In a world of Big Data, companies need people who have the ability to analyze and reach conclusions from the reams of data they collect about customers. But data science extends far beyond the corporate. Any industry that uses data (i.e., practically all of them) needs data-minded people who can use the latest AI-driven tools to help them scour large datasets.

That’s where you come in. As a potential data scientist, you’ll enter an industry that’s experiencing enormous growth to the point where it will be worth $103 billion (approx. €96.37 billion) by 2027. That market growth translates into demand for talented data scientists, which is already seen today as Coresignal’s data – 8,000 available job postings across eight leading positions in the first five months of 2022 alone – demonstrates.

So, the benefits of earning a free data science certification are obvious – you’re entering a growing industry with huge demand that leads to a better salary. But you need to know which courses will help you break into that industry. This article highlights four of the best free data science courses around.

Top Four Free Data Science Courses

As wonderful as the word “free” may be to budget-conscious students, you still need to know that you’re getting something of value from your data science course. The following options deliver a stellar educational experience and leave you with a qualification that employers recognize.

An Introduction to Data Science (Udemy)

Every journey starts with a first step, and it’s crucial that you take the first step into data science with a course that covers the basics and lays a foundation on which you can build. An Introduction to Data Science does just that by teaching you what data science is and how it applies to the modern world.

That teaching starts with a history lesson that shows how interactions with data (and data collection methods) have evolved over the years. From there, you’ll learn how data science applies in modern industry and discover the difference between actual valuable data and the oodles of “noise” that are in datasets.

It’s a quick and easy course, weighing in at 43 minutes spread across six video lectures, so you don’t have to make a huge time commitment. It’s delivered online by a Google Certified Python Expert named Kumar Rajmani Bapat and is ideal for getting the basics of data science down before you move on to a more intensive or focused course.

But the focus on the basics is also the biggest issue with this course. Rather than showing you the techniques a data scientist uses, the course focuses on what data science is and offers a roadmap for getting into the industry. It’s more about “what” than “how,” which may make the course too rudimentary for people who already have some knowledge of the subject. It’s also worth noting that this isn’t one of those free data science courses with certificate, as you’ll need to pay for an Udemy subscription to get your hands on a certificate of completion. You can still watch the videos and complete the course for free, though.

Introduction to Data Science (SkillUP)

With a similar name to the above Udemy course, you’d be forgiven for assuming that SkillUP’s Introduction to Data Science program teaches the same stuff. Though the course is aimed squarely at beginners, it takes a more in-depth approach that makes it the ideal follow-up to Udemy’s offering.

You start with the basic spiel about what data science is and how it applies to modern industry. But from there, the course tips into actual application by demonstrating some of the best Python programming libraries to use in the field. You’ll also dig deep into the algorithms used in data science, with linear regression analysis, confusion matrices, and logistic regression all getting some time to shine.

Given this higher focus on the skills you’ll need to learn to become a data scientist, the course is longer than Udemy’s offering. It clocks in at seven hours of videos and tutorials, all of which you access online and work through at your own pace. The course also expects you to have a solid grasp of math and programming (some experience with Python is a must) so this isn’t ideal for complete beginners to computer science.

This is a data science free online course with certificate, though there is a caveat. SkillUP only provides 90 days of free access to the course. If you feel it will take longer than that to get through the seven hours of tutorials, you’ll need to enroll in a paid subscription. The best approach here is to only start the course when you’re confident that you can block out the time needed to wrap it up within 90 days.

IBM Data Science Professional Certificate (Coursera)

Aimed squarely at the career-focused individual, IBM’s data science course is all about building the skills that set you on the right path to a career in the field. It takes a more practical approach, starting you off with the fundamentals before pushing you into a project where you’ll work with a real-world dataset and publish a report that’s analyzed by stakeholders simulating what you’ll experience in the working world.

The good news is that you don’t need to know anything about data science to get started with the course. It holds your hand as you learn the basics of what data science is (including what a data scientist actually does) and teaches you about the tools and programming languages you’ll use in the field. Once you have a grasp on the fundamentals, you’ll learn how to analyze and visualize data, in addition to creating machine learning models using Python, before wrapping up with the previously mentioned project.

The IBM Data Science Professional Certificate is a more intensive course than the others on this list. It’s essentially a mini degree, requiring you to invest 10 hours per week for five months into your learning. However, the course is provided entirely online, allowing you to schedule that learning time as you see fit. You’ll work through 10 modules as part of the certificate.

That time commitment may be a downside for those who can’t put 10 hours per week into a course, though that downside is outweighed heavily by the fact that you come out with an IBM certification. Having one of the leading names in computing on your certificate is enough to make any employer sit up and take notice.

Data Analysis With Python (freeCodeCamp)

The Python programming language (along with SQL and a few others) underpins almost everything that the modern data scientist does. Data Analysis with Python takes that concept and runs with it by providing a course that digs into using Python to read, analyze, and visualize data.

