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Far from being a temporary educational measure that came into its own during the pandemic, online education is providing students from all over the world with new ways to learn. That’s proven by statistics from Oxford Learning College, which point out that over 100 million students are now enrolled in some form of online course.
The demand for these types of courses clearly exists.
In fact, the same organization indicates that educational facilities that introduce online learning see a 42% increase in income – on average – suggesting that the demand is there.
Enter the Open Institute of Technology (OPIT).
Delivering three online courses – a Bachelor’s degree in computer science and two Master’s degrees – with more to come, OPIT is positioning itself as a leader in the online education space. But why is that? After all, many institutions are making the jump to e-learning, so what separates OPIT from the pack?
Here, you’ll discover the answers as you delve into the five reasons why you should trust OPIT for your online education.
Reason 1 – A Practical Approach
OPIT focuses on computer science education – a field in which theory often dominates the educational landscape. The organization’s Rector, Professor Francesco Profumo, makes this clear in a press release from June 2023. He points to a misalignment between what educators are teaching computer science students and what the labor market actually needs from those students as a key problem.
“The starting point is the awareness of the misalignment,” he says when talking about how OPIT structures its online courses. “That so-called mismatch is generated by too much theory and too little practical approach.” In other words, students in many classes spend far too much time learning the “hows” and “whys” behind computerized systems without actually getting their hands dirty with real work that gives them practical experience in using those systems.
OPIT takes a different approach.
It has developed a didactic approach that focuses far more on the practical element than other courses. That approach is delivered through a combination of classroom sessions – such as live lessons and masterclasses – and practical work offered through quizzes and exercises that mimic real-world situations.
An OPIT student doesn’t simply learn how computers work. They put their skills into practice through direct programming and application, equipping them with skills that are extremely attractive to major employers in the tech field and beyond.
Reason 2 – Flexibility Combined With Support
Flexibility in how you study is one of the main benefits of any online course.
You control when you learn and how you do it, creating an environment that’s beneficial to your education rather than being forced into a classroom setting with which you may not feel comfortable. This is hardly new ground. Any online educational platform can claim that it offers “flexibility” simply because it provides courses via the web.
Where OPIT differs is that it combines that flexibility with unparalleled support bolstered by the experiences of teachers employed from all over the world. The founder and director of OPIT, Riccardo Ocleppo, sheds more light on this difference in approach when he says, “We believe that education, even if it takes place physically at a distance, must guarantee closeness on all other aspects.” That closeness starts with the support offered to students throughout their entire study period.
Tutors are accessible to students at all times. Plus, every participant benefits from weekly professor interactions, ensuring they aren’t left feeling stuck on an educational “island” and have to rely solely on themselves for their education. OPIT further counters the potential isolation that comes with online learning with a Student Support team to guide students through any difficulties they may have with their courses.
In this focus on support, OPIT showcases one of its main differences from other online platforms.
You don’t simply receive course material before being told to “get on with it.” You have the flexibility to learn at your own pace while also having a support structure that serves as a foundation for that learning.
Reason 3 – OPIT Can Adapt to Change Quickly
The field of computer science is constantly evolving.
In the 2020s alone, we’ve seen the rise of generative AI – spurred on by the explosive success of services like ChatGPT – and how those new technologies have changed the way that people use computers.
Riccardo Ocleppo has seen the impact that these constant evolutions have had on students. Before founding OPIT, he was an entrepreneur who received first-hand experience of the fact that many traditional educational institutions struggle to adapt to change.
“Traditional educational institutions are very slow to adapt to this wave of new technologies and trends within the educational sector,” he says. He points to computer science as a particular issue, highlighting the example of a board in Italy of which he is a member. That board – packed with some of the country’s most prestigious tech universities – spent three years eventually deciding to add just two modules on new and emerging technologies to their study programs.
That left Ocleppo feeling frustrated.
When he founded OPIT, he did so intending to make it an adaptable institution in which courses were informed by what the industry needs. Every member of its faculty is not only a superb teacher but also somebody with experience working in industry. Speaking of industry, OPIT collaborates with major companies in the tech field to ensure its courses deliver the skills that those organizations expect from new candidates.
This confronts frustration on both sides. For companies, an OPIT graduate is one for which they don’t need to bridge a “skill gap” between what they’ve learned and what the company needs. For you, as a student, it means that you’re developing skills that make you a more desirable prospect once you have your degree.
