As a data scientist, you bridge the gap between the data a company collects and the actionable insights that the company must extract from this data to succeed. That’s reflected in the salary you can command, with Glassdoor showing us that the average salary in Germany for a data scientist is €63,500, with the potential to hit the €80,000 range.


But you can’t turn up at a company and simply proclaim yourself a data scientist. You need to master the analytical and algorithmic tools data scientists use, along with a solid foundation in the AI technologies pervading the data science space now and in the future. An MSc data science program helps you develop those skills, and this article looks at four of the best (two each for on-campus and online programs) to consider.


Factors to Consider When Choosing a Data Science Master’s Program


Before taking the plunge and applying for a data science Master course, you need to get your feet wet with a little research. Consider the following factors, ranging from the course’s content to its ability to help you land a job.


Program Reputation


A good reputation, both for the program and the institution that provides it, can make the difference between getting a call for an interview or having your CV end up in the trash. Look for accredited universities that deliver courses with provable results.


Curriculum


While everyone who studies for a Master’s in data science has the main goal of being a data scientist, the area you wish to work on impacts your decision. Check the course curriculum to ensure you’re getting what you need on the theoretical, practical, and specific industry levels to make the course worthwhile.


Faculty Expertise and Research Opportunities


Any qualification you earn is only as good as the people behind the course providing that qualification. For a Master’s degree, look for faculty that has demonstrable industry experience, a solid track record of teaching, and the ability to provide research opportunities you can use to beef up your CV.


Industry Connections


As nice as the piece of paper you get upon completing a degree may be, what’s nicer is when that piece of paper comes from a course that gets you directly into a career. Look for established industry connections with big players and an alumni network filled with students who’ve gone on to work in the types of roles that appeal to you.


Program Duration and Flexibility


Life often gets in the way of education. Having commitments to work, family, and personal endeavors can make a full-time course unfeasible. Look for a course that fits around your schedule, whatever that may be, and offers enough flexibility for you to commit time when you can.



Top On-Campus MSc Data Science Programs


Being on campus during your studies gives you a chance to participate in a university’s research projects in person. Plus, you’ll work directly with faculty and meet peers who share your passion for data science and may have a few entrepreneurial ideas for you to latch on to. These are the two best data science Master course options for those who want the on-campus experience.


Master’s in Data Science (ETH Zurich)


Developed by an institution that consistently ranks as one of the world’s top 10 providers of computer science education, this course combines theory with practice. You’ll learn about the concepts underpinning data science and how those concepts apply to industries as diverse as medicine, finances, and environmental research. But the true standout is ETH Zurich’s Data Science Laboratory, where you’ll put your theoretical knowledge into practice by experimenting with real-world data science problems.


The course is delivered in English, meaning you must provide a certificate of English language proficiency at level C1 or higher to apply. Assuming you meet the language requirements, you’ll also need a BSc (or equivalent) offering at least 180 ECTS credits in a technical subject, such as computer science, physics, or math. You’ll pay CHF 730 (approx. €749) per semester for the two-year course, with the program taking no more than eight semesters to complete. Hitting the minimum four semesters means you pay about €2,996 in total, depending on the CHF-to-euro exchange rate.


Master of Science in Data Science (University College London)


University College London (UCL) offers a choice between a one-year full-time program and a two-year part-time program, with international students usually paying more than UK-based students. You need to shell out £38,300 (approx. €44,000) for this Master’s in data science. The course may seem expensive for those on a budget, though help is offered through UCL’s Financial Assistance Fund for Postgraduate Students. You’ll only get access to this fund if you can demonstrate that you’re in financial hardship and have taken all available provisions (such as applying for a student loan) available to escape that hardship.


Moving away from the unpleasantness of such high tuition fees, UCL delivers a data science program that starts with the basic theory of machine learning and ends with a research project to demonstrate your knowledge. Admission is tough – the university received 20 applications per available place in 2022. But you get a degree with accreditation from the Royal Society of Statistics if you’re willing to invest the money and are a proven high-performer in a technical subject.


Online and Part-Time MSc Data Science Programs


An online data science Master degree usually comes with two advantages over on-campus options – lower fees and more flexibility. These two courses stand out in the online space.


Master in Applied Data Science & AI (OPIT)


It’s the word “applied” that makes OPIT’s Master’s program stand out as it tells you that you’re going to learn so much more than basic theory in this course. That’s not to say you won’t learn theory, with topics like AI, machine learning, and problem-solving practices all on the docket in the first term of this 18-month course. But the second term challenges you to put all of that knowledge to the test by confronting you with real-world problems, followed by a third term that offers either an internship or an in-depth project.


