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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.
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:
- Algorithms and data structures
- Programming languages and compilers
- Computer architecture and organization
- Operating systems
- Networking and communication
- Databases and information retrieval
- Artificial intelligence and machine learning
- Human-computer interaction
- Software engineering
- 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:
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.
As we continue the slow march towards an AI-powered future, the coding and technical skills computer science graduates have are in increasingly high demand. This demand accounts for the high salary expectations of a computer science expert (the average salary in the field is €5,700 in Germany) and makes you more attractive as a hiring prospect to employers.
The challenge – finding a quality computer science course that provides the knowledge you need and has a reputation that forces employers to take notice. The four courses in this article (combined with related studies) transform you from computing enthusiast to sought-after computer scientist.
Top Free Computer Science Courses
Kicking off this computer science course list, we have a pair of the best free online computer science courses for building a foundation within the subject area.
Course 1 – CS50: Introduction to Computer Science (Harvard University)
If you’re looking for a free course that carries plenty of prestige, anything with the “Harvard” label attached is a good start. CS50 is a self-paced course, with Harvard estimating an 11-week completion time with between 10 and 20 hours of daily studying. It’s offered in English (sadly, no alternative languages exist at the time of writing) and it’s free to take, though you’ll pay $189 (approx. €175) for an official certificate.
The course covers programming language basics, starting with simple web-based HTML and advancing into Python and C. Advanced computing concepts, such as data structures and cybersecurity, are also covered, though you’re getting more of a baseline knowledge than specialized teaching. Think of the course as a computer science primer designed to give you a foundation that’s ideal for moving on to more complex studies. Add to that the Harvard seal of approval, which looks great on any CV, and you have a course that’s available globally and ideal for impressing employers.
Course 2 – CompTIA A+ (CompTIA)
This free course is A+ by name and mostly A+ by nature, with CompTIA advertising it as the perfect pathway to follow if you want a career in IT or computer sciences. You get an industry-recognized credential that employers will love, with the course focusing as much on practical skills (such as thinking on your feet in an IT crisis) as it does on theoretical instruction.
That’s not to say that theory isn’t covered. Once you’ve gotten to grips with the basics of the hardware and various operating systems, you’ll move into practical modules focused on networking, software, and cybersecurity. The course providers carry some industry weight, too, as titans like Dell, HP, and Intel recommend CompTIA’s courses for anybody who wants to break into the workforce.
There are some downsides – namely the minimal theoretical teaching makes it harder to understand why the practical things you’ll learn work. But as a companion piece to a more technical course (such as CS50), A+ is a great way to develop much-needed skills.
Other Notable Free Courses
The two courses listed above are far from the only free computer science course options available, with the following also being solid choices:
- Python for Everybody – Coming from the University of Michigan, this course teaches the ins and outs of a popular programming language used in AI and machine learning.
- IBM Data Science Professional Certificate – As something of a computer science-adjacent course, IBM’s certificate hones in on data science topics, such as visualization and machine learning models.
- Introduction to Computer Science and Programming – Put together by the best minds at the Massachusetts Institute of Technology (MIT), this is a great course for beginners who are starting from square one when it comes to programming.
Top Paid Computer Science Courses
If you have some money to spend on your education (or access to student funding) these are the best courses for computer science students who value a more traditional paid education.
Course 1 – Bachelor in Modern Computer Science (OPIT)
As an entirely online course, OPIT’s offering allows you to learn at mostly your own pace, though you’re still expected to complete coursework and pass exams at appropriate times. It’s a three-year course (though two-year fast-track options are available) and it’s provided by an institution that has European Qualification Framework (EQF) credentials.
Granted, the course doesn’t come cheap, with its €3,600 per year tuition fees adding up to €10,800 for a three-year course. But that money buys you a comprehensive computer science education, starting with the basics of software development before moving on to modern concepts, like AI and cloud computing. Along the way, you’ll earn professional certifications from Microsoft and Google, giving you something tangible to place on your CV even as you’re still studying. Credit transfer is also available for students who started a computer science course elsewhere and want to transfer to OPIT’s offering.