Reason 4 – OPIT Delivers Tier One Education
Despite their popularity, online courses can still carry a stigma of not being “legitimate” in the face of more traditional degrees. Ocleppo is acutely aware of this fact, which is why he’s quick to point out that OPIT always aims to deliver a Tier One education in the computer science field.
“That means putting together the best professors who create superb learning material, all brought together with a teaching methodology that leverages the advancements made in online teaching,” he says.
OPIT’s degrees are all accredited by the European Union to support this approach, ensuring they carry as much weight as any other European degree. It’s accredited by both the European Qualification Framework (EQF) and the Malta Qualification Framework (MQF), with all of its courses having full legal value throughout Europe.
It’s also here where we see OPIT’s approach to practicality come into play via its course structuring.
Take its Bachelor’s degree in computer science as an example.
Yes, that course starts with a focus on theoretical and foundational knowledge. Building a computer and understanding how the device processes instructions is vital information from a programming perspective. But once those foundations are in place, OPIT delivers on its promises of covering the most current topics in the field.
Machine learning, cloud computing, data science, artificial intelligence, and cybersecurity – all valuable to employers – are taught at the undergraduate level. Students benefit from a broader approach to computer science than most institutions are capable of, rather than bogging them down in theory that serves little practical purpose.
Reason 5 – The Learning Experience
Let’s wrap up by honing in on what it’s actually like for students to learn with OPIT.
After all, as Ocleppo points out, one of the main challenges with online education is that students rarely have defined checkpoints to follow. They can start feeling lost in the process, confronted with a metaphorical ocean of information they need to learn, all in service of one big exam at the end.
Alternatively, some students may feel the temptation to not work through the materials thoroughly, focusing instead on passing a final exam. The result is that those students may pass, but they do so without a full grasp of what they’ve learned – a nightmare for employers who already have skill gaps to handle.
OPIT confronts both challenges by focusing on a continuous learning methodology. Assessments – primarily practical – take place throughout the course, serving as much-needed checkpoints for evaluating progress. When combined with the previously mentioned support that OPIT offers, this approach has led to courses that are created from scratch in service of the student’s actual needs.
Choose OPIT for Your Computer Science Education
At OPIT, the focus lies as much on helping students to achieve their dream careers as it does on teaching them. All courses are built collaboratively. With a dedicated faculty combined with major industry players, such as Google and Microsoft, it delivers materials that bridge the skill gap seen in the computer science field today.
There’s also more to come.
Beyond the three degrees OPIT offers, the institution plans to add more. Game development, data science, and cloud computing, to name a few, will receive dedicated degrees in the coming months, accentuating OPIT’s dedication to adapting to the continuous evolution of the computer science industry. Discover OPIT today – your journey into computing starts with the best online education institution available.
With immense pride and anticipation, we announce the inaugural event for the OPIT – Open Institute of Technology academic year. As pioneers in the new era of Higher Education, this event encapsulates the very ethos of what OPIT represents. Not just an event, but the commencement of a journey to pave the way for the next generation of leaders in the field of IT.
- Date: September 12th, 2023
- Time: 5.00-6.00 PM CEST
- Platform: Online
- Registration: Link
- Official Introduction: Mr. Riccardo Ocleppo, the founder of OPIT, paints a picture of the Institution’s foundational pillars and what prospective students can expect from their academic journey.
- Learning Model Presentation: Prof. Francesco Profumo, our esteemed Rector, delves deep into the heart of OPIT’s avant-garde learning experience, shedding light on its core tenets and alignment with the demands of the contemporary job market.
- Accreditation and Quality Assurance: The Malta Minister of Education, Dr. Clifton Grima, offers insights into the robust educational framework of Malta and the stringent quality assurance measures in place.
- The Future of Jobs in the Era of AI: Prof. Alexiei Dingli navigates the evolving terrains of the job market under the shadow of AI’s relentless march, emphasizing the pivotal role of institutions like OPIT.
- The Impact of Digitalization on a Global Scale: Dr. Bernardo Calzadilla Sarmiento, former Managing Director of UNIDO (United Nations Industrial Development Organization) offers a panoramic view of the digital revolution sweeping across the globe and its profound implications on industry, economy, and education.