Along the way, you’ll learn about the basics of both Python and data analysis, though the real highlight comes from the many libraries and tools the course introduces. You’ll use Seaborn, Numpy, Mayplotlib, and Pandas during the course. All of which are libraries used by professionals to extract and visualize data. The course wraps up with a series of five projects, each testing a different set of skills learned via the modules, with your certification coming after you’ve completed all five.

This is one of those free data science courses that’s entirely self-paced and there are no time constraints or commitments involved. Once you’ve signed up for freeCodeCamp, you can save your progress through the course at any point and return whenever you’re ready. Theoretically, this means you could start the course, save your progress, and then return to it months later, though that isn’t recommended if you want to keep the information fresh in your mind. All told, the course contains 37 modules, plus the five projects required for certification, making it one of the most in-depth Python courses around.

The focus on Python is great for those who are unfamiliar with the language, though it also creates some issues. Namely, this isn’t the right course for those who don’t understand data science fundamentals. It jumps straight into analyzing datasets using Python, so those who don’t really understand what datasets are or how they apply to the modern world should start with a more beginner-oriented course.

Tips for Choosing the Right Data Science Course

You get the same benefit from all of the listed data science online courses – free entry. But each course offers something different. Use these tips to determine which is the right choice for you:

• Assess your current skill level to pick a course that delivers what you need to know right now rather than a course that forces you to run before you can walk.
• Determine your learning goals so you can see how the course fits into your roadmap for becoming a data scientist.
• Consider the course’s format and duration as both will play a huge role in how you schedule your learning around your other commitments, be they work-related or personal.
• Look for courses that offer hands-on project work once you’ve moved beyond learning the basics of data science.
• Read reviews and testimonials from other students to see if people in your position get actual value from the course.

Start Your Journey With Free Data Science Courses Online

Every journey starts with a first step, and that first step could take you into a career that has massive potential for growth if you opt for the data science path. The four courses listed here each offer something different, from exploring the basics of what data science is to digging deep into the programming tools you’ll use to conduct data analysis and visualization. Completing one of the four sets you on the right path, though completing all four gives you a solid grounding (and a set of certifications) that make you immensely attractive to employers.

Large portions of modern life revolve around computers. Many of us start the day by booting a PC and we spend the rest of our time carrying miniaturized computer devices around – our smartphones.

Such devices rely on complex software environments and programs to meet our personal and professional needs. And computer science deals with precisely that.

The job of a computer scientist revolves around software, including theoretical advances, software model design, and the development of new apps. It’s a profession that requires profound knowledge of algorithms, AI, cybersecurity, mathematical analysis, databases, and much more.

In essence, computer science is in the background of everything related to modern digital technologies. Computer scientists solve problems and advance the capabilities of technologies that nearly all industries utilize.

In fact, this scientific field is so broad that explaining what is computer science requires more than a mere definition. That’s why this article will go into considerable detail on the subject to flesh out the meaning behind one of the most important professions of our time.

History of Computer Science

The early history of computer science is a fascinating subject. On the one hand, the mechanics and mathematics that would form the core disciplines of computer science far predate the digital age. On the other hand, the modern iteration of computer science didn’t start until about two decades after the first digital computer came into being.

When examining the roots of computer science, we can go as far back as the antiquity era. Mechanical calculation tools and advanced mathematical algorithms date back millennia. However, those roots are too loosely connected to computer science.

The first people who started exploring the foundations of what is computer science today were Wilhelm Schickard and Gottfried Leibniz in early and late 17th century, respectively.

Schickard is responsible for the design of the world’s first genuine mechanical calculator. Leibniz is the inventor of a calculator that worked in the binary system, the universally known “1-0” number system that paved the way for the digital age.

Despite the early advances in the mentioned fields, it would be another 150 years after Leibniz before mechanical and automated computing machines saw industrial production. Yet, those machines weren’t used for any other purpose apart from calculations.

Computers became more powerful only in the 20th century. Like many other technologies, this branch saw rapid development during the last one hundred years, with IBM creating the first computing lab in 1945.

Yet, while plenty of research was happening, computer science wasn’t established as an independent discipline. That would take place only during the 1960s.

Early Developments

As mentioned, the invention of the binary system could be considered a root of computer science. This isn’t only due to the revolutionary mathematical model – it’s also because the binary number system lends itself particularly well to electronics.

The rise of electrical engineering moved forward inventions like the electrical circuit, the transistor, and powerful data storage solutions. This progress gave birth to the earliest electrical computers, which mostly found use in data processing.

It didn’t take long for massive companies to start using the early computers for information storage. Naturally, this use made further development of the technology necessary. The 1930s saw crucial milestones in computer theory, including the groundbreaking computational model by Alan Turing.

Not long after Turing, John von Neumann created a model of a computer that can store programs. By the 1950s, computers were in use in complex calculations and data processing on a large scale.