Tuition fees vary depending on when you apply for the course. You’ll spend €6,500 when paying the full price, though early birds can get on board for €4,950, saving over €1,500 in the process. There’s also an option for a fast-tracked 12-month course (the same tuition fees apply) for people who can dedicate a little more time per week to their education. As for admissions, a BSc degree in almost any field is enough for you to get through the basic entry criteria. International students must demonstrate English language proficiency up to the B2 level, and OPIT has its own English certification program to help with that.


Master of Science in Applied Data Science (University of Southern California Online)


With the online version of its Master’s in data science program, the University of Southern California (USC) makes a top-class education available to European and international students. The selling point is simple – equip you with the skills you need to work as a data scientist. To do that, the course starts with the basics of Python and how to use this popular programming language to navigate your way through complex datasets. As you progress, you’ll face more real-world problems in data management and visualization that echo those you’ll find in industry.


The online program is offered as a full-time two-year course or part-time three-year version, and you can expect to pay $2,424 (approx. €2,240) per credit unit. A successful applicant will usually have a BSc in an engineering-related course, or one in computer science, math, statistics, or a similar numbers-centric field.



Tips for a Successful Application to a Top MSc Data Science Program


Maybe you’ve found the perfect Master’s in data science among the four in this article, or you have your eye on a different course entirely. Either way, you have a hurdle to jump – the application process. Follow these tips to craft an application that increases your chances of being the student who gets chosen from applicant pools that can number in the hundreds.

  • Craft a strong personal statement to show your university of choice who you are as a person away from whatever accomplishments you list on your CV.
  • Get recommendations from appropriate people (ideally previous teachers or employers in data science-related fields) to show you have people who can vouch for you.
  • Demonstrate relevant work experience wherever you can (internships are your friend) or showcase academic projects related to data science.
  • Spend time preparing for interviews by learning as much as possible about the interviewer and their process.
  • Ensure you meet the minimum requirements regarding English language proficiency and previous degree-level experience.

Online or Off – Find the Data Science Master Degree That Works for You


By pursuing a data science Master course, you set off on a journey that prepares you for a future where Big Data (and the models that parse through that data) are king. Each of the four programs here prepares you for that future, albeit in different ways, and each puts you in line for a career that averages in the high five figures and has the potential to grow even further.

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Cyber Threat Landscape 2024: Human-Centric Cyber Threats
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2024 9 min read

Human-centric cyber threats have long posed a serious issue for organizations. After all, humans are often the weakest link in the cybersecurity chain. Unfortunately, when artificial intelligence came into the mix, it only made these threats even more dangerous.

So, what can be done about these cyber threats now?

That’s precisely what we asked Tom Vazdar, the chair of the Enterprise Cybersecurity Master’s program at the Open Institute of Technology (OPIT), and Venicia Solomons, aka the “Cyber Queen.”

They dedicated a significant portion of their “Cyber Threat Landscape 2024: Navigating New Risks” master class to AI-powered human-centric cyber threats. So, let’s see what these two experts have to say on the topic.

Human-Centric Cyber Threats 101

Before exploring how AI impacted human-centric cyber threats, let’s go back to the basics. What are human-centric cyber threats?

As you might conclude from the name, human-centric cyber threats are cybersecurity risks that exploit human behavior or vulnerabilities (e.g., fear). Even if you haven’t heard of the term “human-centric cyber threats,” you’ve probably heard of (or even experienced) the threats themselves.

The most common of these threats are phishing attacks, which rely on deceptive emails to trick users into revealing confidential information (or clicking on malicious links). The result? Stolen credentials, ransomware infections, and general IT chaos.

How Has AI Impacted Human-Centric Cyber Threats?

AI has infiltrated virtually every cybersecurity sector. Social engineering is no different.

As mentioned, AI has made human-centric cyber threats substantially more dangerous. How? By making them difficult to spot.

In Venicia’s words, AI has allowed “a more personalized and convincing social engineering attack.”

In terms of email phishing, malicious actors use AI to write “beautifully crafted emails,” as Tom puts it. These emails contain no grammatical errors and can mimic the sender’s writing style, making them appear more legitimate and harder to identify as fraudulent.

These highly targeted AI-powered phishing emails are no longer considered “regular” phishing attacks but spear phishing emails, which are significantly more likely to fool their targets.

Unfortunately, it doesn’t stop there.

