According to Data USA, degrees in the business field are among the most popular in the United States, with 840,116 degrees in this field alone being awarded in 2020. You went down the commerce route (meaning you have a grasp of business administration, accounting, and applied economics) and now you’re interested in practical applications of your knowledge.


With your commerce degree firmly under your belt, you may feel like a ship without a rudder – aimless and having no idea what direction to go. Happily, the tech field is ready and waiting for you, as a career in computer sciences may await. Here, we ask, “can a commerce student do BSc Computer Science?” The answer may surprise you, especially if you’re worried that a computer science degree’s eligibility requirements are outside the scope of what you learned in your commerce studies.


Background on Commerce and Computer Science


On the surface, commerce and computer science may seem like they go together as well as peanut butter and granite. But if you dig a little deeper into the scope of each subject, you start to realize that there’s more crossover than there first appears:

  • Commerce – A degree in commerce gives you a firm grasp of the numbers that lie behind the scenes in a business, with banking, economics, and accounting all falling under your developing areas of expertise. Analytics is also a key part of these courses (especially in the research and data analyst fields), which is where we see some crossover with computer science.
  • Computer Science – If commerce is all about the behind-the-scenes numbers in business, computer science handles what goes on under the hood in computing. Software development, data modeling, and analysis all fall under the computer science graduate’s remit, with the ability to pore through data to come to conclusions being essential to this technical subject.

It’s in the analysis that we start to see similarities between commerce and computer science emerge. Yes, commerce focuses more on the numbers behind businesses (and wider economic trends), but the ability to understand the data presented and report on what you see has applications in the computer science field. There’s not a direct crossover, as computer science will require you to learn the “language” in which computers speak, but they are many soft skills you develop in a commerce degree that apply to computer science.


Eligibility for BSc Computer Science


The key questions to ask when considering the issue of whether can commerce student do BSc Computer Science split into two categories:

  • The general eligibility requirements to study a BSc in computer science
  • Specific requirements that apply to commerce students

Eligibility Criteria for BSc Computer Science


BSc Computer Science degrees don’t require a great deal of computer know-how (though it helps), instead focusing on your grasp of mathematics. Requirements include the following:

  • A high school diploma (or your country’s equivalent) that shows solid performance in mathematical subjects.
    • Some degrees require you to achieve a specific Grade Point Average (GPA), though the specific GPA varies depending on where you apply.
  • A high level of English proficiency, which can be measured using one (or both) of the following tests:
    • IELTS – Get a minimum score between 6.0 and 7.0
    • TOEFL – Get a minimum score between 90 and 100

Beyond these educational requirements, international students may need to submit copies of their passport and Visa, alongside certified academic transcripts to show they’ve achieved their country’s equivalents of the above grades. Not all courses require this of international students, with some online universities focusing more on your academic skills and less on your country of origin.


In terms of entrance exams, some colleges enforce computer science-specific exams (such as the CUET or CUCET), while others use NPATS or similar, more general exams, to determine proficiency.


Eligibility Criteria for Commerce Students


You may be standing at the starting line of your educational journey, meaning you’ve not yet applied to start your degree in commerce. First, congratulations on thinking so far ahead that you’re wondering “Can a commerce student do BSc Computer Science?” And second, you need to know what high school subjects help you get onto this degree path.


Commerce is a form of business degree, meaning any high school subjects that apply to the economic world help. Subjects like math, finance, economics, and foreign languages are obvious choices. The likes of marketing and computer applications also help (with the latter also laying some groundwork for your later computer science studies.


Much like computer science, you’ll likely have to take an entrance exam when applying to study commerce at most universities. The CSEET, CUET, and SET are common choices, with the first of these exams focusing specifically on those who study commerce to work as company secretaries.


The Possibility of Flexible Eligibility Criteria


Not all colleges require you to take entrance exams, with some even using broader strokes for their eligibility requirements to the point where they provide flexibility for both commerce and computer science students.