Course 2 – Computational Science and Engineering (Technical University of Munich)
Perpetually hovering around the top 50 universities in the world (it ranked 50th in 2021), the Technical University of Munich (TUM) is like the MIT of Europe. With this course, TUM offers something for students who’ve already started on the computer science track and now feel ready to bring those skills together with applied math and engineering for a Master’s certification.
Lasting four semesters of full-time study, the course costs €152.30 and delivers 120 ECTS credits. You’ll hone in on numerical simulation, focusing on how to develop math-based problem-solving methods that help in developing systems and simulations. Theory is king in this course. But you’ll come away with such a solid grounding in that theory (as well as experience with simulated applications) that prepare you for a computer science and engineering career.
Other Notable Paid Courses
More thought goes into choosing a paid computer science course because you’re investing more than just time into your studies. If neither of the above two courses whets your appetite, the following are a few other notable providers offering courses to European and international students:
- Computer Science BSc by Cambridge University – You get more than a degree from one of the UK’s most prestigious universities with this course. Given that Cambridge University lies in the heart of “Silicon Fen,” this course puts you in the ideal location to gain exposure to over 1,000 Cambridge-based tech companies.
- Computational Thinking for Problem Solving – Devised by the Penn University faculty, this four-week online course starts by teaching the “pillars” of computational thinking, ending with an applied task using the Python programming language.
- Computer Science 101L Master the Theory Behind Programming – Available via Udemy, this course costs about €69 or is available with a monthly subscription to Udemy. It features nearly 12 hours of recorded teaching sessions, alongside articles and other resources, that teach the basics of computer science.
Related Courses for a Well-Rounded Computer Science Education
The courses covered so far focus on computer science, with some variance in a few cases, which is like building the foundations for a house. To turn those foundations into something special (and something from which you can make a living), you may need a few more materials. Computer science-related courses give you those materials, with the following areas being great targets for further study.
Programming is the beating heart of computer science. Every piece of software you’ll ever use has a program behind it. Most basic computer science courses teach general programming skills, often in Python, but further study into languages like SQL, Java, and C broadens your skillset to make you more attractive to employers.
According to web3.career, the average European web developer picks up €70,000 per year, with potential to hit six figures with the right company and training. Many of the basics of web development are things you’ll pick up in a computer science course, though those looking for more formal certification should consider the following:
- Full-Stack Web Development for Free (CodingNinjas)
- Intro to HTML 5 (University of Michigan)
- Web Developer on Google Digital Garage (Google)
The European Council’s research suggests that the cost of cybercrimes amounted to €5.5 trillion on the continent alone, with ransomware attacks being among the biggest threats facing EU companies. Therein lies an opportunity – businesses don’t want to lose trillions of euros and your cybersecurity skills could be the shield they need to fend off cyberattacks.
Top cybersecurity courses to consider include:
- Google Cybersecurity (Google)
- The Complete Cyber Security Course (Udemy)
- Introduction to Cybersecurity Foundations (Infosec)
Estimates state that the data science industry will have a 29% compound annual growth rate (CAGR) between 2022 and 2029, making it an ever-growing monolith in the computer science sector. Your ability to extract insights from massive datasets could be useful to employers and is buoyed by the following top courses:
- Data Science MicroMasters (University of California San Diego)
- CS109 Data Science (Harvard University)
- Master of Science in Machine Learning and Data Science (Imperial College London)
Tips for Choosing the Right Computer Science Course
The computer sciences courses covered in this article run the gamut from beginner-level programs to full Master’s degrees. If you feel like you’re struggling to navigate the sheer volume of options available, these tips help you pick an appropriate course:
- Be honest with yourself about your current skill level to choose a computer science course that challenges without being overwhelming.
- Compare the course’s curriculum and learning outcomes with your goals to ensure you’ll get what you need from your studies.
- Measure your time commitments (and how the course format allows for these commitments) against those the course demands.
- Research the instructors who created the course and check online reviews from past and current students.
