Recommender systems are AI-based algorithms that use different information to recommend products to customers. We can say that recommender systems are a subtype of machine learning because the algorithms “learn from their past,” i.e., use past data to predict the future.
Today, we’re exposed to vast amounts of information. The internet is overflowing with data on virtually any topic. Recommender systems are like filters that analyze the data and offer the users (you) only relevant information. Since what’s relevant to you may not interest someone else, these systems use unique criteria to provide the best results to everyone.
In this article, we’ll dig deep into recommender systems and discuss their types, applications, and challenges.
Types of Recommender Systems
Learning more about the types of recommender systems will help you understand their purpose.
Content-Based Filtering
With content-based filtering, it’s all about the features of a particular item. Algorithms pick up on specific characteristics to recommend a similar item to the user (you). Of course, the starting point is your previous actions and/or feedback.
Sounds too abstract, doesn’t it? Let’s explain it through a real-life example: movies. Suppose you’ve subscribed to a streaming platform and watched The Notebook (a romance/drama starring Ryan Gosling and Rachel McAdams). Algorithms will sniff around to investigate this movie’s properties:
- Genre
- Actors
- Reviews
- Title
Then, algorithms will suggest what to watch next and display movies with similar features. For example, you may find A Walk to Remember on your list (because it belongs to the same genre and is based on a book by the same author). But you may also see La La Land on the list (although it’s not the same genre and isn’t based on a book, it stars Ryan Gosling).
Some of the advantages of this type are:
- It only needs data from a specific user, not a whole group.
- It’s ideal for those who have interests that don’t fall into the mainstream category.
A potential drawback is:
- It recommends only similar items, so users can’t really expand their interests.
Collaborative Filtering
In this case, users’ preferences and past behaviors “collaborate” with one another, and algorithms use these similarities to recommend items. We have two types of collaborative filtering: user-user and item-item.
User-User Collaborative Filtering
The main idea behind this type of recommender system is that people with similar interests and past purchases are likely to make similar selections in the future. Unlike the previous type, the focus here isn’t just on only one user but a whole group.
Collaborative filtering is popular in e-commerce, with a famous example being Amazon. It analyzes the customers’ profiles and reviews and offers recommended products using that data.
The main advantages of user-user collaborative filtering are:
- It allows users to explore new interests and stay in the loop with trends.
- It doesn’t need information about the specific characteristics of an item.
The biggest disadvantage is:
- It can be overwhelmed by data volume and offer poor results.
Item-Item Collaborative Filtering
If you were ever wondering how Amazon knows you want a mint green protective case for the phone you just ordered, the answer is item-item collaborative filtering. Amazon invented this type of filtering back in 1998. With it, the e-commerce platform can make quick product suggestions and let users purchase them with ease. Here, the focus isn’t on similarities between users but between products.
Some of the advantages of item-item collaborative filtering are:
- It doesn’t require information about the user.
- It encourages users to purchase more products.
The main drawback is:
- It can suffer from a decrease in performance when there’s a vast amount of data.
Hybrid Recommender Systems
As we’ve seen, both collaborative and content-based filtering have their advantages and drawbacks. Experts designed hybrid recommender systems that grab the best of both worlds. They overcome the problems behind collaborative and content-based filtering and offer better performance.
With hybrid recommender systems, algorithms take into account different factors:
- Users’ preferences
- Users’ past purchases
- Users’ product ratings
- Similarities between items
- Current trends
A classic example of a hybrid recommender system is Netflix. Here, you’ll see the recommended content based on the TV shows and movies you’ve already watched. You can also discover content that users with similar interests enjoy and can see what’s trending at the moment.
The biggest strong points of this system are:
- It offers precise and personalized recommendations.
- It doesn’t have cold-start problems (poor performance due to lack of information).
The main drawback is:
- It’s highly complex.
Machine Learning Techniques in Recommender Systems
It’s fair to say that machine learning is like the foundation stone of recommender systems. This sub-type of artificial intelligence (AI) represents the process of computers generating knowledge from data. We understand the “machine” part, but what does “learning” implicate? “Learning” means that machines improve their performance and enhance capabilities as they learn more information and become more “experienced.”
The four machine learning techniques recommender systems love are:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
Supervised Learning
In this case, algorithms feed off past data to predict the future. To do that, algorithms need to know what they’re looking for in the data and what the target is. The data in which we know the target label are named labeled datasets, and they teach algorithms how to classify data or make predictions.
