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.
Related posts
Source:
- The Yuan, Published on October 25th, 2024.
By Zorina Alliata
Artificial intelligence is a classic example of a mismatch between perceptions and reality, as people tend to overlook its positive aspects and fear it far more than what is warranted by its actual capabilities, argues AI strategist and professor Zorina Alliata.
ALEXANDRIA, VIRGINIA – In recent years, artificial intelligence (AI) has grown and developed into something much bigger than most people could have ever expected. Jokes about robots living among humans no longer seem so harmless, and the average person began to develop a new awareness of AI and all its uses. Unfortunately, however – as is often a human tendency – people became hyper-fixated on the negative aspects of AI, often forgetting about all the good it can do. One should therefore take a step back and remember that humanity is still only in the very early stages of developing real intelligence outside of the human brain, and so at this point AI is almost like a small child that humans are raising.
AI is still developing, growing, and adapting, and like any new tech it has its drawbacks. At one point, people had fears and doubts about electricity, calculators, and mobile phones – but now these have become ubiquitous aspects of everyday life, and it is not difficult to imagine a future in which this is the case for AI as well.
The development of AI certainly comes with relevant and real concerns that must be addressed – such as its controversial role in education, the potential job losses it might lead to, and its bias and inaccuracies. For every fear, however, there is also a ray of hope, and that is largely thanks to people and their ingenuity.
Looking at education, many educators around the world are worried about recent developments in AI. The frequently discussed ChatGPT – which is now on its fourth version – is a major red flag for many, causing concerns around plagiarism and creating fears that it will lead to the end of writing as people know it. This is one of the main factors that has increased the pessimistic reporting about AI that one so often sees in the media.
However, when one actually considers ChatGPT in its current state, it is safe to say that these fears are probably overblown. Can ChatGPT really replace the human mind, which is capable of so much that AI cannot replicate? As for educators, instead of assuming that all their students will want to cheat, they should instead consider the options for taking advantage of new tech to enhance the learning experience. Most people now know the tell-tale signs for identifying something that ChatGPT has written. Excessive use of numbered lists, repetitive language and poor comparison skills are just three ways to tell if a piece of writing is legitimate or if a bot is behind it. This author personally encourages the use of AI in the classes I teach. This is because it is better for students to understand what AI can do and how to use it as a tool in their learning instead of avoiding and fearing it, or being discouraged from using it no matter the circumstances.
Educators should therefore reframe the idea of ChatGPT in their minds, have open discussions with students about its uses, and help them understand that it is actually just another tool to help them learn more efficiently – and not a replacement for their own thoughts and words. Such frank discussions help students develop their critical thinking skills and start understanding their own influence on ChatGPT and other AI-powered tools.
By developing one’s understanding of AI’s actual capabilities, one can begin to understand its uses in everyday life. Some would have people believe that this means countless jobs will inevitably become obsolete, but that is not entirely true. Even if AI does replace some jobs, it will still need industry experts to guide it, meaning that entirely new jobs are being created at the same time as some older jobs are disappearing.
Adapting to AI is a new challenge for most industries, and it is certainly daunting at times. The reality, however, is that AI is not here to steal people’s jobs. If anything, it will change the nature of some jobs and may even improve them by making human workers more efficient and productive. If AI is to be a truly useful tool, it will still need humans. One should remember that humans working alongside AI and using it as a tool is key, because in most cases AI cannot do the job of a person by itself.
Is AI biased?
Why should one view AI as a tool and not a replacement? The main reason is because AI itself is still learning, and AI-powered tools such as ChatGPT do not understand bias. As a result, whenever ChatGPT is asked a question it will pull information from anywhere, and so it can easily repeat old biases. AI is learning from previous data, much of which is biased or out of date. Data about home ownership and mortgages, e.g., are often biased because non-white people in the United States could not get a mortgage until after the 1960s. The effect on data due to this lending discrimination is only now being fully understood.
AI is certainly biased at times, but that stems from human bias. Again, this just reinforces the need for humans to be in control of AI. AI is like a young child in that it is still absorbing what is happening around it. People must therefore not fear it, but instead guide it in the right direction.
For AI to be used as a tool, it must be treated as such. If one wanted to build a house, one would not expect one’s tools to be able to do the job alone – and AI must be viewed through a similar lens. By acknowledging this aspect of AI and taking control of humans’ role in its development, the world would be better placed to reap the benefits and quash the fears associated with AI. One should therefore not assume that all the doom and gloom one reads about AI is exactly as it seems. Instead, people should try experimenting with it and learning from it, and maybe soon they will realize that it was the best thing that could have happened to humanity.
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Source:
- The European Business Review, Published on October 27th, 2024.
By Lokesh Vij
Lokesh Vij is a Professor of BSc in Modern Computer Science & MSc in Applied Data Science & AI at Open Institute of Technology. With over 20 years of experience in cloud computing infrastructure, cybersecurity and cloud development, Professor Vij is an expert in all things related to data and modern computer science.
In today’s rapidly evolving technological landscape, the fields of blockchain and cloud computing are transforming industries, from finance to healthcare, and creating new opportunities for innovation. Integrating these technologies into education is not merely a trend but a necessity to equip students with the skills they need to thrive in the future workforce. Though both technologies are independently powerful, their potential for innovation and disruption is amplified when combined. This article explores the pressing questions surrounding the inclusion of blockchain and cloud computing in education, providing a comprehensive overview of their significance, benefits, and challenges.
