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OPIT - Open Institute of Technology
OPIT - Open Institute of Technology

Discover the Best Programs Online for Artificial Intelligence Course
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
July 01, 2023 · min read

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.

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Master of Applied Data Science: The Top MSc Programs Explored
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
July 01, 2023 · min read

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, 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.

Program Cost

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.

Program Format

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

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.

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Data Science Courses: The Top 5 Best Programs to Master Skills
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
July 01, 2023 · min read

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.


  • Beginner-friendly
  • 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

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.


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


  • 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.


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


  • 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.


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


  • 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.

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Can I Do Engineering After a BSc in Computer Science?
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
July 01, 2023 · min read

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.

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Can I Do MBA After a BSc in Computer Science?
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
July 01, 2023 · min read

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.

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Do I Need a Master’s Degree in Data Science?
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
July 01, 2023 · min read

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, 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.

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Can I Do MCA After a BSc in Computer Science?
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
July 01, 2023 · min read

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 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.


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.

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Data Science & AI: The Key Differences vs. Machine Learning
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
July 01, 2023 · min read

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

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

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

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

Data Science

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

Definition and Explanation of Data Science

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

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

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

Key Components of Data Science

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

  • Data collection
  • Data analysis
  • Predictive modeling

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

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

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

Applications and Use Cases of Data Science

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

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

Artificial Intelligence (AI)

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

Definition and Explanation of AI

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

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

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

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

Key Components of AI

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

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

We’ll discuss machine learning in a separate section.

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

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

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

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

Applications and Use Cases of AI

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

Machine Learning

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

Definition and Explanation of Machine Learning

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

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

Key Components of Machine Learning

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

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

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

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

Applications and Use Cases of Machine Learning

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

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

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

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

Key Differences Between Data Science, AI, and Machine Learning

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

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

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

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

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

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

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

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

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

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

Understanding the Technology of the Future

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

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

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