- Determine whether the cost of the course (both monetary and time-wise) delivers a suitable return on your investment.
Start Your Computer Science Journey With the Right Course
Options abound when you’re looking for a computer science course, with quality free options sitting right alongside traditional paid courses. Whatever course you choose, always remember – one step in the right direction still means that you’re moving forward. By choosing a course, you take your first step into a constantly evolving and expanding world that could provide you with a lifelong career.
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
- 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.
- 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.
Artificial intelligence (AI) permeates every aspect of modern society, with that effect only becoming more pronounced as we move deeper into the 21st century. That’s a statement supported by the Brookings Institute, which asserts that whoever rules AI by 2030 (be it a country or corporation) will rule the global roost until at least 2100.
The point is that AI is already everywhere, even if in limited capacities, and you need to be ready for an AI-centric world to unfold ahead of you in the future. The right AI courses ensure you’re ready, so let’s look at four that you can complete today.
What Is Artificial Intelligence (AI)?
As humans, our brains give us the ability to learn and adapt to everything around us. For computers, AI achieves the same thing, equipping machines with the ability to take in datasets, learn from the data, and apply what it learns to real-world scenarios. There are many types of AI, with the following three being among the most prominent:
- Narrow AI – An AI system that’s dedicated to performing a single task, like a chatbot that delivers stock responses based on user queries. Think of these AI as the “manual labor” machines that exist to do the same thing over and over again.
- General AI – With general AI, we move closer to AI that has the same capacities to learn and apply that humans have. Multi-functional is the keyword here, as these AIs will be capable of completing multiple tasks at a human level.
- Superintelligent AI – Though not in existence yet, superintelligent AI is the pinnacle of AI research, or the peak on the Mount Everest of AI. In addition to bringing the multi-functional talents that humans have to the table, these AI will have an unlimited capacity for learning.
We’re nowhere near the superintelligent AI level yet (some even say that this type of AI will be more of a threat than a help to humanity), but we can see AI in so many industries already. Self-driving cars, automated stock checkers, and even email spam filters are all examples of narrow AI in action, with each having specific functions. As the technology evolves, and it’s already doing so at a rapid pace, we’ll see more multi-function AI come to the fore.
Factors to Consider When Choosing an AI Course
When choosing a course, the key question is always what is artificial intelligence course criteria that actually matters? Here are five things to look for in an artificial intelligence course:
- Quality course content – In this context, “quality” doesn’t solely mean “good” (though that’s a part of it). Your course also needs to deliver an educational experience that furthers whatever goals you’ve set for yourself in your career.
- Course flexibility – Some people can commit themselves fully to an AI course. Others need to fit their learning around work, family, and other commitments. Figure out which category you slot into and search for courses that offer the flexibility (or lack thereof) that you need.
- Instructor expertise – Good instructors bring a combination of theoretical mastery and industry experience to their courses. That’s why the best AI courses are usually created, and run, by people who currently work in the field.
- Course reviews and ratings – Online reviews and ratings are the modern “word of mouth,” with global courses benefitting (or otherwise) from what their students have to say online. A few minutes of research can tell you if other students consider your chosen course to be a dud or an AI masterclass.
- Pricing – As attractive as a full Master’s degree may be, the five-figure pricing may feel prohibitive. Other courses, such as a short-term artificial intelligence online course, may offer snippets of what you need to know at a much lower price. Balance your needs against your budget to make your choice.
Top AI Online Courses
There is no such thing as the “best” artificial intelligence course because every course offers something different that may or may not align with your needs. But these four run the gamut, from full-blown Master’s degrees (with accreditation) to crash courses designed to get you up to speed as fast as possible.
Course 1 – CS50’s Introduction to Artificial Intelligence With Python (Harvard)
There are few educational institutions as prestigious as Harvard University, and its CS50 course is perfect for those who already have a grasp of the Python programming language. Offered completely online, it’s a self-paced course that comes with a verified certificate (assuming you’re willing to pay an extra $199/€180).