- Q&A Session: Led by Greta Maiocchi, the Head of Admissions at OPIT, this segment is dedicated to addressing queries, clearing doubts, and facilitating an open dialogue.
In a world where AI and digital innovation are reshaping boundaries, institutions like OPIT emerge as guiding lights. Join us at this pivotal juncture as we navigate the AI-driven future, fortified by our dedication to education, foresight, and ambition.
Join us in marking the beginning of an era. Let’s shape the future, together.
Register here for the event.
For 68% of Italian students, the perfect training opens up the world of work and connects them to companies. And 72% of students prefer the hybrid educational model.
The data comes from a survey of 1,600 members of the Docsity community by OPIT – The Open Institute of Technology.
OPIT founder Riccardo Ocleppo states: “Students need more practical learning and skills that allow for a faster and more profitable entry into a company.”
Milan, 19 June 2023 – Italian students aged between 18 and 26 prefer educational and training offerings based on the hybrid models and a focus on up-to-date training provided by quality teaching staff. They’re also less likely to believe that the name of a university is enough to guarantee job opportunities upon graduating. These are some of the chief findings to emerge from an OPIT survey of 1,600 students (secondary level and university) who are part of the Docsity community – a platform for sharing documents and interesting content – just a few days before the beginning of final exams.
The results show that students consider job opportunities and connections with companies as the main factors when evaluating study opportunities (68%). Cost is also an important criterion (39.6%), as is the updating of teaching methods and practical aspects of the course to ensure they’re aligned with today’s work environment (33.1%). Furthermore, 21.7% of those surveyed note the quality of the teaching staff as being crucial to helping them absorb the skills they need to succeed as workers in the future. The “name” and reputation of a university of training provider only matters to 13% of those surveyed.
“The data confirms what we had foreseen when we decided to enter the education market,” says OPIT’s founder and director Riccardo Ocleppo. “Involving companies in our programs was a top priority, and their insights were instrumental in designing the modules we created, including what technologies to rely on and the programming languages we work with, for example.”
“By working with companies to design our programs, we’ve found that students both require and prefer a much more hands-on learning experience. This ensures they’re up to date on current technologies, processes, and ways of working when they join a company. So, our goal for our students is that they leave OPIT feeling much more knowledgeable about what employers really need from them.”
As far as learning methods are concerned, students prefer the hybrid model – having the opportunity to participate in face-to-face lessons while retaining the flexibility to access course content online or even via a fully remote model based on their needs. Amongst university students, 72.6% say they prefer the hybrid model, unlike secondary students, who retain a preference for my “physical” styles of teaching.
When secondary students were asked about their choice of university, 46% of boys and girls indicated engineering, computer science, and STEM as their preferred fields. Humanities and communication followed (20.6%), with economics taking the third spot (17.9%).
“Rapid developments in technology and artificial intelligence,” continues Ocleppo, “are creating new job opportunities for STEM graduates, which current students clearly understand. Specific skills are becoming increasingly important as enterprises move more and more to make the most out of the changes brought by AI. Yet, the shortage of tech workers is expected to grow even faster in the coming years. Despite the concern that the wave of AI-inspired technologies is creating, there is no doubt there will be demand for certain types of professionals with specific technical skills.”
OPIT’s data also indicates a widespread trend toward the continuation of studies beyond initial certification, belying the more pessimistic readings on the growth of the NEET (Not in Education, Employment, or Training) phenomenon. Enrolling in a degree course remains both the safest and preferred choice for the majority of secondary school students – 82% confirmed their intention to continue their studies at the university level. A further 8.3% are undecided about university, while 5% will choose short training courses, with only 2.5% of students surveyed saying they’ll stop education after their fifth-grade exams. Accredited training (university, business school, or some other form of higher education) remains the preferred choice of almost all students (94.6%).
Delving deeper into a behavioral analysis of university students, an interesting preference for further continuation of studies emerges. Over two-thirds (68%) say they wish to continue, demonstrating that a Bachelor’s degree alone is not seen as the ideal pathway into the world of work. In fact, of those who declared a willingness to continue studying after submitting their Bachelor’s thesis, 90% said they want to enroll in a new long-term study program – either a second Bachelor’s degree or a Master’s degree. It’s also significant that more university students are undecided about continuing their educations (22%) than those who are convinced they’ll finish studying permanently upon completion of their degrees (10%).
