Imagine that you own a business that has thousands of customers. You have data on every one of these customers, ranging from basic contact information to data about their purchasing habits. What you have is a huge dataset, and you want to extract information from that dataset in the form of patterns and insights with which you can make decisions.

You’d need a data scientist.

Data scientists specialize in shining a spotlight on the most important insights found in large datasets. They use a range of tools – from complex algorithms to artificial intelligence – to make that spotlight shine brighter. And in a world of Big Data, the data scientist’s role is more important now than ever. With these six courses, split between beginner, intermediate, and advanced levels, you put yourself in a prime position to become the data scientist that so many companies need.

Best Data Science Tutorials for Beginners

Everybody has to start somewhere, and these data science beginner tutorial options are the ideal first step on your journey into the field.

Data Science Tutorial for Beginners (Java T Point)

If you’re looking for a succinct explanation of what data science is, what it involves, and how it applies in the modern world, Java T Point’s tutorial answers the key questions. It’s structured as a long-form article rather than a set of modules or lessons, but it’s well-organized and covers all of the key points in enough depth to make it a handy primer for the data science novice.

This data science tutorial covers a range of topics, from basic explanations of the components of data science to descriptions of the types of jobs available for those who enter the field. It also digs into some of the machine learning aspects of data science, such as decision trees, so you can see how AI ties into modern data science practices.

Granted, the fact that it’s not a traditional course means there’s no community underpinning the tutorial or certification for completion. But as a primer that gives you some foundational knowledge, it’s a superb starting point.

Data Science Full Course – Learn Data Science in 10 Hours (Edureka)

Offered via YouTube, this data science tutorial makes the lofty claim of being able to teach you all you need to know about the subject in 10 hours. While that isn’t strictly true (the more complex aspects are covered superficially), it’s still a great primer for those looking to build a solid foundation in the subject.

The tutorial is a great choice for visual learners, and it covers topics like data categorization, statistics, and the data lifecycle. Charts, graphs, and other visual learning tools abound, with the constant narration helping you to understand what you’re seeing on screen.

As a full 10-hour video, the tutorial could do with being broken up into separate lessons to make it easier to keep your place. But as long as you’re happy to record time stamps (or don’t mind the full 10 hours in one sitting), the course delivers plenty of useful information.

Best Data Science Tutorials for Intermediate Learners

After completing a few of the best data science tutorials for beginners, you’re ready to get your feet wet with intermediate courses that dig into the coding that underpins data science.

Data Science with Python Tutorial (Geeksforgeeks)

Python is the programming language of choice for data scientists, as evidenced by the fact that 69% of data scientists report using Python daily. It’s no surprise, either, as Python is an extremely flexible language that’s ideal for creating the algorithms needed in data science due to its vast range of libraries. The challenge you face is twofold – figuring out how to code in Python and understanding what libraries you need to confront common data science challenges.

Geeksforgeeks offers a data science tutorial that confronts both of those challenges and helps you see how Python applies to the data science field in a practical sense. Starting with a brief introduction to the data science field (the beginner-level tutorials in this list offer more depth), it then dives into everything you need to know about Python. You’ll learn about the basics of Python, such as functions and control statements, before moving into how you can use the language for visualizing data and creating machine learning models.

It’s a highly specialized tutorial, though it’s one that’s essential for prospective data scientists, given the popularity of Python in the field. Unfortunately, there’s no certification for completion. However, it’ll equip you with so much Python knowledge that you can feel confident moving into a more advanced study without worrying about your coding chops.

Data Science and Machine Learning Essentials (Microsoft via Udemy)

Like the above course, Microsoft’s offering covers Python, albeit in far less depth. However, it stands out because it also covers a couple of other languages used commonly in data science – namely R and Azure Machine Learning. As a result, the course is an excellent choice for intermediate data scientists who want to get to grips with the main three programming languages they’ll likely use in the field.

It’s a five-week course, with Microsoft recommending between three and four hours of learning per week, and it’s delivered in English. Each weekly module is capped with a quiz that tests your knowledge. The modules cover everything from data science basics to creating machine learning models in Azure Machine Learning.