Key Topics Covered
- Reinforcement learning as it applies to machine learning
- The core principles of artificial intelligence
- Creating Python programs that use AI
- An in-depth study into graph search algorithms
Course Duration and Pricing
Harvard advertises the course as a seven-week-long self-paced online program and recommends between 10 and 30 hours of study per week. How much time you actually spend on your studies depends on how quickly you pick up the concepts. It’s free to enroll (though a certificate costs money, as mentioned) and enrollment is open between May and December of each year.
Course 2 – Expand Your Knowledge of Artificial Intelligence (Udacity)
Marketed as a “nanodegree” program, which basically means it packs a lot of information into a short timeframe. Expand Your Knowledge gives you access to a digital classroom. It comes with some prerequisites, such as an understanding of Python and statistics, but it’s a course designed for those taking their first steps into applied AI.
Key Topics Covered
- Foundational AI algorithms that power things like NASA’s Mars Rover
- An introduction to AI concepts using Python as your base programming language
- Classical graph search algorithms
- Project reviews and feedback from over 1,400 people in the AI field
Course Duration and Pricing
This is a three-month course, with estimated study hours of between 12 and 15 per week, making it ideal for part-time learners who want to grasp the fundamentals of AI. Pricing is flexible, too. You can subscribe to the monthly version of the course via Udacity at a cost of £329 (approx. €377) per month or buy the whole thing upfront for £837 (approx. €959).
Course 3 – Master in Applied Data Science & AI (OPIT)
Those who’ve already completed a Bachelor’s degree in a computing or statistical subject may want to continue their full-time studies. OPIT’s Master’s program offers that opportunity, with its 100% online course being supported by experienced tutors who are available literally whenever you need them. The course contains both live and prerecorded content and the degree you receive carries European Qualification Framework accreditation.
Key Topics Covered
- Real-life business problems (and solutions) that use both AI and data science
- Python programming in the context of AI and data science
- Business-related topics, such as the ethics surrounding AI usage and project management
- Applied machine learning and artificial intelligence techniques
Course Duration and Pricing
OPIT’s Master’s program is a full-time postgraduate course. The regular version takes 18 months of self-timed study to complete. A fast-track version is available, lasting for 12 months, for those who want a more intensive educational experience. The cost varies depending on when you enroll. Intakes occur in October of each year, with early birds paying a discounted price of €4,950, to save almost €1,500 on the usual €6,500 price.
Course 4 – AI Engineering Professional Certificate (IBM via Coursera)
For those looking for direct tutelage from professionals who already work in the AI field, IBM’s offering is one of the best AI courses online. It’s also ideal for beginners, with no experience in computing needed and a flexible schedule allows you to learn as and how you want. Those studying for formal degrees aren’t left out. The certificate you earn through this course counts toward your degree credit.
Key Topics Covered
- The foundations of machine learning and neural networks
- Machine learning algorithm deployment
- Neural network development using PyTorch, Keras, and TensorFlow
- Implementation of both supervised and unsupervised machine learning models
Course Duration and Pricing
Flexibility is the name of the game with this course. It lasts for eight months, with three hours of learning per week, though fast and full-time learners may be able to complete it much quicker. Enrollment begins in May of each year, and the first seven days of the course act as a free trial so you can get a taste of what it has to offer. It’s also fairly cheap, with the course costing around €125 if you go for the full eight-month option.
Benefits of Taking AI Courses
There’s no use looking for the best artificial intelligence course if you don’t understand how that course will help you in the future. These are four benefits of studying AI:
- Develop a skillset that will not only be important as we move toward an AI-driven future, but will serve as a foundation for the skills you’ll need to develop as AI evolves.
- Combine theoretical and practical knowledge of AI to make your CV sparkle when it’s in front of employers.
- Create the problem-solving skills that are essential in the tech industry, with those skills often being transferable to other sectors.
- Follow whatever path you want in the constantly branching AI field.