The rising demand made the binary machine language too unreliable and impractical. The successor, the so-called assembly language, soon proved just as lacking. By the end of the decade, the world saw the first program languages, which soon became the famed FORTRAN (Formula Translation) and COBOL (Common Business Oriented Language).

The following decade, it became obvious that computer science is a field of study in itself, rather than a subset of mathematical or physical disciplines.

Evolution of Computer Science Over Time

As technology kept progressing, computer science needed to keep up. The first computer operating systems came about in the 1960s, while the next two decades brought about an intense expansion in graphics and affordable hardware.

The combination of these factors (OS, accessible hardware, and graphical development) led to advanced user interfaces, championed by industry giants like Apple and Microsoft.

In parallel to these discoveries, computer networks were advancing, too. The birth of the internet added even more moving parts to the already vast field of computer science, including the first search engines that utilized advanced algorithms, albeit not at the same level as today’s engines.

Furthermore, greater computational capabilities created a need for better storage systems. This included larger databases and faster processing.

Today, computer science explores all of the mentioned facets of computer technology, alongside other fields like robotics and artificial intelligence.

Key Areas of Study in Computer Science

As you’ve undoubtedly noticed, computer science grew in scope with the development of computational technologies. That’s why it’s no surprise that computer science today encompasses many areas that deal with every aspect of the technology currently imaginable.

To answer the question of what is computer science, we’ll list some of the key areas of this discipline:

1. Algorithms and data structures
2. Programming languages and compilers
3. Computer architecture and organization
4. Operating systems
5. Networking and communication
6. Databases and information retrieval
7. Artificial intelligence and machine learning
8. Human-computer interaction
9. Software engineering
10. Computer graphics and visualization

As is apparent, these areas correspond with the historical advances in computational technology. We’ve talked about how algorithms predate the modern age by quite a lot. These mathematical achievements brought about early machine languages, which turned into programming languages.

The progress in data storage and the increased scope of the machines resulted in a need for more robust architecture, which necessitated the creation of operating systems. As computer systems started communicating with each other, better networking became vital.

Work on information retrieval and database management resulted from both individual computer use and a greater reliance on networking. Naturally, it didn’t take long for scientists to start considering how the machines could do even more work individually, which marked the starting point for modern AI.

Throughout its history, computer science developed new disciplines out of the need to solve existing problems and come up with novel solutions. When we consider all that progress, it’s clear that the practical applications of computer science grew alongside the technology itself.

Applications of Computer Science

Computer science is applied in numerous fields and industries. Currently, computer science contributes to the world through innovation and technological development. And as computer systems become more advanced, they are capable of resolving complex issues within some of the most important industries of our age.

Technology and Innovation

In terms of technology and innovation, computer science finds application in the fields of graphics, visualization, sound and video processing, mathematical modeling, analytics, and more.

Graphical rendering helps us visualize concepts that would otherwise be hard to grasp. Technologies like VR and AR expand the way we communicate, while 3D models flesh out future projects in staggering detail.

Sound and video processing capabilities of modern systems continue to revolutionize telecommunications. And, of course, mathematical modeling and analytics expand the possibilities of various systems, from physics to finance.

Problem-Solving in Various Industries

When it comes to the application of computer science in particular industries, this field of study contributes to better quality of life by tackling the most challenging problems in key areas:

• Healthcare
• Finance
• Education
• Entertainment
• Transportation

Granted, these aren’t the only areas where computer science helps overcome issues and previous limitations.

In healthcare, computer systems can produce and analyze medical images, assisting medical experts in diagnosis and patient treatment. Furthermore, branches of computer science like psychoinformatics use digital technologies for a better understanding of psychological traits.

In terms of finance, data gathering and processing is critical for massive financial systems. Additionally, automation and networking make transactions easier and safer.

When it comes to education and entertainment, computer science offers solutions in terms of more comprehensible presentation, as well as more immersive experiences. Many schools worldwide use digital teaching tools today, helping students grasp complex subjects with fewer obstacles compared to traditional methods.

Careers in Computer Science

As should be expected, computer science provides numerous job opportunities in the modern market. Some of the most prominent roles in computer science include systems analysts, programmers, computer research scientists, database administrators, software developers, support specialists, cybersecurity specialists, and network administrators.

The mentioned roles require a level of proficiency in the appropriate field of computer science. Luckily, computer science skills are easier to learn today – mostly thanks to the development of computer science.

An online BSc or MSc in computer science can be an excellent way to get prepared for a career in the most sought-after profession in the modern world.

On that note, not all computer science jobs are projected to grow at the same rate by the end of this decade. Profiles that will likely stay in high demand include:

• Security Analyst
• Software Developer
• Research Scientist
• Database Administrator

Start Learning About Computer Science

Computer science represents a fascinating field that grows with the technology and, in some sense, fuels its own development. This vital branch of science has roots in ancient mathematical principles as well as the latest advances like machine learning and AI.