Supervised learning has found its place in recommender systems because it helps understand patterns and offers valuable recommendations to users. It analyzes the users’ past behavior to predict their future. Plus, supervised learning can handle large amounts of data.
The most obvious drawback of supervised learning is that it requires human involvement, and training machines to make predictions is no walk in the park. There’s also the issue of result accuracy. Whether or not the results will be accurate largely depends on the input and target values.
Unsupervised Learning
With unsupervised learning, there’s no need to “train” machines on what to look for in datasets. Instead, the machines analyze the information to discover hidden patterns or similar features. In other words, you can sit back and relax while the algorithms do their magic. There’s no need to worry about inputs and target values, and that is one of the best things about unsupervised learning.
How does this machine learning technique fit into recommender systems? The main application is exploration. With unsupervised learning, you can discover trends and patterns you didn’t even know existed. It can discover surprising similarities and differences between users and their online behavior. Simply put, unsupervised learning can perfect your recommendation strategies and make them more precise and personal.
Reinforcement Learning
Reinforcement learning is another technique used in recommender systems. It functions like a reward-punishment system, where the machine has a goal that it needs to achieve through a series of steps. The machine will try a strategy, receive back, change the strategy as necessary, and try again until it reaches the goal and gets a reward.
The most basic example of reinforcement learning in recommender systems is movie recommendations. In this case, the “reward” would be the user giving a five-star rating to the recommended movie.
Deep Learning
Deep learning is one of the most advanced (and most fascinating) subcategories of AI. The main idea behind deep learning is building neural networks that mimic and function similarly to human brains. Machines that feature this technology can learn new information and draw their own conclusions without any human assistance.
Thanks to this, deep learning offers fine-tuned suggestions to users, enhances their satisfaction, and ultimately leads to higher profits for companies that use it.
Challenges and Future Trends in Recommender Systems
Although we may not realize it, recommender systems are the driving force of online purchases and content streaming. Without them, we wouldn’t be able to discover amazing TV shows, movies, songs, and products that make our lives better, simpler, and more enjoyable.
Without a doubt, the internet would look very different if it wasn’t for recommender systems. But as you may have noticed, what you see as recommended isn’t always what you want, need, or like. In fact, the recommendations can be so wrong that you may be shocked how the internet could misinterpret you like that. Recommender systems aren’t perfect (at least not yet), and they face different challenges that affect their performance:
- Data sparsity and scalability – If users don’t leave a trace online (don’t review items), the machines don’t have enough data to analyze and make recommendations. Likewise, the datasets change and grow constantly, which can also represent an issue.
- Cold start problem – When new users become a part of a system, they may not receive relevant recommendations because algorithms don’t “know” their preferences, past purchases, or ratings. The same goes for new items introduced to a system.
- Privacy and security concerns – Privacy and security are always at the spotlight of recommender systems. The situation is a paradox. The more a system knows about you, the better recommendations you’ll get. At the same time, you may not be willing to let a system learn your personal information if you want to maintain your privacy. But then, you won’t enjoy great recommendations.
- Incorporating contextual information – Besides “typical” information, other data can help make more precise and relevant recommendations. The problem is how to incorporate them.
- Explainability and trust – Can a recommender system explain why it made a certain recommendation, and can you trust it?
Discover New Worlds with Recommender Systems
Recommender systems are growing smarter by the day, thanks to machine learning and technological advancements. The recommendations were introduced to allow us to save time and find exactly what we’re looking for in a jiff. At the same time, they let us experiment and try something different.
While recommender systems have come a long way, there’s still more than enough room for further development.
-
From the same author
Read the articleRecommender systems are AI-based algorithms that use different information to recommend products to customers. We can say that recommender systems are a subtype of machine learning because the algorithms “learn from their past,” i.e., use past data to predict the future.
Today, we’re exposed to vast amounts of information. The internet is overflowing with data on virtually any topic. Recommender systems are like filters that analyze the data and offer the users (you) only relevant information. Since what’s relevant to you may not interest someone else, these systems use unique criteria to provide the best results to everyone.
In this article, we’ll dig deep into recommender systems and discuss their types, applications, and challenges.
Types of Recommender Systems
Learning more about the types of recommender systems will help you understand their purpose.
Content-Based Filtering
With content-based filtering, it’s all about the features of a particular item. Algorithms pick up on specific characteristics to recommend a similar item to the user (you). Of course, the starting point is your previous actions and/or feedback.