Take Your Next Career Step With an Artificial Intelligence Online Course
Each of the four courses highlighted here offers something different. Some are short-term introductory courses while others allow full-time students to continue in-depth formal education. Whichever you choose serves as an investment into your future. AI is already causing ripples in the industrial ocean, and those ripples will grow into a tidal wave of opportunity for those who are prepared for the explosive growth of the industry. By investing in yourself today, through education and career foresight, you set yourself up for an amazing future tomorrow.
If a theoretical data scientist is somebody who’s mastered the art of extracting and analyzing large datasets, an applied data scientist is someone who can put that mastery into real-world practice. They’re insight specialists. And those insights come using techniques like machine learning and data mining to parse through extensive datasets to find patterns and outcomes.
As a prospective Master of applied data science, you may wonder if this career path is the right choice for you. It is, as long as you want to be part of a growing industry. According to Precedence Research, the data science industry is expected to achieve a compound annual growth rate (CAGR) of 16.43% between 2022 and 2030. That CAGR translates into growth from $112.2 billion in value (approx. €103 billion) to $378.7 billion (approx. €349 billion).
That growth alone demonstrates why getting an applied data science MSc could be valuable to your career prospect. Let’s look at three of the top courses on offer to European and international students.
Top MSc Programs in Applied Data Science – Our Criteria
Before digging into the best Master applied data science programs, it’s important to establish the criteria we’ve used to make our selections. The following five factors play a role:
- Reputation and ranking – While overall university rankings denote the quality of an establishment, we’re more interested in the reputation the specific course has in the industry.
- Curriculum and Sspecialization – What will you study and how will the topics you delve into lead to further specialization? We aim to answer both questions for our selections.
- Faculty expertise – When analyzing faculty expertise, we’re looking for a combination of experienced educators and mentors with real-world experience in data science work.
- Industry connections and partnerships – You want to use your MSc in applied data science to find work. A university that has strong connections to industry leaders (either through faculty or partnerships) can propel you forward in your career.
- Career support and alumni network – Speaking of connections, a good alumni network exposes you to peers who can help your career. Combine that with in-house career support from the university, and you get a course that offers more than a basic education.
Top MSc Programs Explored
After applying the above criteria, we’ve come up with a list of three Master of applied data science programs to pique your interest.
Program 1 – Master in Applied Data Science & AI (Open Institute of Technology)
Available as a fully online course for those who value self-learning, the Open Institute of Technology’s (OPIT’s) program lasts for 18 months with costs starting from €4,950. There’s also a fast-track option available for those who can commit to more extensive studies, with that program offering the same degree in just 12 months.
The educational aspect of the course is divided across two terms. In the first term, you’ll focus primarily on principles and techniques in areas such as Python programming, machine learning, and how to use data science to solve business problems. The second term gets more practical as you start to focus on applications of data science (and AI) in the real world before digging into the ethics behind your work.
As for credentials, OPIT is an accredited institution under the European Qualification Framework and its MSc was created by Professor Lorenzo Livi. Serving as program head, Livi brings the expertise he’s developed through teaching and research at both the University of Exeter and the University of Manitoba to the program.
It’s this focus on attracting international faculty that’s the most attractive part of the course. Beyond Livi, the faculty includes professors from institutions as diverse as the University of California, University of Copenhagen, Microsoft, and the Naval Research Laboratory. This mix of academic excellence and professors with real-world experience can lead you to exciting career opportunities and connections.
Program 2 – Master of Science in Data Science (ETH Zurich)
Ranked as the ninth-best computer science university in the world by Research.com, ETH Zurich has a program that stands out thanks to its Data Science Laboratory. This dedicated facility allows students to utilize their theoretical knowledge on simulated practical problems. Process modeling and data validation get put into practice in this lab, all under the oversight of an experienced mentor.
Speaking of faculty, several members of ETH Zurich specialize in teaching data science in relation to the medical field. Both Gunnar Rätsch, a full professor at the university, and Julia Vogt, an assistant professor can directly aid students who wish to apply their data science expertise to medicine.
Career support comes in the form of a dedicated Career Center, which serves as a central hub for students and the companies with which the university partners. ETH encourages partnership through industry events, such as its Industry Day, which encourage local and national businesses to meet with and discuss the work of its students. These events may prove vital to starting your data science career before you’ve even completed your Master of applied data science.