Asked about what will be most important in a future where they will have to grapple with various AI-led transitions, over half of students (56%) believe it’s essential to understand artificial intelligence and its applications. This was followed by digital marketing (42%), with cybersecurity identified by one in three students (35%) as key due to the job opportunities in that field linked to the need to protect growing amounts of personal data. Fintech closed this ranking at 3%.
OPIT – Open Institute of Technology is an academic institution accredited at the European level that provides an exclusively online training offer focused on Computer Science and a teaching staff made up of professors of international standing. OPIT stands out in the panorama of university-level training for a didactic model shaped by the need for quality, flexibility, and connection with the business world of upcoming generations. OPIT’s degree programs are oriented towards the acquisition of modern and up-to-date skills in the crucial sector of computer science. Its degrees are accredited by the MFHEA and the EQF (European Qualification Framework), and professionally recognized by employers.
A Practical Guide to Thriving in Today’s Job Market Powered by AI and Computer Science
Technology transforms the world in so many ways. Ford’s introduction of the assembly line was essential to the vehicle manufacturing process. The introduction of the internet changed how we communicate, do business, and interact with the world. And in machine learning, we have an emerging technology that transforms how we use computers to complete complex tasks.
Think of machine learning models as “brains” that machines use to actively learn. No longer constrained by rules laid out in their programming, machines have the ability to develop an understanding of new concepts and deliver analysis in ways they never could before. And as a prospective machine learning student, you can become the person who creates the “brains” that modern machines use now and in the future.
But you need a good starting point before you can do any of that. This article covers three of the best machine learning tutorials for beginners who want to get their feet wet while building foundational knowledge that serves them in more specialized courses.
Factors to Consider When Choosing a Machine Learning Tutorial
A machine learning beginner can’t expect to jump straight into a course that delves into neural networking and deep learning and have any idea what they’re doing. They need to learn to crawl before they can walk, making the following factors crucial to consider when choosing a machine learning tutorial for beginners.
- Content quality. You wouldn’t use cheap plastic parts to build an airplane, just like you can’t rely on poor-quality course content to get you started with machine learning. Always look for reviews of a tutorial before engaging, in addition to checking the credentials of the provider to ensure they deliver relevant content that aligns with your career goals.
- Instructor expertise. Sticking with our airplane analogy, imagine being taught how to pilot a plane by somebody who’s never actually flown. It simply wouldn’t work. The same goes for a machine learning tutorial, as you need to see evidence that your instructor does more than parrot information that you can find elsewhere. Look for real-world experience and accreditation from recognized authorities.
- Course structure and pacing. As nice as it would be to have an infinite amount of free time to dedicate to learning, that isn’t a reality for anybody. You have work, life, family, and possibly other study commitments to keep on top of, and your machine learning tutorial has to fit around all of it.
- Practical and real-world examples. Theoretical knowledge can only take you so far. You need to know how to apply what you’ve learned, which is why a good tutorial should have practical elements that test your knowledge. Think of it like driving a car. You can read pages upon pages of material on how to drive properly but you won’t be able to get on the road until you’ve spent time learning behind the wheel.
- Community support. Machine learning is a complex subject and it’s natural to feel a little lost with the materials in many tutorials. A strong community gives you a resource base to lean into, in addition to exposing you to peers (and experienced tech-heads) who can help you along or point you in the right career direction.
Top Three Machine Learning Tutorials for Beginners
Now you know what to look for in a machine learning tutorial for beginners, you’re ready to start searching for a course. But if you want to take a shortcut and jump straight into learning, these three courses are superb starting points.
Tutorial 1 – Intro to Machine Learning (Kaggle)
Offered at no cost, Intro to Machine Learning is a three-hour self-paced course that allows you to learn as and when you feel like learning. All of which is helped by Kaggle’s clever save system. You can use it to save your progress and jump back into your learning whenever you’re ready. The course has seven lessons, the first of which offers an introduction to machine learning as a concept. Whereas the other six dig into more complex topics and come with an exercise for you to complete.