There are few fields worth exploring more today than computer science. Besides understanding our world better, learning more about computer science can open up incredible career paths and provide an opportunity to contribute to resolving some of the burning issues of our time.

Data permeates almost every aspect of our lives. Trying to make sense of it all is a Herculean endeavor that would take humans years (if not centuries). But fear not; it’s machine learning to the rescue.

Machine learning algorithms can comb through data in a matter of days or even hours, uncovering valuable insights. Many industries have already experienced numerous benefits of these algorithms, yet the field promises to get even bigger and better.

However, we shouldn’t discard humans just yet. They still play an essential role in this process.

Machine learning algorithms couldn’t parse and interpret data correctly without human guidance. As the machine learning field grows, so will the need for skilled data scientists.

One way to acquire the skills necessary to participate in this game-changing field is by taking a machine learning course. When chosen wisely, this course will provide you with crucial theory and invaluable practice to enter the field with a bang or take your knowledge to the next level.

To ensure you choose the best machine learning course, we’ve compiled a list of our top five online picks.

Factors to Consider When Choosing a Machine Learning Course

Just like data, there are tons of courses online. Taking all of them would not be humanly possible. And frankly, not all of these courses would be worth your time. Remember these factors when browsing online learning platforms, and you’ll pick the best machine learning course each time.

Course Content and Curriculum

Shakespeare once said, “Expectation is the root of all heartache.” Believe it or not, this quote will benefit you immensely when choosing an online machine learning course.

Just because a course is named Machine Learning, it doesn’t mean it will be helpful to you. The only way to ensure the course is worth taking is to check its curriculum. Provided the description isn’t misleading, you’ll immediately know whether the course suits your educational and professional needs.

Instructor’s Expertise and Experience

Who teaches the course is as important as what is taught (if not more). Otherwise, you could just pick up a book on machine learning with the same content and try to make sense of it.

So, when a machine learning course piques your interest, check out the instructor.

Are they considered an authority in machine learning? Are they industry veterans?

A quick Google search will tell you all you need to know.

Course Duration and Flexibility

“Can I fully commit to this course?” That is the question to ask yourself before starting a machine learning course.

One look at the course’s description will tell you whether it takes an hour or months to complete. Also, you’ll immediately know if it is self-paced or fixed-timeline.

Hands-On Projects and Real-World Applications

No one can deny the value of theoretical knowledge in a machine learning course. There’s no moving on without understanding machine learning algorithms and underlying principles.

But how will you learn to use those theoretical concepts in practice? That’s right, through hands-on projects and case studies.

Ideally, your chosen course will strike the perfect balance between the two.

Course Reviews and Ratings

Sure, it’s easy to manipulate reviews and ratings. But it’s even easier to spot the fake ones. So, give the rating page a quick read-through, and you should be able to tell if the course is any good.

Certification and Accreditation

Certified and accredited courses are a must for those serious about a career in machine learning. Of course, these courses are rarely free. But if they help you land your dream job, the investment will be well worth it.

Top Picks for the Best Machine Learning Courses

We’ve also considered the above-mentioned factors when choosing our top picks for online machine learning courses. Without further ado, check out the best ones to help you learn or improve machine learning skills.

Supervised Machine Learning: Regression and Classification

This course has a lot of things going for it. It was one of the courses that popularized the entire concept of massive open online courses. And it is taught by none other than Andrew Ng, a pioneer and a visionary leader in machine learning and artificial intelligence (AI). In other words, this course is the gold standard by which every machine learning course is evaluated.

Here are all the important details at a glance:

• The course is beginner-friendly and features flexible deadlines.
• It lasts 11 weeks, each covering different machine learning techniques and models (six hours per week).
• It covers the fundamentals of machine learning and teaches you how to apply them.
• The skills you will gain include regularization to avoid overfitting, gradient descent, supervised learning, and linear regression.
• You’ll earn a certificate after completing the course.

The only thing to note about the certificate is that you must sign up for a Coursera membership ($39/€36 a month) to receive it. Otherwise, you can audit the course for free. To apply, you only need to create a Coursera account and press the “Enroll” button.

Machine Learning With Python

Another fan-favorite on Coursera, this machine learning course uses Python (SciPy and scikit-learn libraries). It’s offered by IBM, a company at the forefront of machine learning and AI research.

Here’s what you need to know about this course:

• The course is beginner-friendly but requires a great deal of calculus knowledge.
• It’s divided into four weeks, each dedicated to one broad machine learning task (regression, clustering, classification, and their implementation).
• By the end of the course, you’ll learn the theoretical fundamentals and numerous real-world applications of machine learning.
• The emphasis is placed on hands-on learning.
• A certificate is available, provided you apply for a Coursera membership ($39/€36 a month).

A Coursera account is all you need to apply for this course. You can start with a 7-day free trial. You’ll have to pay $39 (approximately €36) a month to continue learning.

Machine Learning Crash Course

Google’s Machine Learning Crash Course is ideal for those who want a fast-paced approach to learning machine learning. This intensive course uses TensorFlow, Google’s popular open-source machine learning framework.