Colleges with open curriculums (such as Brown University and Hamilton College) offer more freedom in terms of what you study, with their entry requirements being more flexible as a result. Online institutions, such as the Open Institute of Technology (OPIT) may also offer more flexible entry criteria, sometimes allowing you to transfer credit from one course to another. That type of credit transfer may be ideal for you if you start a degree in commerce only to later decide to go down the computer science route.



Career Prospects for Commerce Students in Computer Science


When it comes to careers for those who hold computer science degrees, the obvious heavy-hitters are software and web development, IT management, and systems architecture. There are also exciting careers in the emerging AI fields that take full advantage of the technical skills you’ll develop as part of a BSc in computer science.


As for the career crossover between commerce and computer science, the key is to think about the skills that a commerce degree gives you that can apply in the computing field. Such skills include the following:

  • Analytical Skills – Much like computer science, commerce is all about analyzing the data presented so you can report (and leverage) it for other purposes. Your ability to sit down and pore through the numbers will take you a long way in a computer-related role.
  • Problem-Solving Skills – Closely linked to analytical skills, the ability to solve problems requires you to see the data at hand and come up with solutions while accounting for any restrictions presented. In creating commerce models, those restrictions may relate to budget and competencies, while computer science asks you to solve problems while taking system capabilities and limitations into account.
  • Communication and Teamwork – Though often considered soft skills (as opposed to the “hard” technical skills you learn in a commerce degree), communication and teamwork are vital. If you need proof, try to work alone in any technical career and you’ll see why it’s so crucial to have these skills.

Potential Career Paths for Commerce Students with a BSc in Computer Science


With so much crossover potential between commerce and computer science, it’s clear that the answer to the question can a commerce student do BSc Computer Science is a resounding “yes.” And once you’ve completed your studies, several career paths await:

  • Data Analyst – Reviewing data to find insights (be that into businesses or computer systems) are part of the remit for a data analyst. This role is all about problem-solving, which is a skill you’ll develop in abundance as a commerce and computer science student.
  • Business Analyst – Take the ability to gather insights that is required of a data analyst and apply it specifically to areas of improvement in a business to become a business analyst. You’ll combine technical knowledge of a company’s inner workings with complex financial (and computational) models.
  • IT Consultant – More computer science-centric than commerce-focused, IT consultants deal with the hows and whys of the computer networks businesses build. Your commerce skills will still come into play though, particularly when explaining how IT benefits businesses financially.
  • Financial Technology Specialist – Combining the best of both worlds, this role combines the accounting skills you develop studying commerce with the technical ability needed to understand software and its functions.

 

Challenges and Considerations for Commerce Students


Though it’s possible for a commerce student to study (and succeed in) computer science, there are some challenges to consider.


The Technical Nature of Computer Science


As you learn the language of numbers in a commerce degree, so must you learn the language of machines when studying computer science. Getting to grips with the lingo (not to mention coding) can present a challenge to more business-minded students.


Balancing Your Workload


There’s an old saying that goes “Don’t burn the candle at both ends,” which is a warning not to pack too much onto your work plate. If you study commerce and computer science simultaneously, there’s a risk you may push yourself too far. Avoiding burnout requires finding the balance between your studies and personal time.


Networking and Practical Experience


As a commerce student, you understand that the world of business is as much about who you know as what you know. Finding the right people to take a chance on you, thus giving you practical experience, can be tough. But when armed with a pair of degrees in subjects that complement one another, you’re in a better position to build connections with people who can help you go far.



From Commerce to Computing – Is It Right for You?


So, can a commerce student do BSc Computer Science?


The answer isn’t just “yes,” but that it’s actually a great direction to go. Where a commerce degree equips you with a nice mix of technical knowledge and soft skills, a computer science course gives you even more practical knowledge that allows you to enter more specialized fields. However, your interest in each subject plays a role, as your ability (and passion) for studying hinges on your desire to dig into the more technical world of computing.


Assuming you have a genuine interest (and meet the appropriate eligibility criteria), supplementing your commerce studies with computer science can open up many career paths.

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CCN: Australia Tightens Crypto Oversight as Exchanges Expand, Testing Industry’s Appetite for Regulation
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  • 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.

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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

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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.

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