If a theoretical data scientist is somebody who’s mastered the art of extracting and analyzing large datasets, an applied data scientist is someone who can put that mastery into real-world practice. They’re insight specialists. And those insights come using techniques like machine learning and data mining to parse through extensive datasets to find patterns and outcomes.

As a prospective Master of applied data science, you may wonder if this career path is the right choice for you. It is, as long as you want to be part of a growing industry. According to Precedence Research, the data science industry is expected to achieve a compound annual growth rate (CAGR) of 16.43% between 2022 and 2030. That CAGR translates into growth from $112.2 billion in value (approx. €103 billion) to $378.7 billion (approx. €349 billion).

That growth alone demonstrates why getting an applied data science MSc could be valuable to your career prospect. Let’s look at three of the top courses on offer to European and international students.

Top MSc Programs in Applied Data Science – Our Criteria

Before digging into the best Master applied data science programs, it’s important to establish the criteria we’ve used to make our selections. The following five factors play a role:

  • Reputation and ranking – While overall university rankings denote the quality of an establishment, we’re more interested in the reputation the specific course has in the industry.
  • Curriculum and Sspecialization – What will you study and how will the topics you delve into lead to further specialization? We aim to answer both questions for our selections.
  • Faculty expertise – When analyzing faculty expertise, we’re looking for a combination of experienced educators and mentors with real-world experience in data science work.
  • Industry connections and partnerships – You want to use your MSc in applied data science to find work. A university that has strong connections to industry leaders (either through faculty or partnerships) can propel you forward in your career.
  • Career support and alumni network – Speaking of connections, a good alumni network exposes you to peers who can help your career. Combine that with in-house career support from the university, and you get a course that offers more than a basic education.

Top MSc Programs Explored

After applying the above criteria, we’ve come up with a list of three Master of applied data science programs to pique your interest.

Program 1 – Master in Applied Data Science & AI (Open Institute of Technology)

Available as a fully online course for those who value self-learning, the Open Institute of Technology’s (OPIT’s) program lasts for 18 months with costs starting from €4,950. There’s also a fast-track option available for those who can commit to more extensive studies, with that program offering the same degree in just 12 months.

The educational aspect of the course is divided across two terms. In the first term, you’ll focus primarily on principles and techniques in areas such as Python programming, machine learning, and how to use data science to solve business problems. The second term gets more practical as you start to focus on applications of data science (and AI) in the real world before digging into the ethics behind your work.

As for credentials, OPIT is an accredited institution under the European Qualification Framework and its MSc was created by Professor Lorenzo Livi. Serving as program head, Livi brings the expertise he’s developed through teaching and research at both the University of Exeter and the University of Manitoba to the program.

It’s this focus on attracting international faculty that’s the most attractive part of the course. Beyond Livi, the faculty includes professors from institutions as diverse as the University of California, University of Copenhagen, Microsoft, and the Naval Research Laboratory. This mix of academic excellence and professors with real-world experience can lead you to exciting career opportunities and connections.

Program 2 – Master of Science in Data Science (ETH Zurich)

Ranked as the ninth-best computer science university in the world by Research.com, ETH Zurich has a program that stands out thanks to its Data Science Laboratory. This dedicated facility allows students to utilize their theoretical knowledge on simulated practical problems. Process modeling and data validation get put into practice in this lab, all under the oversight of an experienced mentor.

Speaking of faculty, several members of ETH Zurich specialize in teaching data science in relation to the medical field. Both Gunnar Rätsch, a full professor at the university, and Julia Vogt, an assistant professor can directly aid students who wish to apply their data science expertise to medicine.

Career support comes in the form of a dedicated Career Center, which serves as a central hub for students and the companies with which the university partners. ETH encourages partnership through industry events, such as its Industry Day, which encourage local and national businesses to meet with and discuss the work of its students. These events may prove vital to starting your data science career before you’ve even completed your Master of applied data science.

Coming back to the program, it’s a two-year full-time course through which you’ll earn 120 credits per the European Credit Transfer and Accumulation System (ECTS). Prospective students need to have at least 180 ECTS credits from a relevant Bachelor’s degree, such as a BSc in computer science or mathematics. The program costs CHF 730 (approx. €749) per semester, with the option to make voluntary contributions to things like the university’s student union and its Solidarity Fund for Foreign Students.