As AI technology advances, its capabilities go far beyond crafting a simple email. Venicia warns that AI-powered voice technology can even create convincing voice messages or phone calls that sound exactly like a trusted individual, such as a colleague, supervisor, or even the CEO of the company. Obey the instructions from these phone calls, and you’ll likely put your organization in harm’s way.

How to Counter AI-Powered Human-Centric Cyber Threats

Given how advanced human-centric cyber threats have gotten, one logical question arises – how can organizations counter them? Luckily, there are several ways to do this. Some rely on technology to detect and mitigate threats. However, most of them strive to correct what caused the issue in the first place – human behavior.

Enhancing Email Security Measures

The first step in countering the most common human-centric cyber threats is a given for everyone, from individuals to organizations. You must enhance your email security measures.

Tom provides a brief overview of how you can do this.

No. 1 – you need a reliable filtering solution. For Gmail users, there’s already one such solution in place.

No. 2 – organizations should take full advantage of phishing filters. Before, only spam filters existed, so this is a major upgrade in email security.

And No. 3 – you should consider implementing DMARC (Domain-based Message Authentication, Reporting, and Conformance) to prevent email spoofing and phishing attacks.

Keeping Up With System Updates

Another “technical” move you can make to counter AI-powered human-centric cyber threats is to ensure all your systems are regularly updated. Fail to keep up with software updates and patches, and you’re looking at a strong possibility of facing zero-day attacks. Zero-day attacks are particularly dangerous because they exploit vulnerabilities that are unknown to the software vendor, making them difficult to defend against.

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Nurturing a Culture of Skepticism

The key component of the human-centric cyber threats is, in fact, humans. That’s why they should also be the key component in countering these threats.

At an organizational level, numerous steps are needed to minimize the risks of employees falling for these threats. But it all starts with what Tom refers to as a “culture of skepticism.”

Employees should constantly be suspicious of any unsolicited emails, messages, or requests for sensitive information.

They should always ask themselves – who is sending this, and why are they doing so?

This is especially important if the correspondence comes from a seemingly trusted source. As Tom puts it, “Don’t click immediately on a link that somebody sent you because you are familiar with the name.” He labels this as the “Rule No. 1” of cybersecurity awareness.

Growing the Cybersecurity Culture

The ultra-specific culture of skepticism will help create a more security-conscious workforce. But it’s far from enough to make a fundamental change in how employees perceive (and respond to) threats. For that, you need a strong cybersecurity culture.

Tom links this culture to the corporate culture. The organization’s mission, vision, statement of purpose, and values that shape the corporate culture should also be applicable to cybersecurity. Of course, this isn’t something companies can do overnight. They must grow and nurture this culture if they are to see any meaningful results.

According to Tom, it will probably take at least 18 months before these results start to show.

During this time, organizations must work on strengthening the relationships between every department, focusing on the human resources and security sectors. These two sectors should be the ones to primarily grow the cybersecurity culture within the company, as they’re well versed in the two pillars of this culture – human behavior and cybersecurity.

However, this strong interdepartmental relationship is important for another reason.

As Tom puts it, “[As humans], we cannot do anything by ourselves. But as a collective, with the help within the organization, we can.”

Staying Educated

The world of AI and cybersecurity have one thing in common – they never sleep. The only way to keep up with these ever-evolving worlds is to stay educated.

The best practice would be to gain a solid base by completing a comprehensive program, such as OPIT’s Enterprise Cybersecurity Master’s program. Then, it’s all about continuously learning about new developments, trends, and threats in AI and cybersecurity.

Conducting Regular Training

For most people, it’s not enough to just explain how human-centric cyber threats work. They must see them in action. Especially since many people believe that phishing attacks won’t happen to them or, if they do, they simply won’t fall for them. Unfortunately, neither of these are true.

Approximately 3.4 billion phishing emails are sent each day, and millions of them successfully bypass all email authentication methods. With such high figures, developing critical thinking among the employees is the No. 1 priority. After all, humans are the first line of defense against cyber threats.

But humans must be properly trained to counter these cyber threats. This training includes the organization’s security department sending fake phishing emails to employees to test their vigilance. Venicia calls employees who fall for these emails “clickers” and adds that no one wants to be a clicker. So, they do everything in their power to avoid falling for similar attacks in the future.

However, the key to successful employee training in this area also involves avoiding sending similar fake emails. If the company keeps trying to trick the employees in the same way, they’ll likely become desensitized and less likely to take real threats seriously.