Sounds too abstract, doesn’t it? Let’s explain it through a real-life example: movies. Suppose you’ve subscribed to a streaming platform and watched The Notebook (a romance/drama starring Ryan Gosling and Rachel McAdams). Algorithms will sniff around to investigate this movie’s properties:
- Genre
- Actors
- Reviews
- Title
Then, algorithms will suggest what to watch next and display movies with similar features. For example, you may find A Walk to Remember on your list (because it belongs to the same genre and is based on a book by the same author). But you may also see La La Land on the list (although it’s not the same genre and isn’t based on a book, it stars Ryan Gosling).
Some of the advantages of this type are:
- It only needs data from a specific user, not a whole group.
- It’s ideal for those who have interests that don’t fall into the mainstream category.
A potential drawback is:
- It recommends only similar items, so users can’t really expand their interests.
Collaborative Filtering
In this case, users’ preferences and past behaviors “collaborate” with one another, and algorithms use these similarities to recommend items. We have two types of collaborative filtering: user-user and item-item.
User-User Collaborative Filtering
The main idea behind this type of recommender system is that people with similar interests and past purchases are likely to make similar selections in the future. Unlike the previous type, the focus here isn’t just on only one user but a whole group.
Collaborative filtering is popular in e-commerce, with a famous example being Amazon. It analyzes the customers’ profiles and reviews and offers recommended products using that data.
The main advantages of user-user collaborative filtering are:
- It allows users to explore new interests and stay in the loop with trends.
- It doesn’t need information about the specific characteristics of an item.
The biggest disadvantage is:
- It can be overwhelmed by data volume and offer poor results.
Item-Item Collaborative Filtering
If you were ever wondering how Amazon knows you want a mint green protective case for the phone you just ordered, the answer is item-item collaborative filtering. Amazon invented this type of filtering back in 1998. With it, the e-commerce platform can make quick product suggestions and let users purchase them with ease. Here, the focus isn’t on similarities between users but between products.
Some of the advantages of item-item collaborative filtering are:
- It doesn’t require information about the user.
- It encourages users to purchase more products.
The main drawback is:
- It can suffer from a decrease in performance when there’s a vast amount of data.
Hybrid Recommender Systems
As we’ve seen, both collaborative and content-based filtering have their advantages and drawbacks. Experts designed hybrid recommender systems that grab the best of both worlds. They overcome the problems behind collaborative and content-based filtering and offer better performance.
With hybrid recommender systems, algorithms take into account different factors:
- Users’ preferences
- Users’ past purchases
- Users’ product ratings
- Similarities between items
- Current trends
A classic example of a hybrid recommender system is Netflix. Here, you’ll see the recommended content based on the TV shows and movies you’ve already watched. You can also discover content that users with similar interests enjoy and can see what’s trending at the moment.
The biggest strong points of this system are:
- It offers precise and personalized recommendations.
- It doesn’t have cold-start problems (poor performance due to lack of information).
The main drawback is:
- It’s highly complex.
Machine Learning Techniques in Recommender Systems
It’s fair to say that machine learning is like the foundation stone of recommender systems. This sub-type of artificial intelligence (AI) represents the process of computers generating knowledge from data. We understand the “machine” part, but what does “learning” implicate? “Learning” means that machines improve their performance and enhance capabilities as they learn more information and become more “experienced.”
The four machine learning techniques recommender systems love are:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
Supervised Learning
In this case, algorithms feed off past data to predict the future. To do that, algorithms need to know what they’re looking for in the data and what the target is. The data in which we know the target label are named labeled datasets, and they teach algorithms how to classify data or make predictions.
Supervised learning has found its place in recommender systems because it helps understand patterns and offers valuable recommendations to users. It analyzes the users’ past behavior to predict their future. Plus, supervised learning can handle large amounts of data.
The most obvious drawback of supervised learning is that it requires human involvement, and training machines to make predictions is no walk in the park. There’s also the issue of result accuracy. Whether or not the results will be accurate largely depends on the input and target values.
Unsupervised Learning
With unsupervised learning, there’s no need to “train” machines on what to look for in datasets. Instead, the machines analyze the information to discover hidden patterns or similar features. In other words, you can sit back and relax while the algorithms do their magic. There’s no need to worry about inputs and target values, and that is one of the best things about unsupervised learning.
How does this machine learning technique fit into recommender systems? The main application is exploration. With unsupervised learning, you can discover trends and patterns you didn’t even know existed. It can discover surprising similarities and differences between users and their online behavior. Simply put, unsupervised learning can perfect your recommendation strategies and make them more precise and personal.