Coming back to the program, it’s a two-year full-time course through which you’ll earn 120 credits per the European Credit Transfer and Accumulation System (ECTS). Prospective students need to have at least 180 ECTS credits from a relevant Bachelor’s degree, such as a BSc in computer science or mathematics. The program costs CHF 730 (approx. €749) per semester, with the option to make voluntary contributions to things like the university’s student union and its Solidarity Fund for Foreign Students.
Program 3 – MSc Data Science (IU International University of Applied Science)
Our final program takes us to Germany and one of the most flexible applied data science MSc programs in Europe. Offered in conjunction with London South Bank University, this program results in graduation with a dual degree with both German and British accreditation. You have a choice between taking the two-year program for €556 per month or a pair of part-time programs. The first of the part-time options lasts for 36 months, costing €417 per month, with the second being a 48-month course costing €329 per month.
The course itself focuses primarily on current developments in the data sector, with modules on Big Data, infrastructure engineering, and software development included. The first semester introduces you to machine learning and deep learning concepts, in addition to offering a model engineering case study so you can get your feet wet with applied data science. The second semester makes room for specialization, as you choose an elective that may focus on Big Data, autonomous driving, or smart manufacturing methods.
Faculty members include Professor Thomas Zoller, who oversees the university’s BSc in data science program in addition to contributing to its Master’s program. His expertise lies in machine learning in the context of image processing, in addition to the use of AI and advanced analytics in digital transformation.
As you move closer to wanting to start your career, IU International’s Career Office comes into play. It holds weekly group career talks, both online and on-campus, in addition to daily slots for one-to-one chats with advisors over Zoom or email. You also get access to the university’s Jobteaser platform, which puts you in direct contact with potential recruiters.
Factors to Consider When Choosing an Applied Data Science MSc
The three programs highlighted above each offer a combination of a stellar education and industry connections that help you to get your data science career started. But if you want to do further research into applied data science MSc programs, these are the factors to consider.
Your Personal Goals
Though it may seem obvious to state, your personal goals play a huge role in your decision. For example, somebody who wishes to work in the medical field may favor ETH Zurich’s offering due to the expertise of its faculty, whereas that course may not be the best choice for those interested in finance. Think about what you want to achieve and which program aligns with those goals.
A Master of applied data science doesn’t come cheap. Most courses cost several thousand euros, though you’ll often find that online courses are more manageable from a cost perspective. Consider the program cost and research financial aid options, such as those highlighted on the EURAXESS portal, when making your choice.
A full-time MSc in applied data science may be great for a young student with no other commitments. But it won’t work so well when you’re trying to fit your education around work, life, and your family. Think about the time commitment the program asks of you. Many find that a part-time or self-learning-driven online course is easier to fit around their schedules than a full-time on-campus program.
Location and Campus Facilities
If you opt for an online course then location isn’t an issue – you can study from home. But those studying on-campus have to consider the location (is the university situated in a business hub, for example) and the facilities offered on-site to help them further their data science careers.
Networking opportunities can come in many forms in a Master of applied data science program. Faculty is the obvious source of connections, with many educators having worked (or still working) directly in the industry, but don’t underestimate the connective powers of your peers. Furthermore, take advantage of any career support facilities your university offers to get yourself in front of prospective employers.
Get Your MSc in Applied Data Science
Think of choosing an applied data science MSc in the same way you’d think about making an investment. You want that investment (both in time and money) to offer a suitable return. The three programs listed here offer superb qualifications and give you the real-world experience needed to forge a career in the applied data science sector. Choose the program that suits your needs, or, use the advice provided to research other programs that are closer to home or more in line with your career goals.
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.
- Reliable instructors
- Shareable certificate
- 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.
- Fairly detailed
- Wide range of skills
- Certificate of completion
- Inconsistent teaching quality
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.
- Good representation of Python basics
- Natural language processing module
- Short and simple
- Inconsistent instructions
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.
