As we continue the slow march towards an AI-powered future, the coding and technical skills computer science graduates have are in increasingly high demand. This demand accounts for the high salary expectations of a computer science expert (the average salary in the field is €5,700 in Germany) and makes you more attractive as a hiring prospect to employers.

The challenge – finding a quality computer science course that provides the knowledge you need and has a reputation that forces employers to take notice. The four courses in this article (combined with related studies) transform you from computing enthusiast to sought-after computer scientist.

Top Free Computer Science Courses

Kicking off this computer science course list, we have a pair of the best free online computer science courses for building a foundation within the subject area.

Course 1 – CS50: Introduction to Computer Science (Harvard University)

If you’re looking for a free course that carries plenty of prestige, anything with the “Harvard” label attached is a good start. CS50 is a self-paced course, with Harvard estimating an 11-week completion time with between 10 and 20 hours of daily studying. It’s offered in English (sadly, no alternative languages exist at the time of writing) and it’s free to take, though you’ll pay $189 (approx. €175) for an official certificate.

The course covers programming language basics, starting with simple web-based HTML and advancing into Python and C. Advanced computing concepts, such as data structures and cybersecurity, are also covered, though you’re getting more of a baseline knowledge than specialized teaching. Think of the course as a computer science primer designed to give you a foundation that’s ideal for moving on to more complex studies. Add to that the Harvard seal of approval, which looks great on any CV, and you have a course that’s available globally and ideal for impressing employers.

Course 2 – CompTIA A+ (CompTIA)

This free course is A+ by name and mostly A+ by nature, with CompTIA advertising it as the perfect pathway to follow if you want a career in IT or computer sciences. You get an industry-recognized credential that employers will love, with the course focusing as much on practical skills (such as thinking on your feet in an IT crisis) as it does on theoretical instruction.

That’s not to say that theory isn’t covered. Once you’ve gotten to grips with the basics of the hardware and various operating systems, you’ll move into practical modules focused on networking, software, and cybersecurity. The course providers carry some industry weight, too, as titans like Dell, HP, and Intel recommend CompTIA’s courses for anybody who wants to break into the workforce.

There are some downsides – namely the minimal theoretical teaching makes it harder to understand why the practical things you’ll learn work. But as a companion piece to a more technical course (such as CS50), A+ is a great way to develop much-needed skills.

Other Notable Free Courses

The two courses listed above are far from the only free computer science course options available, with the following also being solid choices:

  • Python for Everybody – Coming from the University of Michigan, this course teaches the ins and outs of a popular programming language used in AI and machine learning.
  • IBM Data Science Professional Certificate – As something of a computer science-adjacent course, IBM’s certificate hones in on data science topics, such as visualization and machine learning models.
  • Introduction to Computer Science and Programming – Put together by the best minds at the Massachusetts Institute of Technology (MIT), this is a great course for beginners who are starting from square one when it comes to programming.

Top Paid Computer Science Courses

If you have some money to spend on your education (or access to student funding) these are the best courses for computer science students who value a more traditional paid education.

Course 1 – Bachelor in Modern Computer Science (OPIT)

As an entirely online course, OPIT’s offering allows you to learn at mostly your own pace, though you’re still expected to complete coursework and pass exams at appropriate times. It’s a three-year course (though two-year fast-track options are available) and it’s provided by an institution that has European Qualification Framework (EQF) credentials.

Granted, the course doesn’t come cheap, with its €3,600 per year tuition fees adding up to €10,800 for a three-year course. But that money buys you a comprehensive computer science education, starting with the basics of software development before moving on to modern concepts, like AI and cloud computing. Along the way, you’ll earn professional certifications from Microsoft and Google, giving you something tangible to place on your CV even as you’re still studying. Credit transfer is also available for students who started a computer science course elsewhere and want to transfer to OPIT’s offering.

Course 2 – Computational Science and Engineering (Technical University of Munich)

Perpetually hovering around the top 50 universities in the world (it ranked 50th in 2021), the Technical University of Munich (TUM) is like the MIT of Europe. With this course, TUM offers something for students who’ve already started on the computer science track and now feel ready to bring those skills together with applied math and engineering for a Master’s certification.

