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:

How?

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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Mar 7, 2025 3 min read

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By Chris Torney

Artificial intelligence (AI) and machine learning have the potential to offer significant benefits and opportunities to businesses, from greater efficiency and productivity to transformational insights into customer behaviour and business performance. But it is vital that firms take into account a number of ethical considerations when incorporating this technology into their business operations. 

The adoption of AI is still in its infancy and, in many countries, there are few clear rules governing how companies should utilise the technology. However, experts say that firms of all sizes, from small and medium-sized businesses (SMBs) to international corporations, need to ensure their implementation of AI-based solutions is as fair and transparent as possible. Failure to do so can harm relationships with customers and employees, and risks causing serious reputational damage as well as loss of trust.

What are the main ethical considerations around AI?

According to Pierluigi Casale, professor in AI at the Open Institute of Technology, the adoption of AI brings serious ethical considerations that have the potential to affect employees, customers and suppliers. “Fairness, transparency, privacy, accountability, and workforce impact are at the core of these challenges,” Casale explains. “Bias remains one of AI’s biggest risks: models trained on historical data can reinforce discrimination, and this can influence hiring, lending and decision-making.”

Part of the problem, he adds, is that many AI systems operate as ‘black boxes’, which makes their decision-making process hard to understand or interpret. “Without clear explanations, customers may struggle to trust AI-driven services; for example, employees may feel unfairly assessed when AI is used for performance reviews.”

Casale points out that data privacy is another major concern. “AI relies on vast datasets, increasing the risk of breaches or misuse,” he says. “All companies operating in Europe must comply with regulations such as GDPR and the AI Act, ensuring responsible data handling to protect customers and employees.”

A third significant ethical consideration is the potential impact of AI and automation on current workforces. Businesses may need to think about their responsibilities in terms of employees who are displaced by technology, for example by introducing training programmes that will help them make the transition into new roles.

Olivia Gambelin, an AI ethicist and the founder of advisory network Ethical Intelligence, says the AI-related ethical considerations are likely to be specific to each business and the way it plans to use the technology. “It really does depend on the context,” she explains. “You’re not going to find a magical checklist of five things to consider on Google: you actually have to do the work, to understand what you are building.”

This means business leaders need to work out how their organisation’s use of AI is going to impact the people – the customers and employees – that come into contact with it, Gambelin says. “Being an AI-enabled company means nothing if your employees are unhappy and fearful of their jobs, and being an AI-enabled service provider means nothing if it’s not actually connecting with your customers.”

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Reuters: EFG Watch: DeepSeek poses deep questions about how AI will develop
OPIT - Open Institute of Technology
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Feb 10, 2025 4 min read

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  • Reuters, Published on February 10th, 2025.

By Mike Scott

Summary

  • DeepSeek challenges assumptions about AI market and raises new ESG and investment risks
  • Efficiency gains significant – similar results being achieved with less computing power
  • Disruption fuels doubts over Big Tech’s long-term AI leadership and market valuations
  • China’s lean AI model also casts doubt on costly U.S.-backed Stargate project
  • Analysts see DeepSeek as a counter to U.S. tariffs, intensifying geopolitical tensions

February 10 – The launch by Chinese company DeepSeek, opens new tab of its R1 reasoning model last month caused chaos in U.S. markets. At the same time, it shone a spotlight on a host of new risks and challenged market assumptions about how AI will develop.

The shock has since been overshadowed by President Trump’s tariff wars, opens new tab, but DeepSeek is set to have lasting and significant implications, observers say. It is also a timely reminder of why companies and investors need to consider ESG risks, and other factors such as geopolitics, in their investment strategies.

“The DeepSeek saga is a fascinating inflection point in AI’s trajectory, raising ESG questions that extend beyond energy and market concentration,” Peter Huang, co-founder of Openware AI, said in an emailed response to questions.

DeepSeek put the cat among the pigeons by announcing that it had developed its model for around $6 million, a thousandth of the cost of some other AI models, while also using far fewer chips and much less energy.

Camden Woollven, group head of AI product marketing at IT governance and compliance group GRC International, said in an email that “smaller companies and developers who couldn’t compete before can now get in the game …. It’s like we’re seeing a democratisation of AI development. And the efficiency gains are significant as they’re achieving similar results with much less computing power, which has huge implications for both costs and environmental impact.”

The impact on AI stocks and companies associated with the sector was severe. Chipmaker Nvidia lost almost $600 billion in market capitalisation after the DeepSeek announcement on fears that demand for its chips would be lower, but there was also a 20-30% drop in some energy stocks, said Stephen Deadman, UK associate partner at consultancy Sia.

As Reuters reported, power producers were among the biggest winners in the S&P 500 last year, buoyed by expectations of ballooning demand from data centres to scale artificial intelligence technologies, yet they saw the biggest-ever one-day drops after the DeepSeek announcement.

One reason for the massive sell-off was the timing – no-one was expecting such a breakthrough, nor for it to come from China. But DeepSeek also upended the prevailing narrative of how AI would develop, and who the winners would be.

Tom Vazdar, professor of cybersecurity and AI at Open Institute of Technology (OPIT), pointed out in an email that it called into question the premise behind the Stargate Project,, opens new tab a $500 billion joint venture by OpenAI, SoftBank and Oracle to build AI infrastructure in the U.S., which was announced with great fanfare by Donald Trump just days before DeepSeek’s announcement.

“Stargate has been premised on the notion that breakthroughs in AI require massive compute and expensive, proprietary infrastructure,” Vazdar said in an email.

There are also dangers in markets being dominated by such a small group of tech companies. As Abbie Llewellyn-Waters, Investment manager at Jupiter Asset Management, pointed out in a research note, the “Magnificent Seven” tech stocks had accounted for nearly 60% of the index’s gains over the previous two years. The group of mega-caps comprised more than a third of the S&P 500’s total value in December 2024.

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