- Renowned institution
- Fully online
- Covers everything data science-related
- Great instructors
- Previous knowledge required
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.
- In-depth course
- Blockchain analysis
- Top-rated professors
- On the expensive side
- Stringent enrollment criteria
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.
When you decided to study for a BSc in Computer Science, you put your technical hat on. With reams of coding to wrap your head around (alongside a lot of technical talk about hardware), you’ve set yourself up for a career that could cover everything from software engineering and web development to data analysis.
But there’s another possibility that you may not have considered – engineering. Here, we answer the question “Can I do engineering after BSc Computer Science” and show you why the engineering path may be the right one to follow (both due to interest and potential career payout).
Options for Pursuing Engineering After BSc Computer Science
You have three options for pursuing engineering once you’re in possession of your BSc in Computer Science, some of which give you indirect entry into the field whereas others offer more practical or specialized education.
Lateral Entry into Engineering Courses
Your first choice is a course that combined the best of both worlds – a Bachelor of Engineering (Computer Science), otherwise known as B.E. Computer Science. As another full-time course, this program is usually spread over four years (though some institutions can fast-track you through a two-year course).
Strong high school scores in physics, math, and chemistry are a must if you decide to go down this route, with a minimum of 75% scored across all (with strong proficiency in English to boot). Assuming you hit those criteria, many colleges ask students to complete the Joint Entrance Exam (JEE), which is an exam that assesses your technical abilities and how you can apply those abilities to practical problems.
Master’s Degree in Engineering
Rather than going back to the bachelor’s level to study engineering after finishing your BSc in Computer Science (which is a lateral step as described above), you could keep marching forward. A Master’s degree in engineering is a post-graduate qualification, with most courses requiring you to have a Bachelor’s degree in a suitable technical subject. Engineering is the most obvious choice, though many Master’s programs accept students with computing backgrounds due to the technical nature of their knowledge.
Often called a “terminal” degree, meaning there are no doctorates for the engineering field, a Master’s in engineering should leave you with full accreditation so you can begin a career as a chartered engineer. Thankfully, you don’t usually have to rely on an entrance exam to start the course, as long as you have an appropriate Bachelor’s degree.
Specialized Engineering Courses and Certifications
There’s plenty of crossover between the engineering and computer science paths, particularly when it comes to devising solutions for physical hardware:
- Network Engineering – Designed to equip you with advanced skills in computing (especially in the areas of developing and managing network systems), network engineering courses come in several flavors. Some universities offer them as specialized Master’s programs, assuming you have an appropriate technical Bachelor’s degree. In some cases, you can enter into trainee courses with workplaces that equip you with network engineering skills, with this option sometimes not requiring formal computer science training beforehand.
- Cyber Security Engineering – With cybercrime losses exceeding $10 billion in 2022 (according to the FBI), there’s an obvious demand for people who can engineer systems designed to deter hackers. Specialized programs, such as an MSc in cyber security engineering, equip you with the ability to offer hardware security services and reverse-engineer cyber-attacks. Entry requirements vary depending on your university, though many ask for a minimum second-class degree in a subject like computer science or electronic engineering.
- Applied Data Science – You’ll pick up on some of the technical concepts that underpin data science while studying for your BSc in Computer Science. A Master’s degree in applied data science teaches you the practical side, equipping you with the skills you need to analyze and work on complicated engineering assets. Again, a degree in a technical subject (like computer science) should be enough for most universities, with this course also offering a path into Ph.D. studies in the applied data science and data-based industrial engineering areas.
Benefits of Pursuing Engineering After BSc Computer Science
After having worked so hard to obtain your BSc in Computer Science, the question “can I do engineering after BSc Computer Science?” may not have crossed your mind. After all, you’re equipped to enter the workforce already, so you’re wondering what the benefits of further study may be. Here are three to consider.
Enhanced Career Prospects
Having a joint specialization between engineering and computer science can be your pathway to a higher salary, with specific specializations in applied data science or cyber security engineering veering into six-figure territory.