So, Tom proposes including gamification in the training. This way, the training can be more engaging and interactive, encouraging employees to actively participate and learn. Interestingly, AI can be a powerful ally here, helping create realistic scenarios and personalized learning experiences based on employee responses.

Following in the Competitors’ Footsteps

When it comes to cybersecurity, it’s crucial to be proactive rather than reactive. Even if an organization hasn’t had issues with cyberattacks, it doesn’t mean it will stay this way. So, the best course of action is to monitor what competitors are doing in this field.

However, organizations shouldn’t stop with their competitors. They should also study other real-world social engineering incidents that might give them valuable insights into the tactics used by the malicious actors.

Tom advises visiting the many open-source databases reporting on these incidents and using the data to build an internal educational program. This gives organizations a chance to learn from other people’s mistakes and potentially prevent those mistakes from happening within their ecosystem.

Stay Vigilant

It’s perfectly natural for humans to feel curiosity when it comes to new information, anxiety regarding urgent-looking emails, and trust when seeing a familiar name pop up on the screen. But in the world of cybersecurity, these basic human emotions can cause a lot of trouble. That is, at least, when humans act on them.

So, organizations must work on correcting human behaviors, not suppressing basic human emotions. By doing so, they can help employees develop a more critical mindset when interacting with digital communications. The result? A cyber-aware workforce that’s well-equipped to recognize and respond to phishing attacks and other cyber threats appropriately.

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Cyber Threat Landscape 2024: The AI Revolution in Cybersecurity
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 17, 2024 9 min read

There’s no doubt about it – artificial intelligence has revolutionized almost every aspect of modern life. Healthcare, finance, and manufacturing are just some of the sectors that have been virtually turned upside down by this powerful new force. Cybersecurity also ranks high on this list.

But as much as AI can benefit cybersecurity, it also presents new challenges. Or – to be more direct –new threats.

To understand just how serious these threats are, we’ve enlisted the help of two prominent figures in the cybersecurity world – Tom Vazdar and Venicia Solomons. Tom is the chair of the Master’s Degree in Enterprise Cybersecurity program at the Open Institute of Technology (OPIT). Venicia, better known as the “Cyber Queen,” runs a widely successful cybersecurity community looking to empower women to succeed in the industry.

Together, they held a master class titled “Cyber Threat Landscape 2024: Navigating New Risks.” In this article, you get the chance to hear all about the double-edged sword that is AI in cybersecurity.

How Can Organizations Benefit From Using AI in Cybersecurity?

As with any new invention, AI has primarily been developed to benefit people. In the case of AI, this mainly refers to enhancing efficiency, accuracy, and automation in tasks that would be challenging or impossible for people to perform alone.

However, as AI technology evolves, its potential for both positive and negative impacts becomes more apparent.

But just because the ugly side of AI has started to rear its head more dramatically, it doesn’t mean we should abandon the technology altogether. The key, according to Venicia, is in finding a balance. And according to Tom, this balance lies in treating AI the same way you would cybersecurity in general.

Keep reading to learn what this means.

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Implement a Governance Framework

In cybersecurity, there is a governance framework called ISO/IEC 27000, whose goal is to provide a systematic approach to managing sensitive company information, ensuring it remains secure. A similar framework has recently been created for AI— ISO/IEC 42001.

Now, the trouble lies in the fact that many organizations “don’t even have cybersecurity, not to speak artificial intelligence,” as Tom puts it. But the truth is that they need both if they want to have a chance at managing the risks and complexities associated with AI technology, thus only reaping its benefits.

Implement an Oversight Mechanism

Fearing the risks of AI in cybersecurity, many organizations chose to forbid the usage of this technology outright within their operations. But by doing so, they also miss out on the significant benefits AI can offer in enhancing cybersecurity defenses.

So, an all-out ban on AI isn’t a solution. A well-thought-out oversight mechanism is.

According to Tom, this control framework should dictate how and when an organization uses cybersecurity and AI and when these two fields are to come in contact. It should also answer the questions of how an organization governs AI and ensures transparency.

With both of these frameworks (governance and oversight), it’s not enough to simply implement new mechanisms. Employees should also be educated and regularly trained to uphold the principles outlined in these frameworks.

Control the AI (Not the Other Way Around!)

When it comes to relying on AI, one principle should be every organization’s guiding light. Control the AI; don’t let the AI control you.

Of course, this includes controlling how the company’s employees use AI when interacting with client data, business secrets, and other sensitive information.

Now, the thing is – people don’t like to be controlled.

But without control, things can go off the rails pretty quickly.

