Computers are already ubiquitous in the workplace, with the constantly-evolving concept of cloud computing becoming so popular that Tech Jury says 90% of businesses were in the cloud (in some form) in 2022. All of those systems need maintenance and software, requiring people who are dab-hands with keyboards at their fingertips to build networks, analyze data, and develop software.


Enter computer scientists.


By studying computer science, you open yourself up to a branching career path that could take you into almost any sort of business. But before that, you need to know the answer to a simple question – “Is BSc Computer Science a good course?”


Understanding BSc Computer Science


Think of a BSc in Computer Science as though it’s a buffet, with every topic covered being a different dish. You’ll get a taste of everything that’s on offer in the computing field, with your later educational (and career) decisions being based on the dish (i.e., the topic) that you like best. Among those topics and study areas are the following:

  • Networks and Computer Systems – Taking a more hardware-oriented focus (though software plays a part), this topic covers how to connect computers so they can interact with one another.
  • Programming – The language of computers is one you’ll need to learn how to speak if you want to develop software or websites. You’ll discover that there are a lot of languages to choose from, each with its own specific uses.
  • Artificial Intelligence (AI) – As one of the fastest-growing fields in computing (Statista anticipates growth from $100 billion in 2021 to almost $2 trillion by 2030), AI is already becoming essential in business. You’ll learn the concepts that govern AI, such as machine learning and neural networks.
  • Network Security – Every advancement in computer science brings with it malicious parties who wish to use (or subvert) that advancement to their own ends. Computer science courses teach the foundational aspects of network security, setting the stage for later specialization.

Moving beyond what you study (and the above isn’t an exhaustive list of topics), how long you spend on earning your BSc in Computer Science is another key deciding factor. Most traditional universities offer three-year courses, extending to four years if you take an internship or in-course work. The newer breed of online universities offer more flexibility, with some fast-track courses taking as little as two years, while others offer a more free-form version of study that lets you move at your own pace. With the latter, you could take several more years to complete your degree, though you’ll be able to fit your studies around work and family more easily than you would with a full-time course.


Benefits of BSc Computer Science


Assuming you’re willing to place the time (and monetary) investment into a BSc in Computer Science, there are three core benefits you’ll get from the course.


1 – Acquire In-Demand Skills and Knowledge


The basics you learn are in demand in most companies, with many offering additional training and tuition to help you build beyond the basics to become a specialist. Key areas of interest for employers include:

  • Programming – Those who can speak the language that lies behind software are always in demand, with programmers earning an average hourly rate of $33.10, according to Indeed. Salary expectations climb as you move through the ranks, with senior software engineers capable of earning in the early six figures.
  • Data Structures and Algorithms – Problem solvers are popular in any business. The knowledge of algorithms you develop when studying computer science allows you to create code (almost like a set of steps) that’s designed to solve problems. The same applies to data structures, which focus on the locations and methods used to keep data organized.
  • Computer Networks and Security – Even a small office has a network of computers, laptops, smart devices, printers, and servers that all need to communicate with one another. Computer scientists enable that communication, and keep the “conversations” machines have with each other shielded from intruding eyes.

2 – Versatility and Adaptability in the Job Market


Computer science graduates are like the chameleons of the job market. They have so much foundational knowledge in an array of subjects that they’re well-placed to be “Jacks of all trades” as general computer experts. Plus, the base they have can be built from, setting the stage for them to specialize in specific areas of computing based on their preferences.


We’ll dig into some specific roles you could take (along with their salaries) in the next section of the article.


3 – Opportunities for Further Education and Specialization


You’re already part way down the road to computer science mastery once you have your BSc, so why stop there? The opportunity exists for further education and specialization, which could open the door to further career opportunities:

  • Masters and Ph.D. Programs – A Master’s degree in computer science (or a related subject) is the next logical educational step once you have your BSc. You’ll build on what you’ve already learned, in addition to having a chance to specialize in your thesis. PhD programs aren’t immediately open (you’ll need your Master’s first) but they give you a chance to delve into subject-specific research and could set you up for a career in teaching computer science.
  • Professional Certifications – If you prefer the less formal educational route, professional certifications enable you to study at your own pace and give you handy pieces of paper you can use to prove your skills. Great examples include Cisco’s CCIE program and CompTIA’s range of certifications.