According to Glass Door, starting salaries for applied data scientists start at around $83,000, though the average is $126,586 per year. Advance in that path until you become a senior or lead data scientist and you’ll find your earnings in the $160,000 range. The same resource suggests the average base pay for a cyber security engineer is nearly as impressive, starting at $92,297 per year, though some organizations offer six-figure contracts for those who have some experience under their belts.
Specialization in a Specific Field
Though a BSc in Computer Science equips you with a ton of foundational knowledge, it can leave you feeling unfocused as potential career paths branch out in front of you. Rather than exploring every one of those branches, shifting into engineering allows you to distill (and build upon) what you already know to create a more focused knowledge base.
In addition to making you more desirable to potential employers (as we see above), a specialization makes it easier to find a job that fits your skill set. You add a layer of polish to your raw skillset, developing an understanding of where your specific talents lie and, more importantly, how you can apply them.
Opportunities for Research and Innovation
Having the skills to access better careers is one thing, but being able to contribute to the development of new technologies can make you feel like you’re making a real difference to the world. Following up your BSc in Computer Science with an engineering specialization equips you with practical knowledge (complementing your technical prowess) to give you the perfect balance for entering into the research world.
As one example, Imperial College London operates a research program that takes a data-driven approach to data science research. Applications of the tech (and ideas) that come from that program are used in fields as diverse as medicine, astrophysics, and finance, allowing researchers to create cross-industry change while working with cutting-edge tech.
Steps to Pursue an Engineering Career Post-BSc
Now that you know that the answer to “Can I do engineering after BSc Computer Science?” is a definite “yes,” there’s one more question to answer:
Step 1 – Research and Choose the Right Engineering Program
Choosing the right engineering program may make you feel like you’re at the starting point of a path that branches out in a dozen directions. Each of those paths has something to offer, though you have to commit to one to become a specialist. Think about what you enjoyed while studying computer science, which, combined with an understanding of your career goals, will help you determine which path leads you toward your passion.
Once you know what you want to study (and why), evaluate the programs open to you using the curriculum offered and the reputations of the programs as your criteria for making a choice.
Step 2 – Prepare for Entrance Exams and Application Process
You’re not going to simply walk into an engineering course because you have a BSc in Computer Science, even if your graduate studies equip you with most of the skills necessary to start a post-graduate engineering course. Some institutions have entrance exams (with the previously mentioned JEE being popular), meaning you need to gather study materials and focus your efforts on passing that exam.
For universities that are happy to accept your BSc in Computer Science as proof of your ability, you still need to complete applications and file them before the appropriate deadlines. These deadlines vary depending on where you apply. For instance, you usually have until the end of June if applying for a program that accepts fall admissions in the United States.
Step 3 – Gain Relevant Work Experience
The more work experience you can get under your belt, especially when studying, the better your resume will look when you start applying for specialized computer engineering roles. Internships and co-op programs can equip you with practical knowledge of the workforce (and help you to build connections), though they’re often unpaid.
If working without pay is a problem for you, accepting part-time or freelance work in an engineering field related to your specialization is an option. Just be wary of burnout if you’re still in the process of completing your studies.
Step 4 – Network With Professionals in the Engineering Field
There’s an old saying that goes “It’s not what you know, it’s who you know.” While that isn’t always the case in engineering (merit and skills go a long way), it still helps to have connections in the field who can point you in the direction of roles and employers.
Attending industry events and conferences (even if you’re not actively looking for a job yet) allows you to hobnob with people who may prove useful when you’re trying to break into the engineering sector. Joining professional associations, such as the Association for Computing Machinery (ACM), offers resources, continuing education, and access to career centers that can help you to get ahead.
Engineer Your Path to a New Career
Computer science and engineering make for good bedfellows, with both fields being highly technical and reliant on you having strong mathematical skills. Perhaps that’s why there are so many attractive (and potentially lucrative) options for specializations, with each offering ways to apply the foundational knowledge you develop during a BSc in Computer Science.
When making your choice, start by figuring out which field grabs your interest before taking the steps described above to reach your career goals.