Program 3 – MSc Data Science (IU International University of Applied Science)

Our final program takes us to Germany and one of the most flexible applied data science MSc programs in Europe. Offered in conjunction with London South Bank University, this program results in graduation with a dual degree with both German and British accreditation. You have a choice between taking the two-year program for €556 per month or a pair of part-time programs. The first of the part-time options lasts for 36 months, costing €417 per month, with the second being a 48-month course costing €329 per month.

The course itself focuses primarily on current developments in the data sector, with modules on Big Data, infrastructure engineering, and software development included. The first semester introduces you to machine learning and deep learning concepts, in addition to offering a model engineering case study so you can get your feet wet with applied data science. The second semester makes room for specialization, as you choose an elective that may focus on Big Data, autonomous driving, or smart manufacturing methods.

Faculty members include Professor Thomas Zoller, who oversees the university’s BSc in data science program in addition to contributing to its Master’s program. His expertise lies in machine learning in the context of image processing, in addition to the use of AI and advanced analytics in digital transformation.

As you move closer to wanting to start your career, IU International’s Career Office comes into play. It holds weekly group career talks, both online and on-campus, in addition to daily slots for one-to-one chats with advisors over Zoom or email. You also get access to the university’s Jobteaser platform, which puts you in direct contact with potential recruiters.

Factors to Consider When Choosing an Applied Data Science MSc

The three programs highlighted above each offer a combination of a stellar education and industry connections that help you to get your data science career started. But if you want to do further research into applied data science MSc programs, these are the factors to consider.

Your Personal Goals

Though it may seem obvious to state, your personal goals play a huge role in your decision. For example, somebody who wishes to work in the medical field may favor ETH Zurich’s offering due to the expertise of its faculty, whereas that course may not be the best choice for those interested in finance. Think about what you want to achieve and which program aligns with those goals.

Program Cost

A Master of applied data science doesn’t come cheap. Most courses cost several thousand euros, though you’ll often find that online courses are more manageable from a cost perspective. Consider the program cost and research financial aid options, such as those highlighted on the EURAXESS portal, when making your choice.

Program Format

A full-time MSc in applied data science may be great for a young student with no other commitments. But it won’t work so well when you’re trying to fit your education around work, life, and your family. Think about the time commitment the program asks of you. Many find that a part-time or self-learning-driven online course is easier to fit around their schedules than a full-time on-campus program.

Location and Campus Facilities

If you opt for an online course then location isn’t an issue – you can study from home. But those studying on-campus have to consider the location (is the university situated in a business hub, for example) and the facilities offered on-site to help them further their data science careers.

Networking Opportunities

Networking opportunities can come in many forms in a Master of applied data science program. Faculty is the obvious source of connections, with many educators having worked (or still working) directly in the industry, but don’t underestimate the connective powers of your peers. Furthermore, take advantage of any career support facilities your university offers to get yourself in front of prospective employers.

Get Your MSc in Applied Data Science

Think of choosing an applied data science MSc in the same way you’d think about making an investment. You want that investment (both in time and money) to offer a suitable return. The three programs listed here offer superb qualifications and give you the real-world experience needed to forge a career in the applied data science sector. Choose the program that suits your needs, or, use the advice provided to research other programs that are closer to home or more in line with your career goals.

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Il Sole 24 Ore: Integrating Artificial Intelligence into the Enterprise – Challenges and Opportunities for CEOs and Management
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Expert Pierluigi Casale analyzes the adoption of AI by companies, the ethical and regulatory challenges and the differentiated approach between large companies and SMEs

By Gianni Rusconi

Easier said than done: to paraphrase the well-known proverb, and to place it in the increasingly large collection of critical issues and opportunities related to artificial intelligence, the task that CEOs and management have to adequately integrate this technology into the company is indeed difficult. Pierluigi Casale, professor at OPIT (Open Institute of Technology, an academic institution founded two years ago and specialized in the field of Computer Science) and technical consultant to the European Parliament for the implementation and regulation of AI, is among those who contributed to the definition of the AI ​​Act, providing advice on aspects of safety and civil liability. His task, in short, is to ensure that the adoption of artificial intelligence (primarily within the parliamentary committees operating in Brussels) is not only efficient, but also ethical and compliant with regulations. And, obviously, his is not an easy task.