Those little exercises are the tutorial’s biggest plus point. They force you to apply what you’ve learned before you can move on to the next lesson. The course also has a dedicated community (led by tutorial creator Dan Becker) that can help you if you get stuck. You even get a certificate for completing the tutorial, though this certificate isn’t as prestigious as one that comes from an organization like Google or IBM.
On the downside, the course isn’t a complete beginner’s course. You’ll need a solid understanding of Python before you get started. Those new to coding should look for Python courses first or they’ll feel lost when the tutorial starts throwing out terminology and programming libraries that they need to use.
Ideal for students with experience in Python who want to apply the programming language to machine learning models.
Tutorial 2 – What Is Machine Learning? (Udemy)
You can’t build a house without any bricks and you can’t build a machine learning model before you understand the different types of learning that underpin that model. Those different types of learning are what the What is Machine Learning tutorial covers. You’ll get to grips with supervised, unsupervised, and reinforcement learning, which are the three core learning types a machine can use to feed its “brain.”
The course introduces you to real-world problems and helps you to see which type of machine learning is best suited to solving those problems. It’s delivered via online videos, totaling just under two hours of teaching, and includes demonstrations in Python to show you how each type of learning is applied to real-world models. All the resources used for the tutorial are available on a GitHub page (which also gives you access to a strong online community) and the tutorial is delivered by an instructor with over 27 years of experience in the field.
It’s not the perfect course, by any means, as it focuses primarily on learning types without digging much deeper. Those looking for a more in-depth understanding of the algorithms used in machine learning won’t find it here, though they will build foundational knowledge that helps them to better understand those algorithms once they encounter them. As an Udemy course, it’s free to take but requires a subscription to the service if you want a certificate and the ability to communicate directly with the course provider.
Ideal for students who want to learn about the different types of machine learning and how to use Python to apply them.
Tutorial 3 – Machine Learning Tutorial (Geeksforgeeks)
As the most in-depth machine learning tutorial for beginners, the Geeksforgeeks offering covers almost all of the theory you could ever hope to learn. It runs the gamut from a basic introduction to machine learning through to advanced concepts, such as natural language processing and neural networks. And it’s all presented via a single web page that acts like a hub that links you to many other pages, allowing you to tailor your learning experience based on what aligns best with your goals.
The sheer volume of content on offer is the tutorial’s biggest advantage, with dedicated learners able to take themselves from complete machine learning newbies to accomplished experts if they complete everything. There’s also a handy discussion board that puts you in touch with others taking the course. Plus, the “Practice” section of the tutorial includes real-world problems, including a “Problem of the Day” that you can use to test different skills.
However, some students may find the way the material is presented to be a little disorganized and it’s easy to lose track of where you are among the sea of materials. The lack of testing (barring the two or three projects in the “Practice” section) may also rankle with those who want to be able to track their progress easily.
Ideal for self-paced learners who want to be able to pick and choose what they learn and when they learn it.
Additional Resources for Learning Machine Learning
Beyond tutorials, there are tons of additional resources you can use to supplement your learning. These resources are essential for continuing your education because machine learning is an evolving concept that changes constantly.
- Books. Machine learning books are great for digging deeper into the theory you learn via a tutorial, though they come with the downside of offering no practical examples or ways to interact with authors.
- YouTube channels. YouTube videos are ideal for visual learners and they tend to offer a free way to build on what you learn in a tutorial. Examples of great channels to check out include Sentdex and DeepLearningAI, with both channels covering emerging trends in the field alongside lectures and tutorials.
- Blogs and websites. Blogs come with the advantage of the communities that sprout up around them, which you can rely on to build connections and further your knowledge. Of course, there’s the information shared in the blogs, too, though you must check the writer’s credentials before digging too deep into their content.
Master a Machine Learning Tutorial for Beginners Before Moving On
A machine learning tutorial for beginners can give you a solid base in the fundamentals of an extremely complex subject. With that base established, you can build up by taking other courses and tutorials that focus on more specialized aspects of machine learning. Without the base, you’ll find the learning experience much harder. Think of it like building a house – you can’t lay any bricks until you have a foundation in place.
The three tutorials highlighted here give you the base you need (and more besides), but it’s continued study that’s the key to success for machine learning students. Once you’ve completed a tutorial, look for books, blogs, YouTube channels, and other courses that help you keep your knowledge up-to-date and relevant in an ever-evolving subject.