The Technological Edge and Future Outlook
Cloud computing has revolutionized how businesses and individuals’ access and manage data and applications. Benefits like scalability, cost efficiency (including eliminating capital expenditure – CapEx), rapid innovation, and experimentation enable businesses to develop and deploy new applications and services quickly without the constraints of traditional on-premises infrastructure – thanks to managed services where cloud providers manage the operating system, runtime, and middleware, allowing businesses to focus on development and innovation. According to Statista, the cloud computing market is projected to reach a significant size of Euro 250 billion or even higher by 2028 (from Euro 110 billion in 2024), with a substantial Compound Annual Growth Rate (CAGR) of 22.78%. The widespread adoption of cloud computing by businesses of all sizes, coupled with the increasing demand for cloud-based services and applications, fuels the need for cloud computing professionals.
Blockchain, a distributed ledger technology, has paved the way by providing a secure, transparent, and tamper-proof way to record transactions (highly resistant to hacking and fraud). In 2021, European blockchain startups raised $1.5 billion in funding, indicating strong interest and growth potential. Reports suggest the European blockchain market could reach $39 billion by 2026, with a significant CAGR of over 47%. This growth is fueled by increasing adoption in sectors like finance, supply chain, and healthcare.
Addressing the Skills Gap
Reports from the World Economic Forum indicate that 85 million jobs may be displaced by a shift in the division of labor between humans and machines by 2025. However, 97 million new roles may emerge that are more adapted to the new division of labor between humans, machines, and algorithms, many of which will require proficiency in cloud computing and blockchain.
Furthermore, the World Economic Forum predicts that by 2027, 10% of the global GDP will be tokenized and stored on the blockchain. This massive shift means a surge in demand for blockchain professionals across various industries. Consider the implications of 10% of the global GDP being on the blockchain: it translates to a massive need for people who can build, secure, and manage these systems. We’re talking about potentially millions of jobs worldwide.
The European Blockchain Services Infrastructure (EBSI), an EU initiative, aims to deploy cross-border blockchain services across Europe, focusing on areas like digital identity, trusted data sharing, and diploma management. The EU’s MiCA (Crypto-Asset Regulation) regulation, expected to be fully implemented by 2025, will provide a clear legal framework for crypto-assets, fostering innovation and investment in the blockchain space. The projected growth and supportive regulatory environment point to a rising demand for blockchain professionals in Europe. Developing skills related to EBSI and its applications could be highly advantageous, given its potential impact on public sector blockchain adoption. Understanding the MiCA regulation will be crucial for blockchain roles related to crypto-assets and decentralized finance (DeFi).
Furthermore, European businesses are rapidly adopting digital technologies, with cloud computing as a core component of this transformation. GDPR (Data Protection Regulations) and other data protection laws push businesses to adopt secure and compliant cloud solutions. Many European countries invest heavily in cloud infrastructure and promote cloud adoption across various sectors. Artificial intelligence and machine learning will be deeply integrated into cloud platforms, enabling smarter automation, advanced analytics, and more efficient operations. This allows developers to focus on building applications without managing servers, leading to faster development cycles and increased scalability. Processing data closer to the source (like on devices or local servers) will become crucial for applications requiring real-time responses, such as IoT and autonomous vehicles.
The projected growth indicates a strong and continuous demand for blockchain and cloud professionals in Europe and worldwide. As we stand at the “crossroads of infinity,” there is a significant skill shortage, which will likely increase with the rapid adoption of these technologies. A 2023 study by SoftwareOne found that 95% of businesses globally face a cloud skills gap. Specific skills in high demand include cloud security, cloud-native development, and expertise in leading cloud platforms like AWS, Azure, and Google Cloud. The European Commission’s Digital Economy and Society Index (DESI) highlights a need for improved digital skills in areas like blockchain to support the EU’s digital transformation goals. A 2023 report by CasperLabs found that 90% of businesses in the US, UK, and China adopt blockchain, but knowledge gaps and interoperability challenges persist.
The Role of Educational Institutions
This surge in demand necessitates a corresponding increase in qualified individuals who can design, implement, and manage cloud-based and blockchain solutions. Educational institutions have a critical role to play in bridging this widening skills gap and ensuring a pipeline of talent ready to meet the demands of this burgeoning industry.
To effectively prepare the next generation of cloud computing and blockchain experts, educational institutions need to adopt a multi-pronged approach. This includes enhancing curricula with specialized programs, integrating cloud and blockchain concepts into existing courses, and providing hands-on experience with leading technology platforms.
Furthermore, investing in faculty development to ensure they possess up-to-date knowledge and expertise is crucial. Collaboration with industry partners through internships, co-teach programs, joint research projects, and mentorship programs can provide students with invaluable real-world experience and insights.
Beyond formal education, fostering a culture of lifelong learning is essential. Offering continuing education courses, boot camps, and online resources enables professionals to upskill or reskill and stay abreast of the latest advancements in cloud computing. Actively promoting awareness of career paths and opportunities in this field and facilitating connections with potential employers can empower students to thrive in the dynamic and evolving landscape of cloud computing and blockchain technologies.
By taking these steps, educational institutions can effectively prepare the young generation to fill the skills gap and thrive in the rapidly evolving world of cloud computing and blockchain.
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