Check out these facts to determine whether this is the best machine learning course for you:

• You can take this course as a beginner if you read some additional resources before starting.
• The course consists of 25 lessons that you can complete in 15 hours.
• Google researchers present the lessons.
• It perfectly combines theoretical video lectures (machine learning concepts and engineering), real-world case studies, and hands-on exercises.
• No certificate is issued upon completion.

Enrolling in this course is pretty straightforward – just click the “Start Crash Course” button. The course is free of charge.

Machine Learning A-Z: Hands-On Python & R in Data Science

As its name implies, this Udemy course is pretty comprehensive. Two data scientists teach it, primarily focusing on practical experiences (learning to create machine learning algorithms). If you feel like you’re missing hands-on experience in machine learning, this is the course for you.

Before applying, consider the following information:

• The course can be beginner-friendly, provided you have solid mathematics knowledge.
• It consists of video lessons and practical exercises (around 40 hours total).
• The introductory portion focuses on regression, classification, and clustering models.
• You’ll receive a certificate of completion.

To gain lifetime access to this course, you’ll need to pay $89.99 (a little over €83). Applying for it is a matter of creating an Udemy account and purchasing the course.

Machine Learning Specialization

This advanced course is the course you want to take when mastering your knowledge of machine learning. Or perhaps we should say courses since this specialization consists of six separate courses. The program was created by Andrew Ng, who also serves as an instructor (one of four total).

Here’s a quick overview of the course’s key features:

• The course isn’t beginner-friendly; it’s intermediate level and requires previous experience.
• At a pace of three hours per week, it takes approximately seven months to complete.
• The course focuses on numerous practical skills, including Python programming, linear regression, and decision trees.
• Each course includes a hands-on project.
• You’re awarded a shareable certificate upon completion of each course in the specialization.

To begin this challenging yet rewarding journey, create a Coursera account and enroll in the specialization. Then, you can choose the first course—the entire specialization costs around $350 (close to €324).

Additional Resources for Learning Machine Learning

The more you immerse yourself in machine learning, the faster you advance. So, besides attending a machine learning course, consider exploring additional learning resources, such as:

• Books and e-books. Books on machine learning provide in-depth explanations of the topic. So, if you feel that a course’s content is insufficient, this is the path for you. Check out “Introduction to Statistical Learning” (theory-focused) and “Hands-On Machine Learning With Scikit-Learn and TensorFlow.”
• Online tutorials and blogs. Due to the complexity of the field, only a few bloggers post consistently on the topic. Still, blogs like Christopher Olah and Machine Learning Mastery are updated relatively frequently and contain plenty of fascinating information.
• Podcasts and YouTube channels. Keep up with the latest news in machine learning with podcasts like “This Week in Machine Learning and AI.” YouTube channels like Stanford Online also offer a treasure trove of valuable information on the topic.
• Networking and community involvement. You can learn much about machine learning by sharing insights and ideas with like-minded individuals. Connect with the machine learning community through courses or conferences (AI & Big Data Expo World Series, MLconf).

Master Machine Learning to Transform Your Future

An online machine learning course allows you to learn directly from the best of the best, whether individuals like Andrew Ng or prominent organizations like Google and IBM. Once you start this exciting journey, you probably won’t want to stop. And considering all the career prospects machine learning can bring, why would you?

If you see a future in computer science, consider pursuing a degree from the Open Institute of Technology. Besides machine learning, you’ll acquire all the necessary skills to succeed in this ever-evolving and lucrative field.

Finding an industry or even area of life that doesn’t utilize digital technologies is quite a challenge today. As computers continue to impact the ways we do business and live, understanding their capabilities and limitations becomes essential. This is the gist of what computer science is all about.

The tasks of computer science keep growing in scope and complexity. This means the demand for professionals in the field is always on the rise. Global companies are always on the lookout not only for people who know computer science but are also experts in the field.

For these reasons, getting an MSc in Computer Science can be the best career move in the modern landscape. Masters in Computer Science allows you to gain detailed knowledge and choose a specialized path. Better yet, holding such a degree elevates your chances of landing a well-paid job at a respectable organization.

Getting an MSc Computer Science is undoubtedly a good idea. You can even do it online, with all of the conveniences of remote learning. Let’s look at the best Masters in Computer Science courses and find out what they offer in terms of professional development.

Factors to Consider When Choosing an MSc Computer Science Program

Picking the right course may be something of a challenge. Numerous institutions offer quality programs, so you might not know where to start or what to look for when making the decision. Here are the key factors that should influence your choice.

Firstly, the reputation of the institution providing the course will matter greatly. Leading universities and learning organizations will offer the most comprehensive programs. Plus, their degrees will be accredited and recognized worldwide.

Next, you’ll need to choose a particular curriculum and specialization that fit your needs and interests. Computer science is a broad field of study, so picking the right study path will be necessary.