Reinforcement Learning
Reinforcement learning is another technique used in recommender systems. It functions like a reward-punishment system, where the machine has a goal that it needs to achieve through a series of steps. The machine will try a strategy, receive back, change the strategy as necessary, and try again until it reaches the goal and gets a reward.
The most basic example of reinforcement learning in recommender systems is movie recommendations. In this case, the “reward” would be the user giving a five-star rating to the recommended movie.
Deep Learning
Deep learning is one of the most advanced (and most fascinating) subcategories of AI. The main idea behind deep learning is building neural networks that mimic and function similarly to human brains. Machines that feature this technology can learn new information and draw their own conclusions without any human assistance.
Thanks to this, deep learning offers fine-tuned suggestions to users, enhances their satisfaction, and ultimately leads to higher profits for companies that use it.
Challenges and Future Trends in Recommender Systems
Although we may not realize it, recommender systems are the driving force of online purchases and content streaming. Without them, we wouldn’t be able to discover amazing TV shows, movies, songs, and products that make our lives better, simpler, and more enjoyable.
Without a doubt, the internet would look very different if it wasn’t for recommender systems. But as you may have noticed, what you see as recommended isn’t always what you want, need, or like. In fact, the recommendations can be so wrong that you may be shocked how the internet could misinterpret you like that. Recommender systems aren’t perfect (at least not yet), and they face different challenges that affect their performance:
- Data sparsity and scalability – If users don’t leave a trace online (don’t review items), the machines don’t have enough data to analyze and make recommendations. Likewise, the datasets change and grow constantly, which can also represent an issue.
- Cold start problem – When new users become a part of a system, they may not receive relevant recommendations because algorithms don’t “know” their preferences, past purchases, or ratings. The same goes for new items introduced to a system.
- Privacy and security concerns – Privacy and security are always at the spotlight of recommender systems. The situation is a paradox. The more a system knows about you, the better recommendations you’ll get. At the same time, you may not be willing to let a system learn your personal information if you want to maintain your privacy. But then, you won’t enjoy great recommendations.
- Incorporating contextual information – Besides “typical” information, other data can help make more precise and relevant recommendations. The problem is how to incorporate them.
- Explainability and trust – Can a recommender system explain why it made a certain recommendation, and can you trust it?
Discover New Worlds with Recommender Systems
Recommender systems are growing smarter by the day, thanks to machine learning and technological advancements. The recommendations were introduced to allow us to save time and find exactly what we’re looking for in a jiff. At the same time, they let us experiment and try something different.
While recommender systems have come a long way, there’s still more than enough room for further development.
-
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Computer Science is fast becoming one of the most valuable fields of study, with high levels of demand and high-salaried career opportunities for successful graduates. If you’re looking for a flexible and rewarding way to hone your computing skills as part of a supportive global community, the BSc in Computer Science at the Open Institute of Technology (OPIT) could be the perfect next step.
Introducing the OPIT BSc in Computer Science
The OPIT BSc in Computer Science is a bachelor’s degree program that provides students with a comprehensive level of both theoretical and practical knowledge of all core areas of computer science. That includes the likes of programming, databases, cloud computing, software development, and artificial intelligence.
Like other programs at OPIT, the Computer Science BSc is delivered exclusively online, with a mixture of recorded and live content for students to engage with. Participants will enjoy the instruction of world-leading lecturers and professors from various fields, including software engineers at major tech brands and esteemed researchers, and will have many paths open to them upon graduation.
Graduates may, for example, seek to push on with their educational journeys, progressing on to a specialized master’s degree at OPIT, like the MSc in Digital Business and Innovation or the MSc in Responsible Artificial Intelligence. Or they could enter the working world in roles like software engineer, data scientist, web developer, app developer, or cybersecurity consultant.
The bullets below outline the key characteristics of this particular course:
- Duration: Three years in total, spread across six terms.
- Content: Core courses for the first four terms, a student-selected specialization for the fifth term, and a capstone project in the final term.
- Focus: Developing detailed theoretical knowledge and practical skills across all core areas of modern computer science.
- Format: Entirely online, with a mixture of live lessons and asynchronous content you can access 24/7 to learn at your own pace.
- Assessment: Progressive assessments over the course of the program, along with a capstone project and dissertation, but no final exams.
