science?

Everybody’s answer is different when they ask themselves “do I need a Master’s in data science?” Some prefer the formalized approach that a Master’s offers, along with the exposure to industry professionals that may set them up for strong careers in the future. Others are less patient, preferring to quickly develop skills in a bootcamp, while yet others want a more free-form educational experience that is malleable to their needs and time constraints.

In the end, your circumstances, career goals, and educational preferences are the main factors when deciding which route to take. A Master’s degree is never a bad thing to have on your resume, but it’s not essential for a career in data science. Explore your options and choose whatever works best for you.

With your BSc in Computer Science completed you have a ton of technical skills (ranging from coding to an in-depth understanding of computer architecture) to add to your resume. But post-graduate education looms and you’re tossing around various options, including doing an MCA (Master of computer applications).

An MCA builds on what you learned in your BSc, with fields of study including computational theory, algorithm design, and a host of mathematical subjects. Knowing that, you’re asking yourself “Can I do MCA after BSc Computer Science?” Let’s answer that question.

Eligibility for MCA After BSc Computer Science

The question of eligibility inevitably comes up when applying to study for an MCA, with three core areas you need to consider:

• The minimum requirements
• Entrance exams and admissions processes
• Your performance in your BSc in Computer Science

Minimum Requirements

Starting with the basics, this is what you need to apply for to study for your MCA:

• A Bachelor’s degree in a relevant computing subject (like computer science or computer applications.)
  • Some institutions accept equivalent courses and external courses as evidence of your understanding of computers
• If you’re an international student, you’ll likely need to pass an English proficiency test
  • IELTS and TOEFL are the most popular of these tests, though some universities require a passing grade in a PTE test.
• Evidence that you have the necessary financial resources to cover the cost of your MCA
  • Costs vary but can be as much as $40,000 for a one or two-year course.

Entrance Exams and Admission Processes

Some universities require you to take entrance exams, which can fall into the following categories:

• National Level – You may have to take a national-level exam (such as India’s NIMCET) to demonstrate your basic computing ability.
• State-Level – Most American universities don’t require state-level entrance exams, though some international universities do. For instance, India has several potential exams you may need to take, including the previously-mentioned NIMCET, the WBJECA, and the MAH MCA CET. All measure your computing competence, with most also requiring you to have completed your BSc in Computer Science before you can take the exam.
• University-Specific – Many colleges, at least in the United States, require students to have passing grades in either the ACT or SATs, both of which you take at the high school level. Some colleges have also started accepting the CLT, which is a new test that positions itself as an alternative to the ACT or SAT. The good news is that you’ll have taken these tests already (assuming you study in the U.S.), so you don’t have to take them again to study for your MCA.

Your Performance Matters

How well you do in your computer science degree matters, as universities have limited intakes and will always favor the highest-performing students (mitigating circumstances notwithstanding). For example, many Indian universities that offer MCAs ask students to achieve at least a 50% or 60% CGPA (Cumulative Grade Point Average) across all modules before considering the student for their programs.

Benefits of Pursuing MCA After BSc Computer Science

Now you know the answer to “Can I do MCA after BSc Computer Science,” is that you can (assuming you meet all other criteria), you’re likely asking yourself if it’s worth it. These three core benefits make pursuing an MCA a great use of your time:

• Enhanced Knowledge and Skills – If your BSc in Computer Science is like the foundation that you lay before building a house, an MCA is the house itself. You’ll be building up the basic skills you’ve developed, which includes getting to grips with more advanced programming languages and learning the intricacies of software development. Those who are more interested in the hardware side of things can dig into the specifics of networking.
• Improved Career Prospects – Your career prospects enjoy a decent bump if you have an MCA, with Pay Scale noting the average base salary of an MCA graduate in the United States to be $118,000 per year. That’s about $15,000 more per year than the $103,719 salary Indeed says a computer scientist earns. Add in the prospect of assuming higher (or more senior) roles in a company and the increased opportunities for specialization that come with post-graduate studies and your career prospects look good.
• Networking Opportunities – An MCA lets you delve deeper into the computing industry, exposing you to industry trends courtesy of working with people who are already embedded within the field. Your interactions with existing professionals work wonders for networking, giving you access to connections that could enhance your future career. Plus, you open the door to internships with more prestigious companies, in addition to participating in study projects that look attractive on a resume.