Job Prospects and Career Opportunities


Building on the previous mention about your chameleon-like ability to get jobs in multiple fields, you need to know is BSc in Computer Science good for the career-focused student. These are the roles you can get (with salary data from Indeed).


Software Development and Engineering


Rather than being the person who uses software, you can be the person who forms and puts together the building blocks that make the software tick. Software developers and engineers use their coding skills to create the next great apps, websites, computer games, and anything else that needs a computer or mobile device to run.


Average Salary – $114,470


Data Analysis and Data Science


Data, data everywhere, and not a drop to drink. That little spin on the classic “lost at sea” phrase tells you everything you need to know about how many companies feel in the Big Data world. They’re collecting tons of data but don’t know how to organize what they have or extract useful information from it. Data analysts and scientists solve that problem.


Average Salary (Data Analyst) – $74,570


Average Salary (Data Scientist) – $129,574


Cybersecurity and Network Administration


There’s a never-ending battle being waged between network administrators and hackers, with each trying to stay one step ahead of the other. Cyberattacks are on the rise, with Security Magazine pointing out that attacks around the globe increased by 38% in 2022. That means there’s always demand for cybersecurity specialists.


Average Salary – $107,063


Research and Academia


Rather than using your skills to benefit private enterprises, you could be responsible for the next generation of computer scientists. The academic path is a noble one, though not always the most profitable, and it affords you the chance to research the subjects you’re passionate about. The level you reach in academia depends on your own academic accomplishments, with a BSc usually being enough for school-level teaching. You’ll need a Master’s or Ph.D. to go into further education or complex research.


Average Salary (Computing Teacher) – $26.79 per hour


Entrepreneurship and Freelance Opportunities


Why restrict yourself to a single company when you could build your own or spread your scientific seeds wide by becoming a freelancer? More control over your destiny is the biggest benefit of this career path, though there’s a more “sink or swim” mentality. Those who hit it big with a great business idea can hit it really big, but there are plenty of failed computing businesses on the entrepreneurial road.


Average Salary – It all depends on what you do and how well you do it


Factors to Consider When Evaluating the Worth of BSc Computer Science


If you’re still asking “Is BSc Computer Science a good course?” the answer is a definite “yes.” But there are some factors to consider before you commit to several years of computing studies:

  • Personal Interests and Aptitude – Success in any area of study requires a passion for your subject and a certain amount of talent in the field. If you’re missing one (or both) of these for computer science then a BSc may not be for you.
  • Job Market Trends – It’s very possible to make a six-figure salary as a computer scientist, though specialization is often needed to hit the highest figures. Still, it’s worth keeping an eye on what’s happening with the job market to ensure you’re studying toward a future role.
  • Return on Investment – Undergraduate programs can cost anywhere between $15,000 and $85,000, so you need to feel confident that a computer science course is the right one for your future career. Otherwise, you’re left with a massive hole in your bank balance that you need to fill with student loan repayments.
  • Job Satisfaction – Working yourself into the ground is never a good thing. You need to feel confident that you’ll achieve the appropriate balance between your work, personal, and family lives.

Comparing BSc Computer Science With Other Courses


A BSc in Computer Science is far from your only choice if you’re interested in delving into computers. Here are three alternatives to consider.


BSc Information Technology


Though an IT degree covers some of the same ground as a computer science one (especially when it comes to computer networks), you’ll trade theoretical knowledge for practical application. Expect to do a lot of work with databases and basic software, with some coding along the way.


BSc Data Science


As a more specialized course, a BSc in Data Science sees you delving deeper into the math and statistics behind computational systems. You’ll learn how to analyze data and may get a better grip on emerging tech, such as machine learning, than you would with a computer science degree.


Bachelor of Engineering (Computer Science)


A bachelor of engineering takes a more hardware-centric focus than a BSc, with this course teaching more about the principles of electrical engineering and how our computing devices actually work. There are still software components, and you’ll touch on similar subjects to a BSc, but you’ll get more practical experience with this course.


Is a BSc in Computer Science Good for You?