What You’ll Learn
Students enrolled in the BSc in Computer Science course at OPIT will enjoy comprehensive instruction in the increasingly diverse sectors that fall under the umbrella of computer science today. That includes a close look at emerging technologies, like AI and machine learning, as well as introductions to the fundamental skills involved in designing and developing pieces of software.
The first four terms are the same for all students. These will include introductions to software engineering, computer security, and cloud computing infrastructure, as well as courses focusing on the core skills that computer scientists invariably need in their careers, like project management, quality assurance, and technical English.
For the fifth term, students will have a choice. They can select five electives from a pool of 27, or select one field to specialize in from a group of five. You may choose to specialize in all things cybersecurity, for example, and learn about emerging cyber threats. Or you could focus more on specific elements of computer science that appeal to your interests and passions, such as game development.
Who It’s For
The BSc in Computer Science program can suit a whole range of prospective applicants and should appeal to anyone with an interest or passion for computing and a desire to pursue a professional career in this field. Whether you’re seeking to enter the world of software development, user experience design, data science, or another related sector, this is the course to consider.
In addition, thanks to OPIT’s engaging, flexible, and exclusively online teaching and learning systems, this course can appeal to people from all over the globe, of different ages, and from different walks of life. It’s equally suitable for recent high school graduates with dreams of making their own apps to seasoned professionals looking to broaden their knowledge or transition to a different career.
The Value of the BSc in Computer Science Course at OPIT
Plenty of universities and higher education establishments around the world offer degrees in computer science, but OPIT’s program stands out for several distinctive reasons.
Firstly, as previously touched upon, all OPIT courses are delivered online. Students have a schedule of live lessons to attend, but can also access recorded content and digital learning resources as and when they choose. This offers an unparalleled level of freedom and flexibility compared to more conventional educational institutions, putting students in the driving seat and letting them learn at their own pace.
OPIT also aims not merely to impart knowledge through lectures and teaching, but to actually help students gain the practical skills they need to take the next logical steps in their education or career. In other words, studying at OPIT isn’t simply about memorizing facts and paragraphs of text; it’s about learning how to apply the knowledge you gain in real-world settings.
OPIT students also enjoy the unique benefits of a global community of like-minded students and world-leading professors. Here, distance is no barrier, and while students and teachers may come from completely different corners of the globe, all are made to feel welcome and heard. Students can reach out to their lecturers when they feel the need for guidance, answers, and advice.
Other benefits of studying with OPIT include:
- Networking opportunities and events, like career fairs, where you can meet and speak with representatives from some of the world’s biggest tech brands
- Consistent support systems from start to finish of your educational journey in the form of mentorships and more
- Helpful tools to expedite your education, like the OPIT AI Copilot, which provides personalized study support
Entry Requirements and Fees
To enroll in the OPIT BSc in Computer Science and take your next steps towards a thrilling and fulfilling career in this field, you’ll need to meet some simple criteria. Unlike other educational institutions, which can impose strict and seemingly unattainable requirements on their applicants, OPIT aims to make tech education more accessible. As a result, aspiring students will require:
- A higher secondary school leaving certificate at EQF Level 4, or equivalent
- B2-level English proficiency, or higher
Naturally, applicants should also have a passion for computer science and a willingness to study, learn, and make the most of the resources, community, and support systems provided by the institute.
In addition, if you happen to have relevant work experience or educational achievements, you may be able to use these to skip certain modules or even entire terms and obtain your degree sooner. OPIT offers a comprehensive credit transfer program, which you can learn more about during the application process.
Regarding fees, OPIT also stands out from the crowd compared to conventional educational institutions, offering affordable rates to make higher tech education more accessible. There are early bird discounts, scholarship opportunities, and even the option to pay either on a term-by-term basis or a one-off up-front fee.
The Open Institute of Technology (OPIT) provides a curated collection of courses for students at every stage of their learning journey, including those who are just starting. For aspiring tech leaders and those who don’t quite feel ready to dive directly into a bachelor’s degree, there’s the OPIT Foundation Program. It’s the perfect starting point to gain core skills, boost confidence, and build a solid base for success.
Introducing the OPIT Foundation Year Program
As the name implies, OPIT’s Foundation Program is about foundation-level knowledge and skills. It’s the only pre-bachelor program in the OPIT lineup, and successful students on this 60-ECTS credit course will obtain a Pre-Tertiary Certificate in Information Technology upon its completion. From there, they can move on to higher levels of learning, like a Bachelor’s in Digital Business or Modern Computer Science.