The institution you enroll in should have quality faculty members. This aspect is relatively straightforward: If you pick a reputable university, chances are the faculty will be up to par. On a similar note, such institutions will provide ample research opportunities.

The financial aspect is, of course, another important factor. Tuition fees differ considerably between institutions, and some may provide considerable aid for upcoming students. Yet, that doesn’t mean you should opt for the most affordable variant – the combination of a reasonable price and quality education will be the winning one.

When studying on-campus, the location and facilities will be crucial. While not the deciding factor, this may be a tipping point when comparing two otherwise evenly matched institutions.

Lastly, career support is one of the most important advantages you can get from an MSc program. Some institutions provide considerable opportunities for career development, connecting students with leading companies in the field. Additionally, network-building options will matter in this regard.

Top MSc Computer Science Courses and Programs

Norwegian University of Science and Technology

• Location: Gjøvik, Norway
• Duration: Two years
• Study Mode: Full-time
• Requirements: Informatics bachelor’s or engineering degree; minimum average grade: C; minimum informatics credits: 80; documented informatics and mathematics knowledge
• Tuition fees: No fees
• Scholarships/Financial aid: Free program – no financial aid needed
• Career prospects: Machine learning, gaming industry, AI, VR; possibility of Ph.D. program application

Check out MSc in Computer Science at the Norwegian University of Science and Technology.

KHT

• Location: Stockholm, Sweden
• Duration: Two years
• Study Mode: Full-time
• Requirements: Bachelor’s degree from a Swedish or another recognized university in informatics, computer science, or mathematics (minimum 180 ECTS credits); proficient use of the English language – IELTS 6.5, TOEFL 20, PTE 62, ESOL C1 (minimum 180 points)
• Tuition fees: SEK 310,000; application fee is SEK 900
• Scholarships/Financial aid: Scholarships are available from KTH, the Swedish Institute, and associated organizations; full and one-year scholarships available
• Career prospects: Graduates from KHT have moved forward to Ph.D. studies worldwide or found jobs at leading tech companies like Google, Oracle, Saab, Spotify, and Bloomberg.

Check out MSc in Computer Science at KHT.

University Leiden

• Location: Leiden, Netherlands
• Duration: Two years
• Study Mode: Full-time
• Requirements: Bachelor’s degree in AI, Bioinformatics, Computer Science or a related program; English proficiency – IELTS 6.5, TOEFL 90
• Tuition fees: Students from the EU, Suriname, or Switzerland: €2,314 yearly; other students: €19,600 yearly
• Scholarships/Financial aid: Various scholarships available; EU students under the age of 30 are eligible for a Dutch government loan
• Career prospects: Careers in AI, computer science and education, data science, and advanced computer systems

Check out MSc in Computer Science at University Leiden.

Specializations Within MSc Computer Science

Computer science has numerous subcategories and fields of study. These fields are widely different, so you’ll need to choose your specialization carefully. Let’s look at the key disciplines of computer science that you can specialize in and what those disciplines mean.

Artificial Intelligence and Machine Learning

As a field of computer science, AI deals with methods and technologies that allow machines to simulate human intelligence. This includes machine learning, deep learning, and similar disciplines. Through learning methods, either assisted or unassisted by humans, machines can process data and draw conclusions somewhat independently.

Data Science and Big Data Analysis

Data science, as the name implies, deals with data gathering, processing, and analysis. This facet of computer science is particularly important, as it finds plenty of practical applications in business, other sciences, demographics, and statistics.

A subset of data science, big data analysis focuses on extracting information from massive databases. A data scientist’s job is to compile the data and use advanced technological solutions to draw meaningful conclusions. The volumes of data analyzed this way far surpass anything that humans can achieve without computer assistance.

Cybersecurity and Information Security

Today, cybersecurity counts among the most important facets of computer science. Other disciplines gather, produce, and store copious amounts of data which often contain sensitive information. Unfortunately, modern criminals prey on that information to gain access to financial accounts, steal confidential data, and blackmail businesses and individuals.

Cybersecurity attempts to foil attacks from malicious parties. As the methods of crime evolve, so do the technologies meant to fight them. From phishing prevention to protection from hacking, cybersecurity, and information security ensures sensitive data doesn’t end up in the wrong hands.

Software Engineering and Development

Software is at the core of all computer systems, and it’s an ever-evolving aspect of computer science. New software solutions are needed practically every day, and that’s where software engineering and development come in.

Software engineers design new programs and work out how to implement them. Developers work on finding novel solutions to practical and theoretical challenges. These two branches of computer science are responsible for helping machines keep up with users’ demands, both privately and professionally.

Human-Computer Interaction and User Experience Design

We might not think much about the way we interact with computers. At least that’s the case if the user experience is done right. Designing the elements that people use in regular interaction reflects how efficiently computer systems work. Without quality user experience or means of interaction, software alone doesn’t serve much purpose.