The most important question to ask isn’t “Is BSc Computer Science a good course,” but rather is it the right course for you? Your career goals, coupled with your desire (or lack thereof) to invest your time and money into the degree, may be the main deciding factors.


As with any course, ask yourself what the ultimate benefit is to you and weigh up your options (remembering that there are several types of computing degrees) to make the right choice.

Related posts

CCN: Australia Tightens Crypto Oversight as Exchanges Expand, Testing Industry’s Appetite for Regulation
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 3 min read

Source:

  • CCN, published on March 29th, 2025

By Kurt Robson

Over the past few months, Australia’s crypto industry has undergone a rapid transformation following the government’s proposal to establish a stricter set of digital asset regulations.

A series of recent enforcement measures and exchange launches highlight the growing maturation of Australia’s crypto landscape.

Experts remain divided on how the new rules will impact the country’s burgeoning digital asset industry.

New Crypto Regulation

On March 21, the Treasury Department said that crypto exchanges and custody services will now be classified under similar rules as other financial services in the country.

“Our legislative reforms will extend existing financial services laws to key digital asset platforms, but not to all of the digital asset ecosystem,” the Treasury said in a statement.

The rules impose similar regulations as other financial services in the country, such as obtaining a financial license, meeting minimum capital requirements, and safeguarding customer assets.

The proposal comes as Australian Prime Minister Anthony Albanese’s center-left Labor government prepares for a federal election on May 17.

Australia’s opposition party, led by Peter Dutton, has also vowed to make crypto regulation a top priority of the government’s agenda if it wins.

Australia’s Crypto Growth

Triple-A data shows that 9.6% of Australians already own digital assets, with some experts believing new rules will push further adoption.

Europe’s largest crypto exchange, WhiteBIT, announced it was entering the Australian market on Wednesday, March 26.

The company said that Australia was “an attractive landscape for crypto businesses” despite its complexity.

In March, Australia’s Swyftx announced it was acquiring New Zealand’s largest cryptocurrency exchange for an undisclosed sum.

According to the parties, the merger will create the second-largest platform in Australia by trading volume.

“Australia’s new regulatory framework is akin to rolling out the welcome mat for cryptocurrency exchanges,” Alexander Jader, professor of Digital Business at the Open Institute of Technology, told CCN.

“The clarity provided by these regulations is set to attract a wave of new entrants,” he added.

Jader said regulatory clarity was “the lifeblood of innovation.” He added that the new laws can expect an uptick “in both local and international exchanges looking to establish a foothold in the market.”

However, Zoe Wyatt, partner and head of Web3 and Disruptive Technology at Andersen LLP, believes that while the new rules will benefit more extensive exchanges looking for more precise guidelines, they will not “suddenly turn Australia into a global crypto hub.”

“The Web3 community is still largely looking to the U.S. in anticipation of a more crypto-friendly stance from the Trump administration,” Wyatt added.

Read the full article below:

Read the article
Agenda Digitale: Generative AI in the Enterprise – A Guide to Conscious and Strategic Use
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 6 min read

Source:


By Zorina Alliata, Professor of Responsible Artificial Intelligence e Digital Business & Innovation at OPIT – Open Institute of Technology

Integrating generative AI into your business means innovating, but also managing risks. Here’s how to choose the right approach to get value

The adoption of generative AI in the enterprise is growing rapidly, bringing innovation to decision-making, creativity and operations. However, to fully exploit its potential, it is essential to define clear objectives and adopt strategies that balance benefits and risks.

Over the course of my career, I have been fortunate to experience firsthand some major technological revolutions – from the internet boom to the “renaissance” of artificial intelligence a decade ago with machine learning.

However, I have never seen such a rapid rate of adoption as the one we are experiencing now, thanks to generative AI. Although this type of AI is not yet perfect and presents significant risks – such as so-called “hallucinations” or the possibility of generating toxic content – ​​it fills a real need, both for people and for companies, generating a concrete impact on communication, creativity and decision-making processes.

Defining the Goals of Generative AI in the Enterprise

When we talk about AI, we must first ask ourselves what problems we really want to solve. As a teacher and consultant, I have always supported the importance of starting from the specific context of a company and its concrete objectives, without inventing solutions that are as “smart” as they are useless.