Career Prospects after MCA

After you’ve completed your MCA, the path ahead of you branches out, opening up the possibilities of entering the workforce or continuing your studies.

Job Roles and Positions

If you want to jump straight into the workforce once you have your MCA, there are several roles that will welcome you with open arms:

• Software Developer/Engineer – Equipped with the advanced programming skills an MCA provides, you’re in a great position to take a junior software development role that can quickly evolve into a senior position.
• Systems Analyst – Organization is the name of the game when you’re a systems analyst. These professionals focus on how existing computer systems are organized, coming up with ways to streamline IT operations to get companies operating more efficiently.
• Database Administrator – Almost any software (or website) you care to mention has databases running behind the scenes. Database administrators organize these virtual “filing systems,” which can cover everything from basic login details for websites to complex financial information for major companies.
• Network Engineer – Even the most basic office has a computer network (taking in desktops, laptops, printers, servers, and more) that requires management. A Network engineer provides that management, with a sprinkling of systems analysis that may help with the implementation of new networks.
• IT Consultant – If you don’t want to be tied down to one company, you can take your talents on the road to serve as an IT consultant for companies that don’t have in-house IT teams. You’ll be a “Jack of all trades” in this role, though many consultants choose to specialize in either the hardware or software sides.

Industries and Sectors

Moving away from specific roles, the skills you earn through an MCA makes you desirable in a host of industries and sectors:

• IT and Software Companies – The obvious choice for an MCA graduate, IT and software focus on hardware and software respectively. It’s here where you’ll find the software development and networking roles, though whether you work for an agency, as a solo consultant, or in-house for a business is up to you.
• Government Organizations – In addition to the standard software and networking needs that government agencies face (like most workplaces), cybersecurity is critical in this field. According to Security Intelligence, 106 government or state agencies faced ransomware attacks in 2022, marking nearly 30 more attacks than they faced the year prior. You may be able to turn your knowledge to thwarting this rising tide of cyber-threats, though there are many less security-focused roles available in government organizations.
• Educational Institutions – The very institutions from which you earn your MCA have need of the skills they teach. You’ll know this yourself from working first-hand with the complex networks of computing hardware the average university or school has. Throw software into the mix and your expertise can help educational institutions save money and provide better services to students.
• E-Commerce and Startups – Entrepreneurs with big ideas need technical people to help them build the foundations of their businesses, meaning MCAs are always in demand at startups. The same applies to e-commerce companies, which make heavy use of databases to store customer and financial details.

Further Education and Research Opportunities

You’ve already taken a big step into further education by completing an MCA (which is a post-graduate course), so you’re in the perfect place to take another step. Choosing to work on getting your doctorate in computer science requires a large time commitment, with most programs taking between four and five years, but it allows for more independent study and research. The financial benefits may also be attractive, with Salary.com pointing to an average base salary of $120,884 (before bonuses and benefits) for those who take their studies to the Ph.D. level.

Top MCA Colleges and Universities

Drawing from data provided by College Rank, the following are the top three colleges for those interested in an MCA:

• The University of Washington – A 2.5-year course that is based in the college’s Seattle campus, the University of Washington’s MCA is a part-time program that accepts about 60% of the 120 applicants it receives each year.
• University of California-Berkeley (UCB) – UCB’s program is a tough one to get into, with students needing to achieve a minimum 3.0 Grade Point Average (GPA) on top of having three letters of recommendation. But once you’re in, you’ll join a small group of students focused on research into AI, database management, and cybersecurity, among other areas.
• University of Illinois – Another course that has stringent entry requirements, the University of Illinois’s MCA program requires you to have a 3.2 GPA in your BSc studies to apply. It’s also great for those who wish to specialize, as you get a choice of 11 study areas to focus on for your thesis.

Conclusion

Pursuing an MCA after completing your BSc in Computer Science allows you to build up from your foundational knowledge. Your career prospects open up, meaning you’ll spend less time “working through the ranks” than you would if you enter the workforce without an MCA. Plus, the data shows that those with MCAs earn an average of about $15,000 per year more than those with a BSc in Computer Science.

If you’re pondering the question, “Can I do MCA after BSc Computer Science,” the answer comes down to what you hope to achieve in your career. Those interested in positions of seniority, higher pay scales, and the ability to specialize in specific research areas may find an MCA attractive.

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.