Networking and Cloud Computing

A standalone computer system is a rarity these days. Networking, the internet, and cloud computing unlocked the full potential of the digital world. Today, computers can do their best when connected online, which is why these aspects of computer science count among the most important today.

Internet of Things and Embedded Systems

The Internet of Things (IoT) refers to a network of interconnected smart devices. This technology makes smart homes possible, but that’s only a small part of what IoT can do. Automated manufacturing, logistics, and numerous other complex systems function on this principle. In a sense, IoT and embedded systems represent the pinnacle of computer science since it brings together all other fields of research.

Tips for a Successful MSc Computer Science Application

Applying for an MSc in Computer Science is a step that shouldn’t be taken lightly. Your application will require careful consideration, particularly regarding the career path you wish to take. It would be best to start with a list of programs that fit your chosen field of research.

Once you have that list, you should narrow the choice according to the specific criteria that we listed here. To recap, those criteria are:

• The institution’s reputation and accreditation
• The curriculum
• Faculty and opportunities for research
• Fees and scholarships/financial aid
• Location and facilities
• Networking opportunities and career support

After you choose the program, it will be time to prepare the strongest application possible. You’ll have the best chances of getting accepted into the program with a well-written statement of purpose, the appropriate letters of recommendation, test scores and academic transcripts, and written proof of extracurricular activities and work experience.

Lastly, you should prepare to visit the campus and schedule an interview. Don’t disregard this aspect of the application process, as it could easily determine whether you’ll get accepted.

Start Your Computer Science Master’s Journey Today

Getting an MSc in Computer Science may be a significant boost for your career. Select the right program, and you might find yourself at the top of the job market. If your interests fall into any field of computer science, consider enrolling in a master’s program at a leading institution – it will be an excellent career move.

Data science is all the rage these days. It plays a pivotal role in many organizations, as it makes raw data easily understandable for managers and owners. In turn, it provides stakeholders with better decision-making opportunities.

Considering the enormous importance of data science, it’s no surprise the industry has grown to a whopping $65 billion. It’s also no wonder why there are 150K+ data scientists in the U.S., either, with more people expected to flock to this realm. So, why not become one of them and set yourself up to earn more than $120,000 per year?

All it takes is to invest in high-quality education, and this article will point you in the right direction. Here’s an overview of the five best data science courses to help propel your career.

Factors to Consider When Choosing a Data Science Course

We’ll take a closer look at the best data science courses in 2023 shortly, but let’s put that on hold for a few moments. After all, you don’t want to end up enrolling in a module that doesn’t suit your needs and budget, do you?

Our data science course buyer’s guide has come to the rescue. Check out the factors you should consider when selecting your module.

Course Content and Curriculum

Becoming a data scientist is a lucrative but broad career path. Did you know that this field branches out into multiple sub-fields? These include data engineering, machine learning, and data analysis. There’s no one-size-fits-all solution when it comes to data science courses, which is why you should make sure the curriculum ties in with your goals.

For example, if you want to spearhead the next generation of machine learning developments, look for a course that focuses on machine learning. In other words, module content should be in line with your needs.

Course Duration and Flexibility

Course duration is another important consideration. If you only want to scratch the surface of data science, a so-called boot camp might be a good choice. It typically lasts two or three months and gives you a basic understanding of this topic.

But if you wish to become a data science mastermind, a BSc or MSc in data science is the right option. It takes at least four years, but it teaches you all you need to know about this area, including theoretical knowledge and practical skills.

Instructor’s Expertise and Experience

Experienced instructors should also be a priority. Just like Elon Musk leads the way in Tesla with his extensive programming expertise, your teachers should be your focal point with their data science knowledge. Check their credentials before hitting the “Enroll” button.

Course Fees and Return on Investment

While you can get a lot of value out of a free data science course, paid alternatives are the real deal. Still, be sure you can afford the module before starting your first lesson. Reliable providers should offer transparent pricing with no hidden fees.

Course Reviews and Ratings

One of the best ways to determine if a course is compatible with you is word of mouth. So, put your search engine to work and see what others are saying about different modules. You’ll be able to learn more about the instructors’ approach, pricing, and content.

Best Data Science Courses Available

Now that you have a sense of direction when looking for a data science course, let’s get to the brass tacks of this article. Completing one of the following modules can be your leg up, giving you an edge over other candidates during your job search.

1. Data Science Specialization by Coursera

Coursera is the repository of many courses, including those related to data science. Their Data Science Specialization course can be an excellent choice if you have some understanding of this field but want to expand your horizons.

If you sign up for the module, you’ll gain access to an array of valuable lessons. The list includes cleaning and analyzing data with R, managing different projects with GitHub, and applying data regression models.

Furthermore, the instructors come from established institutions, and you get a shareable certificate after completing the course. Keep in mind that some prior Python knowledge is recommended to take the module.