AI is a formidable tool to support different processes: from decision-making to optimizing operations or developing more accurate predictive analyses. But to have a significant impact on the business, you need to choose carefully which task to entrust it with, making sure that the solution also respects the security and privacy needs of your customers .

Understanding Generative AI to Adopt It Effectively

A widespread risk, in fact, is that of being guided by enthusiasm and deploying sophisticated technology where it is not really needed. For example, designing a system of reviews and recommendations for films requires a certain level of attention and consumer protection, but it is very different from an X-ray reading service to diagnose the presence of a tumor. In the second case, there is a huge ethical and medical risk at stake: it is necessary to adapt the design, control measures and governance of the AI ​​to the sensitivity of the context in which it will be used.

The fact that generative AI is spreading so rapidly is a sign of its potential and, at the same time, a call for caution. This technology manages to amaze anyone who tries it: it drafts documents in a few seconds, summarizes or explains complex concepts, manages the processing of extremely complex data. It turns into a trusted assistant that, on the one hand, saves hours of work and, on the other, fosters creativity with unexpected suggestions or solutions.

Yet, it should not be forgotten that these systems can generate “hallucinated” content (i.e., completely incorrect), or show bias or linguistic toxicity where the starting data is not sufficient or adequately “clean”. Furthermore, working with AI models at scale is not at all trivial: many start-ups and entrepreneurs initially try a successful idea, but struggle to implement it on an infrastructure capable of supporting real workloads, with adequate governance measures and risk management strategies. It is crucial to adopt consolidated best practices, structure competent teams, define a solid operating model and a continuous maintenance plan for the system.

The Role of Generative AI in Supporting Business Decisions

One aspect that I find particularly interesting is the support that AI offers to business decisions. Algorithms can analyze a huge amount of data, simulating multiple scenarios and identifying patterns that are elusive to the human eye. This allows to mitigate biases and distortions – typical of exclusively human decision-making processes – and to predict risks and opportunities with greater objectivity.

At the same time, I believe that human intuition must remain key: data and numerical projections offer a starting point, but context, ethics and sensitivity towards collaborators and society remain elements of human relevance. The right balance between algorithmic analysis and strategic vision is the cornerstone of a responsible adoption of AI.

Industries Where Generative AI Is Transforming Business

As a professor of Responsible Artificial Intelligence and Digital Business & Innovation, I often see how some sectors are adopting AI extremely quickly. Many industries are already transforming rapidly. The financial sector, for example, has always been a pioneer in adopting new technologies: risk analysis, fraud prevention, algorithmic trading, and complex document management are areas where generative AI is proving to be very effective.

Healthcare and life sciences are taking advantage of AI advances in drug discovery, advanced diagnostics, and the analysis of large amounts of clinical data. Sectors such as retail, logistics, and education are also adopting AI to improve their processes and offer more personalized experiences. In light of this, I would say that no industry will be completely excluded from the changes: even “humanistic” professions, such as those related to medical care or psychological counseling, will be able to benefit from it as support, without AI completely replacing the relational and care component.

Integrating Generative AI into the Enterprise: Best Practices and Risk Management

A growing trend is the creation of specialized AI services AI-as-a-Service. These are based on large language models but are tailored to specific functionalities (writing, code checking, multimedia content production, research support, etc.). I personally use various AI-as-a-Service tools every day, deriving benefits from them for both teaching and research. I find this model particularly advantageous for small and medium-sized businesses, which can thus adopt AI solutions without having to invest heavily in infrastructure and specialized talent that are difficult to find.

Of course, adopting AI technologies requires companies to adopt a well-structured risk management strategy, covering key areas such as data protection, fairness and lack of bias in algorithms, transparency towards customers, protection of workers, definition of clear responsibilities regarding automated decisions and, last but not least, attention to environmental impact. Each AI model, especially if trained on huge amounts of data, can require significant energy consumption.

Furthermore, when we talk about generative AI and conversational models , we add concerns about possible inappropriate or harmful responses (so-called “hallucinations”), which must be managed by implementing filters, quality control and continuous monitoring processes. In other words, although AI can have disruptive and positive effects, the ultimate responsibility remains with humans and the companies that use it.

Read the full article below (in Italian):

Read the article