The experience gained over the last 15 years in the field of machine learning and the role played in organizations such as Europol and in leading technology companies are the requirements that Casale brings to the table to balance the needs of EU bodies with the pressure exerted by American Big Tech and to preserve an independent approach to the regulation of artificial intelligence. A technology, it is worth remembering, that implies broad and diversified knowledge, ranging from the regulatory/application spectrum to geopolitical issues, from computational limitations (common to European companies and public institutions) to the challenges related to training large-format language models.

CEOs and AI

When we specifically asked how CEOs and C-suites are “digesting” AI in terms of ethics, safety and responsibility, Casale did not shy away, framing the topic based on his own professional career. “I have noticed two trends in particular: the first concerns companies that started using artificial intelligence before the AI ​​Act and that today have the need, as well as the obligation, to adapt to the new ethical framework to be compliant and avoid sanctions; the second concerns companies, like the Italian ones, that are only now approaching this topic, often in terms of experimental and incomplete projects (the expression used literally is “proof of concept”, ed.) and without these having produced value. In this case, the ethical and regulatory component is integrated into the adoption process.”

In general, according to Casale, there is still a lot to do even from a purely regulatory perspective, due to the fact that there is not a total coherence of vision among the different countries and there is not the same speed in implementing the indications. Spain, in this regard, is setting an example, having established (with a royal decree of 8 November 2023) a dedicated “sandbox”, i.e. a regulatory experimentation space for artificial intelligence through the creation of a controlled test environment in the development and pre-marketing phase of some artificial intelligence systems, in order to verify compliance with the requirements and obligations set out in the AI ​​Act and to guide companies towards a path of regulated adoption of the technology.

Read the full article below (in Italian):

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The Lucky Future: How AI Aims to Change Everything
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 10, 2025 7 min read

There is no question that the spread of artificial intelligence (AI) is having a profound impact on nearly every aspect of our lives.

But is an AI-powered future one to be feared, or does AI offer the promise of a “lucky future.”

That “lucky future” prediction comes from Zorina Alliata, principal AI Strategist at Amazon and AI faculty member at Georgetown University and the Open Institute of Technology (OPIT), in her recent webinar “The Lucky Future: How AI Aims to Change Everything” (February 18, 2025).

However, according to Alliata, such a future depends on how the technology develops and whether strategies can be implemented to mitigate the risks.

How AI Aims to Change Everything

For many people, AI is already changing the way they work. However, more broadly, AI has profoundly impacted how we consume information.

From the curation of a social media feed and the summary answer to a search query from Gemini at the top of your Google results page to the AI-powered chatbot that resolves your customer service issues, AI has quickly and quietly infiltrated nearly every aspect of our lives in the past few years.

While there have been significant concerns recently about the possibly negative impact of AI, Alliata’s “lucky future” prediction takes these fears into account. As she detailed in her webinar, a future with AI will have to take into consideration:

  • Where we are currently with AI and future trajectories
  • The impact AI is having on the job landscape
  • Sustainability concerns and ethical dilemmas
  • The fundamental risks associated with current AI technology

According to Alliata, by addressing these risks, we can craft a future in which AI helps individuals better align their needs with potential opportunities and limitations of the new technology.

Industry Applications of AI

While AI has been in development for decades, Alliata describes a period known as the “AI winter” during which educators like herself studied AI technology, but hadn’t arrived at a point of practical applications. Contributing to this period of uncertainty were concerns over how to make AI profitable as well.

That all changed about 10-15 years ago when machine learning (ML) improved significantly. This development led to a surge in the creation of business applications for AI. Beginning with automation and robotics for repetitive tasks, the technology progressed to data analysis – taking a deep dive into data and finding not only new information but new opportunities as well.

This further developed into generative AI capable of completing creative tasks. Generative AI now produces around one billion words per day, compared to the one trillion produced by humans.

