Most of the modern world – work, private life, and entertainment – revolves around computers and IT in general. Naturally, this landscape creates a high demand for computer science jobs. As a result, BSc Computer Science positions are well-paid and offer excellent career opportunities.


With all these advantages considered, it’s no wonder that people from other professions pivot toward computer science. This includes biology students, too.


But can a biology student do BSc Computer Science? And, equally as important, should they?


The answer to the first question is relatively complex and will represent the bulk of this article. But the second answer is a resounding yes. Interdisciplinary education can be a massive advantage in today’s world, providing venues for innovation and greater career advances.


Let’s delve deeper into the question of can a biology student do BSc Computer Science.


Background on BSc Computer Science


A BSc degree is often a part of professional development for people interested in IT. The degree usually follows a core computer science course. After obtaining the BSc, you can move forward towards a specialization or pursue a PhD in the field.


As a biology student, your path to BSc Computer Science will be different. The first step on the way is to understand what computer science is, which areas it covers, and what core skills it requires. This section will explain just that, plus the career opportunities that come with BSc Computer Science.


Definition and Scope


Computer science deals with computer systems. If you’re (rightfully) wondering what that means precisely, the answer is: practically anything related to computers.


A computer scientist can work on the architecture and structure of a processor chip. On the other hand, their colleague could be engaged in supporting the structure of the internet. Both roles fall under the umbrella of computer science.


At its core, this branch of IT concerns with questions about the nature of computing. In that light, one of the computer scientist’s main tasks is to understand what a computer system is. Then, these professionals can move onto designing different systems for particular purposes.


Core Subjects and Skills


BSc Computer Science courses teach core subjects that provide the essential skills for the job. As you might presume, programming is the crucial skill of a computer scientist. This skill requires proficiency in programming languages and a deep understanding of data structures. In addition, knowing the ins and outs of algorithms is pivotal for programming.


Software development is another skill that computer scientists must have. Besides coding knowledge, this skill calls for high proficiency in the principles of software engineering. A good computer scientists should be able to perform the entire development process from coding to implementation.


Computer science calls for a good understanding of math basics like algebra and calculus. However, advanced techniques will also be necessary.


Finally, a computer scientist should have a firm grasp on data analysis and visualization. The former improves professional capabilities, while the latter helps communicate the data to the stakeholders.


Core subjects in BSc Computer Science courses that tackle these and other skills include:


  • Programming principles
  • Computer networks
  • Computer architectures
  • Foundational mathematics
  • Data structures and Algorithms
  • Web development
  • Introduction to operating systems
  • Cloud computing
  • Programming paradigms

Job Prospects and Career Opportunities


Employment in the computer science sector is growing rapidly, following a trend that’s projected to continue throughout the decade. The U.S. Bureau of Labor Statistics expects a 15% growth in the computer science landscape, along with hundreds of thousands of new jobs.


As the IT sector keeps innovating, even more jobs may become available. After all, many of today’s most desired professions didn’t exist at the start of the century, and computer science is developing rapidly.


Some of the career opportunities in computer science are for programmers, systems analysts, support specialists, software and computer engineers, and data scientists.



Comparing Biology and Computer Science


The question of can a biology student do BSc Computer Science comes down to a few crucial considerations. One of the first things you might ask is: what do computer science and biology even have in common.


Surprisingly, there are considerable similarities between the two fields.


Similarities


The most obvious aspect that computer science and biology share is that both are scientific disciplines. This means that the scientific approach is a hard requirement for both fields.


Biology and computer science aim to solve problems following two crucial methods: data analysis and interpretation and the scientific principle. A computer scientist will follow the same path to a conclusion as a biologist:


  • Observation
  • Question
  • Hypothesis
  • Prediction
  • Testing
  • Iteration

Furthermore, both disciplines will utilize mathematical models, although computer science will lean into math more than biology. Lastly, living organisms can be thought about as systems, which is somewhat similar to a computer scientist’s understanding of computers and other IT technologies.


Differences


Of course, the differences between biology and computer science will be much more evident. The two fields employ completely different sets of skills and require knowledge specific to their subjects. Naturally, people specializing in biology and computer science will also have completely different career paths.


When it comes to the underlying principles behind the two sciences, other crucial differences come to mind:


  • Computer scientists regularly build artificial systems while biologists explore natural ones.
  • As a science, biology is more based on observation, unlike the often experimental computer science.
  • Biology is often regarded as an applied field, while computer science may be viewed as more abstract.

Assessing the Feasibility of a Biology Student Pursuing BSc Computer Science


Now that we’ve seen what makes biology and computer science similar in some regards and different in others, let’s return to the original question:


Can a biology student do BSc Computer Science?


To answer that question, we’ll need to look at two aspects. Firstly, doing a BSc in Computer Science comes with certain prerequisites. And second, you as a biology student must be ready and willing to adapt to the new field.


Analyzing the Prerequisites


The essential skills that are required for a BSc in Computer Science include programming and mathematics. As a biology student, you’ll likely already have some courses in math, which will make that part of the equation easier.


However, programming definitely won’t be a part of the standard biology curriculum. The same goes for other computer science skills.


Yet, this mismatch doesn’t mean that a biology student can’t pivot towards computer science. The process will only require more effort than for someone with a computer science background.


To enroll in a BSc Computer Science program, you’ll need to have a good grasp of the mentioned skills. Since studying biology doesn’t offer knowledge on programming or computer science in general, you’ll need to acquire those skills in addition to your primary studies.


The good news is that you won’t need any other specific knowledge besides math and the basics of programming and computer science. If you’re seriously considering transitioning into computer science, fulfilling these prerequisites will be well worth your while.


Evaluating the Adaptability


Besides the necessary entry-level knowledge for a BSc Computer Science, another factor will determine your success: whether you can adapt to the new field of study.


The similarities between biology and computer science will play a massive role here.


You can lean into your understanding of the scientific principle and apply it to computer systems rather than biological organisms. The transition can be viewed as following the same general methods but using them on a different subject.


Also, data collection and analysis skills will be an excellent foundation for computer science. These skills are vital in biology. Luckily, they also represent an essential part of computer science, so you’ll be able to apply them to the new discipline relatively easy.


Granted, the usefulness of your prior knowledge and skills will reach a limit at a point. Then, you’ll need to show another crucial quality: the willingness to adopt new concepts and learn new subjects.


Your advantage will be in the foundational scientific skills that you’ll have as a biologist. Building on those skills with computer science-specific knowledge will make your transition smoother. The key consideration here will be that you’re ready to learn.


Options for Biology Students to Transition Into BSc Computer Science


The final part of answering the question of can a biology student do BSc Computer Science is the practical method of transitioning. You’ll have several options in that regard:


  • Enroll in a bridge course or a preparatory program
  • Complete an online course and get the appropriate certification
  • Rather than biology alone, opt for an interdisciplinary degree or a dual-degree program
  • Pursue a biology degree simultaneously with a computer science minor

Each of these options will help you gain the necessary knowledge for the BSc and prepare for a career in computer science.



Can a Biology Student Do BSc Computer Science? Absolutely!


As you’ve seen, the path from a biology student to BSc in Computer Science isn’t a straight one. However, it’s completely achievable if you have the motivation.


Getting interdisciplinary education will represent an excellent opportunity for professional growth. Better yet, it will open up your possibilities for personal development as well. Learning about a new discipline is always a benefit, even if you pursue a different career path later in life.


If computer science sounds like an interesting prospect, nothing stops you from following that line of study. Fortunately, the opportunities for just that are readily available. Enlist in a quality BSc course and start building your knowledge base and skills.

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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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Medium: First cohort of students set to graduate from Open Institute of Technology
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 4 min read

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  • Medium, published on March 24th, 2025

By Alexandre Lopez

The first ever cohort will graduate from Open Institute of Technology (OPIT) on 8th March 2025, with 40 students receiving a Master of Science degree in Applied Data Science and AI.

OPIT was launched two years ago by renowned edtech entrepreneur Riccardo Ocleppo and Prof. Francesco Profumo (former minister of education in Italy), who witnessed the growing tech skills gap and wanted to combat it directly through creating a brand-new, accredited academic institution focused on innovative BSc and MSc degrees in the field of Technology.

The higher education institution has grown since its initial launch. Having started with just two degrees on offer — BSc in Modern Computer Science and an MSc in Applied Data Science and Artificial Intelligence — OPIT now offers two bachelor’s and four master’s degrees in a range of areas, such as Computer Science, Digital Business, Artificial Intelligence and Enterprise Cybersecurity.

Students at OPIT can learn from a wide range of professors who combine academic and professional expertise in software engineering, cloud computing, AI, cybersecurity, and much more. The institution operates on a fully remote system, with over 300 students tuning in from 78 countries around the world.

80% of OPIT’s students are already working professionals who are currently employed at top companies across many industries. They are in global tech firms like Accenture, Cisco, and Broadcom and financial companies such as UBS, PwC, Deloitte, and First Bank of Nigeria. Some are leading innovation at Dynatrace and Leonardo, while others focus on sustainability and social impact with Too Good To Go, Caritas, and the Pharo Foundation. From AI and software development to healthcare and international organizations like NATO and the United Nations Mine Action Service (UNMAS), OPIT alumni are making a real difference in the world.

OPIT is working on the development of the expansion of our current academic offerings, new courses, doctoral programs, applied research, and technology transfer initiatives with companies.

Once in the program, students have flexible options to complete their studies faster (by studying during the summer) or extend their studies longer than the standard duration. Every OPIT degree ends with a “capstone project”, providing them with real-life experiences in relevant businesses and industries. Some examples of capstone projects include “AI in Anti-Money Laundering: Leveraging AI to combat financial crime,” or “Predictive Modeling for Climate Disasters: Using AI to anticipate climate-related emergencies.”

The graduation on March 8th marks a pivotal moment for OPIT.

“The success of this first class of graduates marks a significant milestone for OPIT and reinforces our mission: to provide high-quality, globally accessible tech education that meets the ever-evolving demands of the job market,” said Riccardo Ocleppo, founder of OPIT.

“In just two years, we have built a dynamic and highly professional learning environment, attracting students from all over the world and connecting them with leading companies.”

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