Pros:

• Beginner-friendly
• Reliable instructors
• Shareable certificate

Cons:

• Requires Python knowledge

Price: Free enrollment from May 30; $49 per month otherwise

Duration: Approx. 11 months

2. The Data Science Course: Complete Data Science Bootcamp by Udemy

Although this is technically a boot camp, it’s one of the most comprehensive data science courses online. It lifts the veil of mystery surrounding data science and offers detailed explanations of the key concepts in this area.

For instance, if you wish to apply deep learning principles in your work, you can learn how to do so with this course. Other useful skills you can pick up here include Python-based machine learning, data pre-processing, logistic and linear regression, and statistical analyses.

The biggest downside is that lesson quality is inconsistent. Unlike Coursera, Udemy doesn’t attract renowned data science professionals. Basically, anyone can teach on the platform, even if they don’t have credentials. The good news is that you get a certificate of completion for passing the course.

Pros:

• Fairly detailed
• Wide range of skills
• Certificate of completion

Cons:

• Inconsistent teaching quality

Price: $74.99

Duration: 31 hours of video materials

3. Python for Data Science and Machine Learning Bootcamp by Udemy

Udemy makes another appearance on our rundown with their Python for Data Science and Machine Learning course. As you’ve probably guessed, it’s geared toward budding data scientists who want to climb the career ladder with Python.

And admittedly, the course does a good job of teaching the basics of this programming language. It tackles a variety of topics, such as machine learning, Pandas, Seaborn, Sci-Kit, decision tree algorithms, and natural language processing. It comes with a certificate of completion and is relatively short, allowing you to grasp the fundamentals of Python in just a few weeks.

Again, the only drawback might be lesson quality. You may receive instructions from first-class teachers, but you may also have subpar instructors.

Pros:

• Good representation of Python basics
• Natural language processing module
• Short and simple

Cons:

• Inconsistent instructions

Price: $74.99

Duration: 25 hours of video materials

4. Master of Applied Data Science by University of Michigan

For some aspiring data scientists, courses provided by renowned universities are the only ones in play. If you have the same affinity, consider this Master of Applied Data Science at the University of Michigan.

What stands out about this course is that it’s fully online, despite coming from a top-rated school. Therefore, you don’t have to attend classes in person to make headway.

When it comes to the curriculum, it covers most (if not all) subjects you need to apply data science in real life. It delves deep into machine learning, natural language processing, data preparation, and network analysis. Plus, you get a hands-on experience with real data from several companies around the globe. Completing the module earns you an accredited diploma.

As for the instructors, you shouldn’t have issues with inconsistent lectures. Michigan professors are well-versed in data science and know how to transfer knowledge effectively.

Still, many people are put off the program due to the price. It also requires some previous knowledge of statistics and Python.

Pros:

• Renowned institution
• Fully online
• Covers everything data science-related
• Great instructors

Cons:

• Pricey
• Previous knowledge required

Price: $34,000-$46,000

Duration: 12-36 weeks

5. Online Master of Computer Science by Arizona State University

The University of Michigan can be an excellent choice, but it doesn’t blow other schools out of the water. Arizona State is a solid option, too, with its Online Master of Computer Science.

Practical teaching is the highlight of this course. The curriculum focuses on applied projects throughout its duration, enabling you to gain a better understanding of data science and related fields. Some of the skills you can acquire and polish here include machine learning, software security, and computer forensics.

On top of that, the course puts a heavy emphasis on blockchain-related data science. Hence, if you want to test the waters with this ever-growing industry, Arizona State has you covered.

Instructions are also high-quality. Even though it’s an online course, the professors devote the same attention to you as to your fellow students on campus.

As for the drawbacks, the course isn’t affordable for many people. You also need to meet strict admission and GPA criteria.

Pros:

• In-depth course
• Blockchain analysis
• Top-rated professors

Cons:

• On the expensive side
• Stringent enrollment criteria

Price: $15,000

Duration: 18-36 weeks

Tips for Succeeding in a Data Science Course

Just because you choose an exceptional data science course doesn’t mean you’ll breeze through the curriculum. The following tips will help make your experience smoother.

• Set clear goals and expectations — Determine whether you want a basic or advanced understanding of data science.
• Dedicated time for learning and practice — Allocate as much time as necessary to learn and practice key skills.
• Engage in online forums and communities — Visit forums and other online communities to find heaps of resources and course materials.
• Work on real-world projects — Practice applying data science by manipulating real-life data.
• Continuously update your skills — Always look for new learning opportunities to get a full picture of your curriculum.

A Remunerative Career Is Waiting

If you’re looking to master critical skills, the best data science course for you might be Master of Applied Data Science by the University of Michigan. It’s expensive, but it’s jam-packed with real-world knowledge. If you need something simpler that still offers some value, the courses by Coursera and Udemy may be a good fit.

So, make your pick carefully. By enrolling in a course that aligns with your needs, you’ll get a better learning experience and higher retention. And nothing paves the way for a lucrative career in data science like top-grade education.

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.