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    Computer Science Courses: Top Free & Paid Picks for Students

    OPIT - Open Institute of Technology
    OPIT - Open Institute of Technology
    Jul 1, 2023 • 9 min read
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    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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    CCN: Australia Tightens Crypto Oversight as Exchanges Expand, Testing Industry’s Appetite for Regulation
    OPIT - Open Institute of Technology
    OPIT - Open Institute of Technology
    Mar 31, 2025 • 3 min read

    Source:

    • CCN, published on March 29th, 2025

    By Kurt Robson

    Over the past few months, Australia’s crypto industry has undergone a rapid transformation following the government’s proposal to establish a stricter set of digital asset regulations.

    A series of recent enforcement measures and exchange launches highlight the growing maturation of Australia’s crypto landscape.

    Experts remain divided on how the new rules will impact the country’s burgeoning digital asset industry.

    New Crypto Regulation

    On March 21, the Treasury Department said that crypto exchanges and custody services will now be classified under similar rules as other financial services in the country.

    “Our legislative reforms will extend existing financial services laws to key digital asset platforms, but not to all of the digital asset ecosystem,” the Treasury said in a statement.

    The rules impose similar regulations as other financial services in the country, such as obtaining a financial license, meeting minimum capital requirements, and safeguarding customer assets.

    The proposal comes as Australian Prime Minister Anthony Albanese’s center-left Labor government prepares for a federal election on May 17.

    Australia’s opposition party, led by Peter Dutton, has also vowed to make crypto regulation a top priority of the government’s agenda if it wins.

    Australia’s Crypto Growth

    Triple-A data shows that 9.6% of Australians already own digital assets, with some experts believing new rules will push further adoption.

    Europe’s largest crypto exchange, WhiteBIT, announced it was entering the Australian market on Wednesday, March 26.

    The company said that Australia was “an attractive landscape for crypto businesses” despite its complexity.

    In March, Australia’s Swyftx announced it was acquiring New Zealand’s largest cryptocurrency exchange for an undisclosed sum.

    According to the parties, the merger will create the second-largest platform in Australia by trading volume.

    “Australia’s new regulatory framework is akin to rolling out the welcome mat for cryptocurrency exchanges,” Alexander Jader, professor of Digital Business at the Open Institute of Technology, told CCN.

    “The clarity provided by these regulations is set to attract a wave of new entrants,” he added.

    Jader said regulatory clarity was “the lifeblood of innovation.” He added that the new laws can expect an uptick “in both local and international exchanges looking to establish a foothold in the market.”

    However, Zoe Wyatt, partner and head of Web3 and Disruptive Technology at Andersen LLP, believes that while the new rules will benefit more extensive exchanges looking for more precise guidelines, they will not “suddenly turn Australia into a global crypto hub.”

    “The Web3 community is still largely looking to the U.S. in anticipation of a more crypto-friendly stance from the Trump administration,” Wyatt added.

    Read the full article below:

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    Agenda Digitale: Generative AI in the Enterprise – A Guide to Conscious and Strategic Use
    OPIT - Open Institute of Technology
    OPIT - Open Institute of Technology
    Mar 31, 2025 • 6 min read

    Source:

    By Zorina Alliata, Professor of Responsible Artificial Intelligence e Digital Business & Innovation at OPIT – Open Institute of Technology

    Integrating generative AI into your business means innovating, but also managing risks. Here’s how to choose the right approach to get value

    The adoption of generative AI in the enterprise is growing rapidly, bringing innovation to decision-making, creativity and operations. However, to fully exploit its potential, it is essential to define clear objectives and adopt strategies that balance benefits and risks.

    Over the course of my career, I have been fortunate to experience firsthand some major technological revolutions – from the internet boom to the “renaissance” of artificial intelligence a decade ago with machine learning.

    However, I have never seen such a rapid rate of adoption as the one we are experiencing now, thanks to generative AI. Although this type of AI is not yet perfect and presents significant risks – such as so-called “hallucinations” or the possibility of generating toxic content – ​​it fills a real need, both for people and for companies, generating a concrete impact on communication, creativity and decision-making processes.

    Defining the Goals of Generative AI in the Enterprise

    When we talk about AI, we must first ask ourselves what problems we really want to solve. As a teacher and consultant, I have always supported the importance of starting from the specific context of a company and its concrete objectives, without inventing solutions that are as “smart” as they are useless.