Lasting four semesters of full-time study, the course costs €152.30 and delivers 120 ECTS credits. You’ll hone in on numerical simulation, focusing on how to develop math-based problem-solving methods that help in developing systems and simulations. Theory is king in this course. But you’ll come away with such a solid grounding in that theory (as well as experience with simulated applications) that prepare you for a computer science and engineering career.

Other Notable Paid Courses

More thought goes into choosing a paid computer science course because you’re investing more than just time into your studies. If neither of the above two courses whets your appetite, the following are a few other notable providers offering courses to European and international students:

  • Computer Science BSc by Cambridge University – You get more than a degree from one of the UK’s most prestigious universities with this course. Given that Cambridge University lies in the heart of “Silicon Fen,” this course puts you in the ideal location to gain exposure to over 1,000 Cambridge-based tech companies.
  • Computational Thinking for Problem Solving – Devised by the Penn University faculty, this four-week online course starts by teaching the “pillars” of computational thinking, ending with an applied task using the Python programming language.
  • Computer Science 101L Master the Theory Behind Programming – Available via Udemy, this course costs about €69 or is available with a monthly subscription to Udemy. It features nearly 12 hours of recorded teaching sessions, alongside articles and other resources, that teach the basics of computer science.

Related Courses for a Well-Rounded Computer Science Education

The courses covered so far focus on computer science, with some variance in a few cases, which is like building the foundations for a house. To turn those foundations into something special (and something from which you can make a living), you may need a few more materials. Computer science-related courses give you those materials, with the following areas being great targets for further study.

Programming Languages

Programming is the beating heart of computer science. Every piece of software you’ll ever use has a program behind it. Most basic computer science courses teach general programming skills, often in Python, but further study into languages like SQL, Java, and C broadens your skillset to make you more attractive to employers.

Web Development

According to web3.career, the average European web developer picks up €70,000 per year, with potential to hit six figures with the right company and training. Many of the basics of web development are things you’ll pick up in a computer science course, though those looking for more formal certification should consider the following:

  • Full-Stack Web Development for Free (CodingNinjas)
  • Intro to HTML 5 (University of Michigan)
  • Web Developer on Google Digital Garage (Google)

Cybersecurity

The European Council’s research suggests that the cost of cybercrimes amounted to €5.5 trillion on the continent alone, with ransomware attacks being among the biggest threats facing EU companies. Therein lies an opportunity – businesses don’t want to lose trillions of euros and your cybersecurity skills could be the shield they need to fend off cyberattacks.

Top cybersecurity courses to consider include:

  • Google Cybersecurity (Google)
  • The Complete Cyber Security Course (Udemy)
  • Introduction to Cybersecurity Foundations (Infosec)

Data Science

Estimates state that the data science industry will have a 29% compound annual growth rate (CAGR) between 2022 and 2029, making it an ever-growing monolith in the computer science sector. Your ability to extract insights from massive datasets could be useful to employers and is buoyed by the following top courses:

  • Data Science MicroMasters (University of California San Diego)
  • CS109 Data Science (Harvard University)
  • Master of Science in Machine Learning and Data Science (Imperial College London)

Tips for Choosing the Right Computer Science Course

The computer sciences courses covered in this article run the gamut from beginner-level programs to full Master’s degrees. If you feel like you’re struggling to navigate the sheer volume of options available, these tips help you pick an appropriate course:

  • Be honest with yourself about your current skill level to choose a computer science course that challenges without being overwhelming.
  • Compare the course’s curriculum and learning outcomes with your goals to ensure you’ll get what you need from your studies.
  • Measure your time commitments (and how the course format allows for these commitments) against those the course demands.
  • Research the instructors who created the course and check online reviews from past and current students.
  • Determine whether the cost of the course (both monetary and time-wise) delivers a suitable return on your investment.

Start Your Computer Science Journey With the Right Course

Options abound when you’re looking for a computer science course, with quality free options sitting right alongside traditional paid courses. Whatever course you choose, always remember – one step in the right direction still means that you’re moving forward. By choosing a course, you take your first step into a constantly evolving and expanding world that could provide you with a lifelong career.

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Il Sole 24 Ore: Integrating Artificial Intelligence into the Enterprise – Challenges and Opportunities for CEOs and Management
OPIT - Open Institute of Technology
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Apr 14, 2025 6 min read

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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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