In other words, the Foundation Program provides a gentle welcome into the world of higher technological education, while also serving as a springboard to help students achieve their long-term goals. By mixing both guided learning and independent study, it also prepares students for the EQF Level 4 experiences and challenges they’ll face once they enroll in a bachelor’s program in IT or a related field.
Here’s a quick breakdown of what the OPIT Foundation Program course involves:
- Duration: Six months, split into two terms, with each term lasting 13 weeks
- Content: Three courses per term, with each one worth 10 ECTS credits, for a total of 60
- Focus: Core skills, like mathematics, English, and introductory-level computing
- Format: Video lectures, independent learning, live sessions, and digital resources (e-books, etc.)
- Assessment: Two to three assessments over the course of the program
What You’ll Learn
The OPIT Foundation Program doesn’t intensely focus on any one particular topic, nor does it thrust onto you the more advanced, complicated aspects of technological education you would find in a bachelor’s or master’s program. Instead, it largely keeps things simple, focusing on the basic building blocks of knowledge and core skills so that students feel comfortable taking the next steps in their studies.
It includes the following courses, spread out across two terms:
- Academic Skills
- Mathematics Literacy I
- Mathematics Literacy II
- Internet and Digital Technology
- Academic Reading, Writing, and Communication
- Introduction to Computer Hardware and Software
Encompassing foundational-level lessons in digital business, computer science, and computer literacy, the Foundation Program produces graduates with a commanding knowledge of common operating systems. Exploring reading and writing, it also helps students master the art of communicating their ideas and responses in clear, academic English.
Who It’s For
The Foundation Year program is for people who are eager to enter the world of technology and eventually pursue a bachelor’s or higher level of education in this field, but feel they need more preparation. It’s for the people who want to work on their core skills and knowledge before progressing to more advanced topics, so that they don’t feel lost or left behind later on.
It can appeal to anyone with a high school-level education and ambitions of pushing themselves further, and to anyone who wants to work in fields like computer science, digital business, and artificial intelligence (AI). You don’t need extensive experience or qualifications to get started (more on that below); just a passion for tech and the motivation to learn.
The Value of the Foundation Program
With technology playing an increasingly integral role in the world today, millions of students want to develop their tech knowledge and skills. The problem is that technology-oriented degree courses can sometimes feel a little too complex or even inaccessible, especially for those who may not have had the most conventional educational journeys in the past.
While so many colleges and universities around the world simply expect students to show up with the relevant skills and knowledge to dive right into degree programs, OPIT understands that some students need a helping hand. That’s where the Foundation Program comes in – it’s the kind of course you won’t find at a typical university, aimed at bridging the gap between high school and higher education.
By progressing through the Foundation Program, students gain not just knowledge, but confidence. The entire course is aimed at eliminating uncertainty and unease. It imbues students with the skills and understanding they need to push onward, to believe in themselves, and to get more value from wherever their education takes them next.
On its own, this course won’t necessarily provide the qualifications you need to move straight into the job market, but it’s a vital stepping stone towards a degree. It also provides numerous other advantages that are unique to the OPIT community:
- Online Learning: Enjoy the benefits of being able to learn at your own pace, from the comfort of home, without the costs and inconveniences associated with relocation, commuting, and so on.
- Strong Support System: OPIT professors regularly check in with students and are on hand around the clock to answer queries and provide guidance.
- Academic Leaders: The OPIT faculty is made up of some of the world’s sharpest minds, including tech company heads, experienced researchers, and even former education ministers.
Entry Requirements and Fees
Unlike OPIT’s other, more advanced courses, the Foundation Program is aimed at beginners, so it does not have particularly strict or complex entry requirements. It’s designed to be as accessible as possible, so that almost anyone can acquire the skills they need to pursue education and a career in technology. The main thing you’ll need is a desire to learn and improve your skills, but applicants should also possess:
- English proficiency at level B2 or higher
- A Secondary School Leaving Certificate, or equivalent
Regarding the fees, OPIT strives to lower the financial barrier of education that can be such a deterrent in conventional education around the world. The institute’s tuition fees are fairly and competitively priced, all-inclusive (without any hidden charges to worry about), and accessible for those working with different budgets.
Given that all resources and instruction are provided online, you can also save a lot of money on relocation and living costs when you study with OPIT. In addition, applicants have the option to pay either up front, with a 10% discount on the total, or on a per-term basis, allowing you to stretch the cost out over a longer period to ease the financial burden.
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