Machine learning, data science, and artificial intelligence are common terms in modern technology. These terms are often used interchangeably but incorrectly, which is understandable.
After all, hundreds of millions of people use the advantages of digital technologies. Yet only a small percentage of those users are experts in the field.
AI, data science, and machine learning represent valuable assets that can be used to great advantage in various industries. However, to use these tools properly, you need to understand what they are. Furthermore, knowing the difference between data science and machine learning, as well as how AI differs from both, can dispel the common misconceptions about these technologies.
Read on to gain a better understanding of the three crucial tech concepts.
Data Science
Data science can be viewed as the foundation of many modern technological solutions. It’s also the stage from which existing solutions can progress and evolve. Let’s define data science in more detail.
Definition and Explanation of Data Science
A scientific discipline with practical applications, data science represents a field of study dedicated to the development of data systems. If this definition sounds too broad, that’s because data science is a broad field by its nature.
Data structure is the primary concern of data science. To produce clean data and conduct analysis, scientists use a range of methods and tools, from manual to automated solutions.
Data science has another crucial task: defining problems that previously didn’t exist or slipped by unnoticed. Through this activity, data scientists can help predict unforeseen issues, improve existing digital tools, and promote the development of new ones.
Key Components of Data Science
Breaking down data science into key components, we get to three essential factors:
- Data collection
- Data analysis
- Predictive modeling
Data collection is pretty much what it sounds like – gathering of data. This aspect of data science also includes preprocessing, which is essentially preparation of raw data for further processing.
During data analysis, data scientists draw conclusions based on the gathered data. They search the data for patterns and potential flaws. The scientists do this to determine weak points and system deficiencies. In data visualization, scientists aim to communicate the conclusions of their investigation through graphics, charts, bullet points, and maps.
Finally, predictive modeling represents one of the ultimate uses of the analyzed data. Here, create models that can help them predict future trends. This component also illustrates the differentiation between data science vs. machine learning. Machine learning is often used in predictive modeling as a tool within the broader field of data science.
Applications and Use Cases of Data Science
Data science finds uses in marketing, banking, finance, logistics, HR, and trading, to name a few. Financial institutions and businesses take advantage of data science to assess and manage risks. The powerful assistance of data science often helps these organizations gain the upper hand in the market.
In marketing, data science can provide valuable information about customers, help marketing departments organize, and launch effective targeted campaigns. When it comes to human resources, extensive data gathering, and analysis allow HR departments to single out the best available talent and create accurate employee performance projections.
Artificial Intelligence (AI)
The term “artificial intelligence” has been somewhat warped by popular culture. Despite the varying interpretations, AI is a concrete technology with a clear definition and purpose, as well as numerous applications.
Definition and Explanation of AI
Artificial intelligence is sometimes called machine intelligence. In its essence, AI represents a machine simulation of human learning and decision-making processes.
AI gives machines the function of empirical learning, i.e., using experiences and observations to gain new knowledge. However, machines can’t acquire new experiences independently. They need to be fed relevant data for the AI process to work.
Furthermore, AI must be able to self-correct so that it can act as an active participant in improving its abilities.
Obviously, AI represents a rather complex technology. We’ll explain its key components in the following section.
Key Components of AI
A branch of computer science, AI includes several components that are either subsets of one another or work in tandem. These are machine learning, deep learning, natural language processing (NLP), computer vision, and robotics.
It’s no coincidence that machine learning popped up at the top spot here. It’s a crucial aspect of AI that does precisely what the name says: enables machines to learn.
We’ll discuss machine learning in a separate section.
Deep learning relates to machine learning. Its aim is essentially to simulate the human brain. To that end, the technology utilizes neural networks alongside complex algorithm structures that allow the machine to make independent decisions.
Natural language processing (NLP) allows machines to comprehend language similarly to humans. Language processing and understanding are the primary tasks of this AI branch.
Somewhat similar to NLP, computer vision allows machines to process visual input and extract useful data from it. And just as NLP enables a computer to understand language, computer vision facilitates a meaningful interpretation of visual information.
Finally, robotics are AI-controlled machines that can replace humans in dangerous or extremely complex tasks. As a branch of AI, robotics differs from robotic engineering, which focuses on the mechanical aspects of building machines.
Applications and Use Cases of AI
The variety of AI components makes the technology suitable for a wide range of applications. Machine and deep learning are extremely useful in data gathering. NLP has seen a massive uptick in popularity lately, especially with tools like ChatGPT and similar chatbots. And robotics has been around for decades, finding use in various industries and services, in addition to military and space applications.