    AI is a formidable tool to support different processes: from decision-making to optimizing operations or developing more accurate predictive analyses. But to have a significant impact on the business, you need to choose carefully which task to entrust it with, making sure that the solution also respects the security and privacy needs of your customers .

    Understanding Generative AI to Adopt It Effectively

    A widespread risk, in fact, is that of being guided by enthusiasm and deploying sophisticated technology where it is not really needed. For example, designing a system of reviews and recommendations for films requires a certain level of attention and consumer protection, but it is very different from an X-ray reading service to diagnose the presence of a tumor. In the second case, there is a huge ethical and medical risk at stake: it is necessary to adapt the design, control measures and governance of the AI ​​to the sensitivity of the context in which it will be used.

    The fact that generative AI is spreading so rapidly is a sign of its potential and, at the same time, a call for caution. This technology manages to amaze anyone who tries it: it drafts documents in a few seconds, summarizes or explains complex concepts, manages the processing of extremely complex data. It turns into a trusted assistant that, on the one hand, saves hours of work and, on the other, fosters creativity with unexpected suggestions or solutions.

    Yet, it should not be forgotten that these systems can generate “hallucinated” content (i.e., completely incorrect), or show bias or linguistic toxicity where the starting data is not sufficient or adequately “clean”. Furthermore, working with AI models at scale is not at all trivial: many start-ups and entrepreneurs initially try a successful idea, but struggle to implement it on an infrastructure capable of supporting real workloads, with adequate governance measures and risk management strategies. It is crucial to adopt consolidated best practices, structure competent teams, define a solid operating model and a continuous maintenance plan for the system.

    The Role of Generative AI in Supporting Business Decisions

    One aspect that I find particularly interesting is the support that AI offers to business decisions. Algorithms can analyze a huge amount of data, simulating multiple scenarios and identifying patterns that are elusive to the human eye. This allows to mitigate biases and distortions – typical of exclusively human decision-making processes – and to predict risks and opportunities with greater objectivity.

    At the same time, I believe that human intuition must remain key: data and numerical projections offer a starting point, but context, ethics and sensitivity towards collaborators and society remain elements of human relevance. The right balance between algorithmic analysis and strategic vision is the cornerstone of a responsible adoption of AI.

    Industries Where Generative AI Is Transforming Business

    As a professor of Responsible Artificial Intelligence and Digital Business & Innovation, I often see how some sectors are adopting AI extremely quickly. Many industries are already transforming rapidly. The financial sector, for example, has always been a pioneer in adopting new technologies: risk analysis, fraud prevention, algorithmic trading, and complex document management are areas where generative AI is proving to be very effective.

    Healthcare and life sciences are taking advantage of AI advances in drug discovery, advanced diagnostics, and the analysis of large amounts of clinical data. Sectors such as retail, logistics, and education are also adopting AI to improve their processes and offer more personalized experiences. In light of this, I would say that no industry will be completely excluded from the changes: even “humanistic” professions, such as those related to medical care or psychological counseling, will be able to benefit from it as support, without AI completely replacing the relational and care component.

    Integrating Generative AI into the Enterprise: Best Practices and Risk Management

    A growing trend is the creation of specialized AI services AI-as-a-Service. These are based on large language models but are tailored to specific functionalities (writing, code checking, multimedia content production, research support, etc.). I personally use various AI-as-a-Service tools every day, deriving benefits from them for both teaching and research. I find this model particularly advantageous for small and medium-sized businesses, which can thus adopt AI solutions without having to invest heavily in infrastructure and specialized talent that are difficult to find.

    Of course, adopting AI technologies requires companies to adopt a well-structured risk management strategy, covering key areas such as data protection, fairness and lack of bias in algorithms, transparency towards customers, protection of workers, definition of clear responsibilities regarding automated decisions and, last but not least, attention to environmental impact. Each AI model, especially if trained on huge amounts of data, can require significant energy consumption.

    Furthermore, when we talk about generative AI and conversational models , we add concerns about possible inappropriate or harmful responses (so-called “hallucinations”), which must be managed by implementing filters, quality control and continuous monitoring processes. In other words, although AI can have disruptive and positive effects, the ultimate responsibility remains with humans and the companies that use it.

    Read the full article below (in Italian):

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