Of course, the biggest benefit of this course (aside from the content) is the Microsoft-approved certification you get at the end. Any employer who sees Microsoft on your CV will sit up and take notice. Still, you’ll need to build on what you learn here with a more advanced data science tutorial, ideally one that covers more real-world applications of working with data.

Best Data Science Tutorials for Advanced Learners

Once you’re secure in your foundational knowledge and you have a good idea of how to apply data science practices, you’re ready to step into a more advanced data science tutorial. Here are two options.

Data Science Tutorial – Learn Data Science From Scratch (DataFlair)

Think of DataFlair’s main data science tutorial page as a hub world in a video game. There are dozens of different directions in which to take your studying, and you’re in complete control of where you go and what you learn. The page hosts over 370 tutorials (free of charge) that cover everything from the basics of data science to using data mining and Python to parse through massive data sets.

The sheer depth of coverage makes this set of tutorials ideal for the advanced learner. The more basic sides of the course can fill in any knowledge gaps that weren’t covered in previous tutorials you’ve taken. And on the more advanced side, you’ll be exposed to real-world examples that show you how to apply your theoretical knowledge in a practical environment. There’s even a set of quizzes that you can use to test your understanding of what you read.

There are some drawbacks, namely that this data science tutorial doesn’t offer a certificate and is less interactive than many paid courses. However, self-paced learners who thrive when presented with pages of theoretical knowledge will find almost everything they need to know about data science in this collection.

MicroMasters® Program in Statistics and Data Science (Massachusetts Institute of Technology)

By the time you’re at the advanced stage of learning data science, you’ll probably want an official certification to take pride of place on your CV. This mini-Master’s degree comes from the Massachusetts Institute of Technology (MIT), which is one of the world’s leading technology and engineering schools.

The course lasts for one year and two months, with between 10 and 14 hours of study required per week, making it a choice only for those who can commit to a part-time consistent learning schedule. It’s also not a free data science tutorial, as you’ll have to pay £1,210 (approx. €1,401) for the program.

If you can vault those hurdles, you get a graduate-level course that teaches you how to develop the machine learning models used in modern data science. Plus, having the letters “MIT” on your course certification (and the networking opportunities that come with learning from some of the institutions leading professors) makes this course even more valuable.

Find the Best Data Science Tutorials for Your Skill Level

Whether you’re taking your first tentative steps into the world of data science or you’re an advanced learner looking to brush up your skills, there’s a data science tutorial out there for you. The six highlighted in this article represent the best data science tutorials available (two for each skill level) on the web.

Let’s close by answering a key question – why complete one of these tutorials?

Precedence Research has the answer, stating that the data science field will enjoy a compound annual growth rate (CAGR) of 16.43% between 2022 and 2030. Rapid growth means more job opportunities (and higher salaries) for those with data science skills. Use these tutorials to build your skill base before shifting your career focus to a field that looks set to explode as Big Data becomes more crucial to how companies operate.

Related posts

Times of Malta: Malta-based OPIT launches innovative AI tool for students, academic staff
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Sep 22, 2025 5 min read

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The launch was officially unveiled during an event held at Microsoft Italia in Milan, titled AI Agents and the Future of Higher Education.

A tech-focused higher education institution based and accredited in Malta has developed a new AI assistant designed to support both students and faculty.

In a statement, the Open Institute of Technology (OPIT), announced the launch of the OPIT AI Copilot.

With the Fall Term starting on September 15, OPIT said it has already launched beta testing with faculty champions and is currently piloting full-course integrations.

Students who will be part of the pilot-phase will be able to prompt the entire OPIT – Open Institute of Technology knowledge base, personalized to their own progress.

The platform was developed entirely in-house to fully personalize the experience for the students, and also make it a real-life playground for in-class projects. It is among the first custom-built AI agents to be deployed by an accredited European higher education institution.

The launch was officially unveiled during an event held at Microsoft Italia in Milan, titled AI Agents and the Future of Higher Education

The gathering brought together academics and technology leaders from prominent European Institutions, such as Instituto de Empresa (IE University), OPIT itself and the Royal College of Arts, to explore how artificial intelligence is reshaping the university experience.

The OPIT AI Copilot has been trained on the institute’s complete academic archive, a collection created over the past three years that includes 131 courses, more than 3,500 hours of recorded lectures, 7,500 study resources, 320 certified assessments, and thousands of exercises and original learning documents.

Unlike generic AI tools, the Copilot is deeply integrated with OPIT’s learning management system, allowing it to track each student’s progress and provide tailored support.

This integration means the assistant can reference relevant sources within the learning environment, adapt to the student’s stage of study, and ensure that unreleased course content remains inaccessible.

A mobile app is also scheduled for release this autumn, that will allow students to download exercise and access other tools.

During examinations, the Copilot automatically switches to what the institute calls an “anti-cheating mode”, restricting itself to general research support rather than providing direct answers.

For OPIT’s international community of 500 students from nearly 100 countries, many of whom balance studies with full-time work, the ability to access personalised assistance at any time of day is a key advantage.

“Eighty-five per cent of students are already using large language models in some way to study,” said OPIT founder and director Riccardo Ocleppo. “We wanted to go further by creating a solution tailored to our own community, reflecting the real experiences of remote learners and working professionals.”

Tool aims to cut correction time by 30%

The Copilot will also reduce administrative burdens for faculty. It can help grade assignments, generate new educational materials, and create rubrics that allow teachers to cut correction time by as much as 30 per cent.

According to OPIT, this will free up staff to dedicate more time to teaching and direct student engagement.

At the Milan event, Rector Francesco Profumo underlined the broader implications of AI in higher education. “We are in the midst of a deep transformation, where AI is no longer just a tool: it is an environment that radically changes how we learn, teach, and create,” he said.

“But it is not a shortcut. It is a cultural, ethical, and pedagogical challenge, and to meet it we must have the courage to rethink traditional models and build bridges between human and artificial intelligence.”

OPIT was joined on stage by representatives from other leading institutions, including Danielle Barrios O’Neill of the Royal College of Art, who spoke about the role of AI in art and creativity, and Francisco Machin of IE University, who discussed applications in business and management education.

OPIT student Asya Mantovani, also employed at a leading technology and consulting firm in Italy,  gave a first-hand account of balancing professional life with online study.

The assistant has been in development for the past eight months, involving a team of OPIT professors, researchers, and engineers.

Ocleppo stressed that OPIT intends to make its AI innovations available beyond its own institution. “We want to put technology at the service of higher education,” he said.

“Our goal is to develop solutions not only for our own students, but also to share with global institutions eager to innovate the learning experience in a future that is approaching very quickly.”

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E-book: AI Agents in Education
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Sep 15, 2025 3 min read

From personalization to productivity: AI at the heart of the educational experience.

Click this link to read and download the e-book.

At its core, teaching is a simple endeavour. The experienced and learned pass on their knowledge and wisdom to new generations. Nothing has changed in that regard. What has changed is how new technologies emerge to facilitate that passing on of knowledge. The printing press, computers, the internet – all have transformed how educators teach and how students learn.

Artificial intelligence (AI) is the next game-changer in the educational space.

Specifically, AI agents have emerged as tools that utilize all of AI’s core strengths, such as data gathering and analysis, pattern identification, and information condensing. Those strengths have been refined, first into simple chatbots capable of providing answers, and now into agents capable of adapting how they learn and adjusting to the environment in which they’re placed. This adaptability, in particular, makes AI agents vital in the educational realm.

The reasons why are simple. AI agents can collect, analyse, and condense massive amounts of educational material across multiple subject areas. More importantly, they can deliver that information to students while observing how the students engage with the material presented. Those observations open the door for tweaks. An AI agent learns alongside their student. Only, the agent’s learning focuses on how it can adapt its delivery to account for a student’s strengths, weaknesses, interests, and existing knowledge.

Think of an AI agent like having a tutor – one who eschews set lesson plans in favour of an adaptive approach designed and tweaked constantly for each specific student.

In this eBook, the Open Institute of Technology (OPIT) will take you on a journey through the world of AI agents as they pertain to education. You will learn what these agents are, how they work, and what they’re capable of achieving in the educational sector. We also explore best practices and key approaches, focusing on how educators can use AI agents to the benefit of their students. Finally, we will discuss other AI tools that both complement and enhance an AI agent’s capabilities, ensuring you deliver the best possible educational experience to your students.

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