Tom gives just one example of this. In 2022, an improperly trained (and controlled) chatbot gave an Air Canada customer inaccurate information and a non-existing discount. As a result, the customer bought a full-price ticket. A lawsuit ensued, and in 2024, the court ruled in the customer’s favor, ordering Air Canada to pay compensation.

This case alone illustrates one thing perfectly – you must have your AI systems under control. Tom hypothesizes that the system was probably affordable and easy to implement, but it eventually cost Air Canada dearly in terms of financial and reputational damage.

How Can Organizations Protect Themselves Against AI-Driven Cyberthreats?

With well-thought-out measures in place, organizations can reap the full benefits of AI in cybersecurity without worrying about the threats. But this doesn’t make the threats disappear. Even worse, these threats are only going to get better at outsmarting the organization’s defenses.

So, what can the organizations do about these threats?

Here’s what Tom and Venicia suggest.

Fight Fire With Fire

So, AI is potentially attacking your organization’s security systems? If so, use AI to defend them. Implement your own AI-enhanced threat detection systems.

But beware – this isn’t a one-and-done solution. Tom emphasizes the importance of staying current with the latest cybersecurity threats. More importantly – make sure your systems are up to date with them.

Also, never rely on a single control system. According to our experts, “layered security measures” are the way to go.

Never Stop Learning (and Training)

When it comes to AI in cybersecurity, continuous learning and training are of utmost importance – learning for your employees and training for the AI models. It’s the only way to ensure all system aspects function properly and your employees know how to use each and every one of them.

This approach should also alleviate one of the biggest concerns regarding an increasing AI implementation. Namely, employees fear that they will lose their jobs due to AI. But the truth is, the AI systems need them just as much as they need those systems.

As Tom puts it, “You need to train the AI system so it can protect you.”

That’s why studying to be a cybersecurity professional is a smart career move.

However, you’ll want to find a program that understands the importance of AI in cybersecurity and equips you to handle it properly. Get a master’s degree in Enterprise Security from OPIT, and that’s exactly what you’ll get.

Join the Bigger Fight

When it comes to cybersecurity, transparency is key. If organizations fail to report cybersecurity incidents promptly and accurately, they not only jeopardize their own security but also that of other organizations and individuals. Transparency builds trust and allows for collaboration in addressing cybersecurity threats collectively.

So, our experts urge you to engage in information sharing and collaborative efforts with other organizations, industry groups, and governmental bodies to stay ahead of threats.

How Has AI Impacted Data Protection and Privacy?

Among the challenges presented by AI, one stands out the most – the potential impact on data privacy and protection. Why? Because there’s a growing fear that personal data might be used to train large AI models.

That’s why European policymakers sprang into action and introduced the Artificial Intelligence Act in March 2024.

This regulation, implemented by the European Parliament, aims to protect fundamental rights, democracy, the rule of law, and environmental sustainability from high-risk AI. The act is akin to the well-known General Data Protection Regulation (GDPR) passed in 2016 but exclusively targets the use of AI. The good news for those fearful of AI’s potential negative impact is that every requirement imposed by this act is backed up with heavy penalties.

But how can organizations ensure customers, clients, and partners that their data is fully protected?

According to our experts, the answer is simple – transparency, transparency, and some more transparency!

Any employed AI system must be designed in a way that doesn’t jeopardize anyone’s privacy and freedom. However, it’s not enough to just design the system in such a way. You must also ensure all the stakeholders understand this design and the system’s operation. This includes providing clear information about the data being collected, how it’s being used, and the measures in place to protect it.

Beyond their immediate group of stakeholders, organizations also must ensure that their data isn’t manipulated or used against people. Tom gives an example of what must be avoided at all costs. Let’s say a client applies for a loan in a financial institution. Under no circumstances should that institution use AI to track the client’s personal data and use it against them, resulting in a loan ban. This hypothetical scenario is a clear violation of privacy and trust.

And according to Tom, “privacy is more important than ever.” The same goes for internal ethical standards organizations must develop.

Keeping Up With Cybersecurity

Like most revolutions, AI has come in fast and left many people (and organizations) scrambling to keep up. However, those who recognize that AI isn’t going anywhere have taken steps to embrace it and fully benefit from it. They see AI for what it truly is – a fundamental shift in how we approach technology and cybersecurity.

Those individuals have also chosen to advance their knowledge in the field by completing highly specialized and comprehensive programs like OPIT’s Enterprise Cybersecurity Master’s program. Coincidentally, this is also the program where you get to hear more valuable insights from Tom Vazdar, as he has essentially developed this course.

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