Finding an industry or even area of life that doesn’t utilize digital technologies is quite a challenge today. As computers continue to impact the ways we do business and live, understanding their capabilities and limitations becomes essential. This is the gist of what computer science is all about.
The tasks of computer science keep growing in scope and complexity. This means the demand for professionals in the field is always on the rise. Global companies are always on the lookout not only for people who know computer science but are also experts in the field.
For these reasons, getting an MSc in Computer Science can be the best career move in the modern landscape. Masters in Computer Science allows you to gain detailed knowledge and choose a specialized path. Better yet, holding such a degree elevates your chances of landing a well-paid job at a respectable organization.
Getting an MSc Computer Science is undoubtedly a good idea. You can even do it online, with all of the conveniences of remote learning. Let’s look at the best Masters in Computer Science courses and find out what they offer in terms of professional development.
Factors to Consider When Choosing an MSc Computer Science Program
Picking the right course may be something of a challenge. Numerous institutions offer quality programs, so you might not know where to start or what to look for when making the decision. Here are the key factors that should influence your choice.
Firstly, the reputation of the institution providing the course will matter greatly. Leading universities and learning organizations will offer the most comprehensive programs. Plus, their degrees will be accredited and recognized worldwide.
Next, you’ll need to choose a particular curriculum and specialization that fit your needs and interests. Computer science is a broad field of study, so picking the right study path will be necessary.
The institution you enroll in should have quality faculty members. This aspect is relatively straightforward: If you pick a reputable university, chances are the faculty will be up to par. On a similar note, such institutions will provide ample research opportunities.
The financial aspect is, of course, another important factor. Tuition fees differ considerably between institutions, and some may provide considerable aid for upcoming students. Yet, that doesn’t mean you should opt for the most affordable variant – the combination of a reasonable price and quality education will be the winning one.
When studying on-campus, the location and facilities will be crucial. While not the deciding factor, this may be a tipping point when comparing two otherwise evenly matched institutions.
Lastly, career support is one of the most important advantages you can get from an MSc program. Some institutions provide considerable opportunities for career development, connecting students with leading companies in the field. Additionally, network-building options will matter in this regard.
Top MSc Computer Science Courses and Programs
Norwegian University of Science and Technology
- Location: Gjøvik, Norway
- Duration: Two years
- Study Mode: Full-time
- Requirements: Informatics bachelor’s or engineering degree; minimum average grade: C; minimum informatics credits: 80; documented informatics and mathematics knowledge
- Tuition fees: No fees
- Scholarships/Financial aid: Free program – no financial aid needed
- Career prospects: Machine learning, gaming industry, AI, VR; possibility of Ph.D. program application
Check out MSc in Computer Science at the Norwegian University of Science and Technology.
KHT
- Location: Stockholm, Sweden
- Duration: Two years
- Study Mode: Full-time
- Requirements: Bachelor’s degree from a Swedish or another recognized university in informatics, computer science, or mathematics (minimum 180 ECTS credits); proficient use of the English language – IELTS 6.5, TOEFL 20, PTE 62, ESOL C1 (minimum 180 points)
- Tuition fees: SEK 310,000; application fee is SEK 900
- Scholarships/Financial aid: Scholarships are available from KTH, the Swedish Institute, and associated organizations; full and one-year scholarships available
- Career prospects: Graduates from KHT have moved forward to Ph.D. studies worldwide or found jobs at leading tech companies like Google, Oracle, Saab, Spotify, and Bloomberg.
Check out MSc in Computer Science at KHT.
University Leiden
- Location: Leiden, Netherlands
- Duration: Two years
- Study Mode: Full-time
- Requirements: Bachelor’s degree in AI, Bioinformatics, Computer Science or a related program; English proficiency – IELTS 6.5, TOEFL 90
- Tuition fees: Students from the EU, Suriname, or Switzerland: €2,314 yearly; other students: €19,600 yearly
- Scholarships/Financial aid: Various scholarships available; EU students under the age of 30 are eligible for a Dutch government loan
- Career prospects: Careers in AI, computer science and education, data science, and advanced computer systems
Check out MSc in Computer Science at University Leiden.