Machine Learning
Machine learning is an AI branch that’s frequently used in data science. Defining what this aspect of AI does will largely clarify its relationship to data science and artificial intelligence.
Definition and Explanation of Machine Learning
Machine learning utilizes advanced algorithms to detect data patterns and interpret their meaning. The most important facets of machine learning include handling various data types, scalability, and high-level automation.
Like AI in general, machine learning also has a level of complexity to it, consisting of several key components.
Key Components of Machine Learning
The main aspects of machine learning are supervised, unsupervised, and reinforcement learning.
Supervised learning trains algorithms for data classification using labeled datasets. Simply put, the data is first labeled and then fed into the machine.
Unsupervised learning relies on algorithms that can make sense of unlabeled datasets. In other words, external intervention isn’t necessary here – the machine can analyze data patterns on its own.
Finally, reinforcement learning is the level of machine learning where the AI can learn to respond to input in an optimal way. The machine learns correct behavior through observation and environmental interactions without human assistance.
Applications and Use Cases of Machine Learning
As mentioned, machine learning is particularly useful in data science. The technology makes processing large volumes of data much easier while producing more accurate results. Supervised and particularly unsupervised learning are especially helpful here.
Reinforcement learning is most efficient in uncertain or unpredictable environments. It finds use in robotics, autonomous driving, and all situations where it’s impossible to pre-program machines with sufficient accuracy.
Perhaps most famously, reinforcement learning is behind AlphaGo, an AI program developed for the Go board game. The game is notorious for its complexity, having about 250 possible moves on each of 150 turns, which is how long a typical game lasts.
Alpha Go managed to defeat the human Go champion by getting better at the game through numerous previous matches.
Key Differences Between Data Science, AI, and Machine Learning
The differences between machine learning, data science, and artificial intelligence are evident in the scope, objectives, techniques, required skill sets, and application.
As a subset of AI and a frequent tool in data science, machine learning has a more closely defined scope. It’s structured differently to data science and artificial intelligence, both massive fields of study with far-reaching objectives.
The objectives of data science are pto gather and analyze data. Machine learning and AI can take that data and utilize it for problem-solving, decision-making, and to simulate the most complex traits of the human brain.
Machine learning has the ultimate goal of achieving high accuracy in pattern comprehension. On the other hand, the main task of AI in general is to ensure success, particularly in emulating specific facets of human behavior.
All three require specific skill sets. In the case of data science vs. machine learning, the sets don’t match. The former requires knowledge of SQL, ETL, and domains, while the latter calls for Python, math, and data-wrangling expertise.
Naturally, machine learning will have overlapping skill sets with AI, since it’s its subset.
Finally, in the application field, data science produces valuable data-driven insights, AI is largely used in virtual assistants, while machine learning powers search engine algorithms.
How Data Science, AI, and Machine Learning Complement Each Other
Data science helps AI and machine learning by providing accurate, valuable data. Machine learning is critical in processing data and functions as a primary component of AI. And artificial intelligence provides novel solutions on all fronts, allowing for more efficient automation and optimal processes.
Through the interaction of data science, AI, and machine learning, all three branches can develop further, bringing improvement to all related industries.
Understanding the Technology of the Future
Understanding the differences and common uses of data science, AI, and machine learning is essential for professionals in the field. However, it can also be valuable for businesses looking to leverage modern and future technologies.
As all three facets of modern tech develop, it will be important to keep an eye on emerging trends and watch for future developments.
Related posts
2025 has come to a close, with 2026 already underway. There are many exciting events ahead and future milestones to aim for and look forward to. But it’s also the ideal time to look back over the last 12 months, exploring the most notable achievements we’ve made, lessons we’ve learned, and important moments to reflect on as the new year continues for OPIT’s staff, students, and broader community.
1. Student Commitment
Studying isn’t always easy. It involves long days, and even long evenings sometimes, with a seemingly never-ending series of tasks to accomplish and goals to aim for. It can take a lot out of even the most hard-working and dedicated individuals.
Yet, despite the hardships and challenges, OPIT students demonstrated remarkable resilience, continuous curiosity, and indefatigable determination throughout 2025. Looking back on the year, students at all levels of the OPIT community should feel proud and celebrate their accomplishments.
2. Podcast Launch
2025 saw a lot of new arrivals at OPIT, with fresh projects and innovations arriving on the scene. Chief among them was the OPIT EDGE Podcast, an exciting addition to the institute’s ever-expanding multimedia offerings.
There have already been several episodes of the podcast for students and technology enthusiasts in general to enjoy, with the first episode of this student-driven project involving an in-depth discussion with industry expert Matteo Zangani on the potential of quantum AI technology.
3. Success Stories
While many new students have joined the OPIT ranks in 2025 and will also do so in 2026, others have now achieved their educational objectives and are already moving on to the next exciting steps and chapters in their personal and professional lives.
There are so many inspiring success stories from the last 12 months, it’s impossible to list them all. But just one notable example has to be Maria Brilaki, who recently concluded her Master’s in Responsible AI, defending a powerful thesis related to non-invasive glucose monitoring through near-infrared spectroscopy and machine learning.
4. Graduation in Malta
2025 was a big year of firsts for OPIT, including the institute’s first official graduation ceremony, which took place on March 8 at a grand ceremony in Malta, honoring the achievements of dozens of applied data science and AI graduates.
The hybrid event was open to both in-person and virtual attendees, bringing together members of the OPIT community from across the world. It was a huge moment for the graduates themselves and a thrilling milestone for OPI – a testament to all the hard work that has gone into building this institute.
5. OPIT AI Copilot
Artificial intelligence is the technology of the moment, and OPIT isn’t just dedicated to teaching the next-generation of technology leaders how to work with AI responsibly and efficiently; it’s also interested in harnessing the powers and potential of AI to improve its educational offerings, too.
This culminated in the development and release of OPIT AI Copilot in 2025. This groundbreaking AI tool now provides real-time, personalized learning support, along with contextual assistance, and is available on a round-the-clock basis for students to turn to, as and when they feel the need.
6. Hackathons
2025 also saw OPIT students and faculty take more active roles in various events, including hackathons. In November, for example, OPIT got involved with the 6th edition of the ESCP Hackathon, with several students entering as developers.
This was an exciting and unique opportunity for those students to meet up in person, put the skills they’ve honed during their time at OPIT to the test in a challenging environment, and learn from one another. OPIT will surely participate in more hackathons in the years to come, so stay tuned for more details on upcoming events and how you can play your part.
7. Strengthening Collaboration
From day one, OPIT has focused on building a strong network of established technology and business partners, opening doors and providing opportunities for both education and employment for its students.
This continued throughout 2025, with OPIT strengthening its connections with a number of world-leading organizations, including Accenture, AWS, Hype, Buffetti, and more. Through events like hackathons, career fairs, and more, OPIT makes the most of its ever-expanding and increasingly impressive professional network.
8. Online Career Fair
Another big first for 2025 was the inaugural OPIT Online Career Fair, an event that was held on November 19 and 20, with more than a dozen established and emerging companies from around the world in attendance, including the likes of Deloitte, Tinexta Cyber, Datapizza, RWS Group, Planet Farms, and Nesperia Group.
The only nature of this event ensured that students all enjoyed equal access, no matter where they were based, and everyone was able to hear from industry experts and enjoy the unique array of opportunities on offer, forging their own connections and learning more about brands they might like to work with or for in the future.
9. Education Innovation
OPIT has always been about innovating, delivering newer and smarter ways to learn for students across the globe, no matter their background, budget, or social class. And the institute has continually innovated over the course of 2025, helping students learn skills and broaden their knowledge efficiently and intuitively.
As we enter 2026, OPIT’s innovation is set to be on full display once more, with no less than two new courses for new applicants to choose from: AI-Driven Software Development (Elective) and Business Intelligence and Decision Making (Elective).
10. The Power of the OPIT Community
Perhaps the crowning achievement for OPIT in 2025 was the demonstrable success of not just individual students or faculty members, but the entire OPIT community, as a whole. Everyone, from alumni to new students and seasoned staff members, played their part in the institute’s success, paving the way for more great things and major milestones in 2026 and beyond.
As OPIT Rector and former Italian Minister of Education, Francesco Profumo, puts it:
“What inspires me most is the mindset of our students: forward-looking, responsible, and driven by a desire not just to succeed, but to contribute. Their dedication reminds us why education remains one of the most powerful forces for shaping the future.”
Bring talented tech experts together, set them a challenge, and give them a deadline. Then, let them loose and watch the magic happen. That, in a nutshell, is what hackathons are all about. They’re proven to be among the most productive tech events when it comes to solving problems and accelerating innovation.
What Is a Hackathon?
Put simply, a hackathon is a short-term event – often lasting just a couple of days, or sometimes even only a matter of hours – where tech experts come together to solve a specific problem or come up with ideas based on a central theme or topic. As an example, teams might be tasked with discovering a new way to use AI in marketing or to create an app aimed at improving student life.
The term combines the words “hack” and “marathon,” due to how participants (hackers or programmers) are encouraged to work around-the-clock to create a prototype, proof-of-concept, or new solution. It’s similar to how marathon runners are encouraged to keep running, putting their skills and endurance to the test in a race to the finish line.
The Benefits of Hackathons
Hackathons provide value both for the companies that organize them and the people who take part. Companies can use them to quickly discover new ideas or overcome challenges, for example, while participants can enjoy testing their skills, innovating, networking, and working either alone or as part of a larger team.
Benefits for Companies and Sponsors
Many of the world’s biggest brands have come to rely on hackathons as ways to drive innovation and uncover new products, services, and opportunities. Meta, for example, the brand behind Facebook, has organized dozens of hackathons, some of which have led to the development of well-known Facebook features, like the “Like” button. Here’s how hackathons help companies:
- Accelerate Innovation: In fast-moving fields like technology, companies can’t always afford to spend months or years working on new products or features. They need to be able to solve problems quickly, and hackathons create the necessary conditions to deliver rapid success.
- Employee Development: Leading companies like Meta have started to use annual hackathons as a way to not only test their workforce’s skills but to give employees opportunities to push themselves and broaden their skill sets.
- Internal Networking: Hackathons also double up as networking events. They give employees from different teams, departments, or branches the chance to work with and learn from one another. This, in turn, can promote or reinforce team-oriented work cultures.
- Talent Spotting: Talents sometimes go unnoticed, but hackathons give your workforce’s hidden gems a chance to shine. They’re terrific opportunities to see who your best problem solvers and most creative thinkers at.
- Improving Reputation: Organizing regular hackathons helps set companies apart from their competitors, demonstrating their commitment to innovation and their willingness to embrace new ideas. If you want your brand to seem more forward-thinking and innovative, embracing hackathons is a great way to go about it.
Benefits for Participants
The hackers, developers, students, engineers, and other people who take part in hackathons arguably enjoy even bigger and better benefits than the businesses behind them. These events are often invaluable when it comes to upskilling, networking, and growing, both personally and professionally. Here are some of the main benefits for participants, explained:
- Learning and Improvement: Hackathons are golden opportunities for participants to gain knowledge and skills. They essentially force people to work together, sharing ideas, contributing to the collective, and pushing their own boundaries in pursuit of a common goal.
- Networking: While some hackathons are purely internal, others bring together different teams or groups of people from different schools, businesses, and places around the world. This can be wonderful for forming connections with like-minded individuals.
- Sense of Pride: Everyone feels a sense of pride after accomplishing a project or achieving a goal, but this often comes at the end of weeks or months of effort. With hackathons, participants can enjoy that same satisfying feeling after just a few hours or a couple of days of hard work.
- Testing Oneself: A hackathon is an amazing chance to put one’s skills to the test and see what one is truly capable of when given a set goal to aim for and a deadline to meet. Many participants are surprised to see how well they respond to these conditions.
- Boosting Skills: Hackathons provide the necessary conditions to hone and improve a range of core soft skills, such as teamwork, communication, problem-solving, organization, and punctuality. By the end, participants often emerge with more confidence in their abilities.
Hackathons at OPIT
The Open Institute of Technology (OPIT) understands the unique value of hackathons and has played its part in sponsoring these kinds of events in the past. OPIT was one of the sponsors behind ESCPHackathon 6, for example, which involved 120 students given AI-related tasks, with mentorship and guidance from senior professionals and developers from established brands along the way.
Marco Fediuc, one of the participants, summed up the mood in his comments:
“The hackathon was a truly rewarding experience. I had the pleasure of meeting OPIT classmates and staff and getting to know them better, the chance to collaborate with brilliant minds, and the opportunity to take part in an exciting and fun event.
“Participating turned out to be very useful because I had the chance to work in a fast-paced, competitive environment, and it taught me what it means to stay calm and perform under pressure… To prospective Computer Science students, should a similar opportunity arise, I can clearly say: Don’t underestimate yourselves!”
The new year will also see the arrival of OPIT Hackathon 2026, giving more students the chance to test their skills, broaden their networks, and enjoy the one-of-a-kind experiences that these events never fail to deliver. This event is scheduled to be held February 13-15, 2026, and is open to all OPIT Bachelor’s and Master’s students, along with recent graduates. Interested parties have until February 1 to register.
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