We are now at the stage where AI can complete complex tasks involving multiple steps. In her webinar, Alliata gave the example of a team creating storyboards and user pathways for a new app they wanted to develop. Using photos and rough images, they were able to use AI to generate the code for the app, saving hundreds of hours of manpower.

The next step in AI evolution is Artificial General Intelligence (AGI), an extremely autonomous level of AI that can replicate or in some cases exceed human intelligence. While the benefits of such technology may readily be obvious to some, the industry itself is divided as to not only whether this form of AI is close at hand or simply unachievable with current tools and technology, but also whether it should be developed at all.

This unpredictability, according to Alliata, represents both the excitement and the concerns about AI.

The AI Revolution and the Job Market

According to Alliata, the job market is the next area where the AI revolution can profoundly impact our lives.

To date, the AI revolution has not resulted in widespread layoffs as initially feared. Instead of making employees redundant, many jobs have evolved to allow them to work alongside AI. In fact, AI has also created new jobs such as AI prompt writer.

However, the prediction is that as AI becomes more sophisticated, it will need less human support, resulting in a greater job churn. Alliata shared statistics from various studies predicting as many as 27% of all jobs being at high risk of becoming redundant from AI and 40% of working hours being impacted by language learning models (LLMs) like Chat GPT.

Furthermore, AI may impact some roles and industries more than others. For example, one study suggests that in high-income countries, 8.5% of jobs held by women were likely to be impacted by potential automation, compared to just 3.9% of jobs held by men.

Is AI Sustainable?

While Alliata shared the many ways in which AI can potentially save businesses time and money, she also highlighted that it is an expensive technology in terms of sustainability.

Conducting AI training and processing puts a heavy strain on central processing units (CPUs), requiring a great deal of energy. According to estimates, Chat GPT 3 alone uses as much electricity per day as 121 U.S. households in an entire year. Gartner predicts that by 2030, AI could consume 3.5% of the world’s electricity.

To reduce the energy requirements, Alliata highlighted potential paths forward in terms of hardware optimization, such as more energy-efficient chips, greater use of renewable energy sources, and algorithm optimization. For example, models that can be applied to a variety of uses based on prompt engineering and parameter-efficient tuning are more energy-efficient than training models from scratch.

Risks of Using Generative AI

While Alliata is clearly an advocate for the benefits of AI, she also highlighted the risks associated with using generative AI, particularly LLMs.

  • Uncertainty – While we rely on AI for answers, we aren’t always sure that the answers provided are accurate.
  • Hallucinations – Technology designed to answer questions can make up facts when it does not know the answer.
  • Copyright – The training of LLMs often uses copyrighted data for training without permission from the creator.
  • Bias – Biased data often trains LLMs, and that bias becomes part of the LLM’s programming and production.
  • Vulnerability – Users can bypass the original functionality of an LLM and use it for a different purpose.
  • Ethical Risks – AI applications pose significant ethical risks, including the creation of deepfakes, the erosion of human creativity, and the aforementioned risks of unemployment.

Mitigating these risks relies on pillars of responsibility for using AI, including value alignment of the application, accountability, transparency, and explainability.

The last one, according to Alliata, is vital on a human level. Imagine you work for a bank using AI to assess loan applications. If a loan is denied, the explanation you give to the customer can’t simply be “Because the AI said so.” There needs to be firm and explainable data behind the reasoning.

OPIT’s Masters in Responsible Artificial Intelligence explores the risks and responsibilities inherent in AI, as well as others.

A Lucky Future

Despite the potential risks, Alliata concludes that AI presents even more opportunities and solutions in the future.

Information overload and decision fatigue are major challenges today. Imagine you want to buy a new car. You have a dozen features you desire, alongside hundreds of options, as well as thousands of websites containing the relevant information. AI can help you cut through the noise and narrow the information down to what you need based on your specific requirements.

Alliata also shared how AI is changing healthcare, allowing patients to understand their health data, make informed choices, and find healthcare professionals who meet their needs.

It is this functionality that can lead to the “lucky future.” Personalized guidance based on an analysis of vast amounts of data means that each person is more likely to make the right decision with the right information at the right time.

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