Specializations Within MSc Computer Science
Computer science has numerous subcategories and fields of study. These fields are widely different, so you’ll need to choose your specialization carefully. Let’s look at the key disciplines of computer science that you can specialize in and what those disciplines mean.
Artificial Intelligence and Machine Learning
As a field of computer science, AI deals with methods and technologies that allow machines to simulate human intelligence. This includes machine learning, deep learning, and similar disciplines. Through learning methods, either assisted or unassisted by humans, machines can process data and draw conclusions somewhat independently.
Data Science and Big Data Analysis
Data science, as the name implies, deals with data gathering, processing, and analysis. This facet of computer science is particularly important, as it finds plenty of practical applications in business, other sciences, demographics, and statistics.
A subset of data science, big data analysis focuses on extracting information from massive databases. A data scientist’s job is to compile the data and use advanced technological solutions to draw meaningful conclusions. The volumes of data analyzed this way far surpass anything that humans can achieve without computer assistance.
Cybersecurity and Information Security
Today, cybersecurity counts among the most important facets of computer science. Other disciplines gather, produce, and store copious amounts of data which often contain sensitive information. Unfortunately, modern criminals prey on that information to gain access to financial accounts, steal confidential data, and blackmail businesses and individuals.
Cybersecurity attempts to foil attacks from malicious parties. As the methods of crime evolve, so do the technologies meant to fight them. From phishing prevention to protection from hacking, cybersecurity, and information security ensures sensitive data doesn’t end up in the wrong hands.
Software Engineering and Development
Software is at the core of all computer systems, and it’s an ever-evolving aspect of computer science. New software solutions are needed practically every day, and that’s where software engineering and development come in.
Software engineers design new programs and work out how to implement them. Developers work on finding novel solutions to practical and theoretical challenges. These two branches of computer science are responsible for helping machines keep up with users’ demands, both privately and professionally.
Human-Computer Interaction and User Experience Design
We might not think much about the way we interact with computers. At least that’s the case if the user experience is done right. Designing the elements that people use in regular interaction reflects how efficiently computer systems work. Without quality user experience or means of interaction, software alone doesn’t serve much purpose.
Networking and Cloud Computing
A standalone computer system is a rarity these days. Networking, the internet, and cloud computing unlocked the full potential of the digital world. Today, computers can do their best when connected online, which is why these aspects of computer science count among the most important today.
Internet of Things and Embedded Systems
The Internet of Things (IoT) refers to a network of interconnected smart devices. This technology makes smart homes possible, but that’s only a small part of what IoT can do. Automated manufacturing, logistics, and numerous other complex systems function on this principle. In a sense, IoT and embedded systems represent the pinnacle of computer science since it brings together all other fields of research.
Tips for a Successful MSc Computer Science Application
Applying for an MSc in Computer Science is a step that shouldn’t be taken lightly. Your application will require careful consideration, particularly regarding the career path you wish to take. It would be best to start with a list of programs that fit your chosen field of research.
Once you have that list, you should narrow the choice according to the specific criteria that we listed here. To recap, those criteria are:
- The institution’s reputation and accreditation
- The curriculum
- Faculty and opportunities for research
- Fees and scholarships/financial aid
- Location and facilities
- Networking opportunities and career support
After you choose the program, it will be time to prepare the strongest application possible. You’ll have the best chances of getting accepted into the program with a well-written statement of purpose, the appropriate letters of recommendation, test scores and academic transcripts, and written proof of extracurricular activities and work experience.
Lastly, you should prepare to visit the campus and schedule an interview. Don’t disregard this aspect of the application process, as it could easily determine whether you’ll get accepted.
Start Your Computer Science Master’s Journey Today
Getting an MSc in Computer Science may be a significant boost for your career. Select the right program, and you might find yourself at the top of the job market. If your interests fall into any field of computer science, consider enrolling in a master’s program at a leading institution – it will be an excellent career move.
-
From the same author
Read the articleSource:
- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Read the articleSource:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
-
Go to all authors
Related posts
Source:
- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Source:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
Have questions?
Visit our FAQ page or get in touch with us!
Write us at +39 335 576 0263
Get in touch at hello@opit.com
Talk to one of our Study Advisors
We are international
We can speak in:
