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.

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Il Sole 24 Ore: Integrating Artificial Intelligence into the Enterprise – Challenges and Opportunities for CEOs and Management
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 14, 2025 6 min read

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Expert Pierluigi Casale analyzes the adoption of AI by companies, the ethical and regulatory challenges and the differentiated approach between large companies and SMEs

By Gianni Rusconi

Easier said than done: to paraphrase the well-known proverb, and to place it in the increasingly large collection of critical issues and opportunities related to artificial intelligence, the task that CEOs and management have to adequately integrate this technology into the company is indeed difficult. Pierluigi Casale, professor at OPIT (Open Institute of Technology, an academic institution founded two years ago and specialized in the field of Computer Science) and technical consultant to the European Parliament for the implementation and regulation of AI, is among those who contributed to the definition of the AI ​​Act, providing advice on aspects of safety and civil liability. His task, in short, is to ensure that the adoption of artificial intelligence (primarily within the parliamentary committees operating in Brussels) is not only efficient, but also ethical and compliant with regulations. And, obviously, his is not an easy task.

The experience gained over the last 15 years in the field of machine learning and the role played in organizations such as Europol and in leading technology companies are the requirements that Casale brings to the table to balance the needs of EU bodies with the pressure exerted by American Big Tech and to preserve an independent approach to the regulation of artificial intelligence. A technology, it is worth remembering, that implies broad and diversified knowledge, ranging from the regulatory/application spectrum to geopolitical issues, from computational limitations (common to European companies and public institutions) to the challenges related to training large-format language models.

CEOs and AI

When we specifically asked how CEOs and C-suites are “digesting” AI in terms of ethics, safety and responsibility, Casale did not shy away, framing the topic based on his own professional career. “I have noticed two trends in particular: the first concerns companies that started using artificial intelligence before the AI ​​Act and that today have the need, as well as the obligation, to adapt to the new ethical framework to be compliant and avoid sanctions; the second concerns companies, like the Italian ones, that are only now approaching this topic, often in terms of experimental and incomplete projects (the expression used literally is “proof of concept”, ed.) and without these having produced value. In this case, the ethical and regulatory component is integrated into the adoption process.”

In general, according to Casale, there is still a lot to do even from a purely regulatory perspective, due to the fact that there is not a total coherence of vision among the different countries and there is not the same speed in implementing the indications. Spain, in this regard, is setting an example, having established (with a royal decree of 8 November 2023) a dedicated “sandbox”, i.e. a regulatory experimentation space for artificial intelligence through the creation of a controlled test environment in the development and pre-marketing phase of some artificial intelligence systems, in order to verify compliance with the requirements and obligations set out in the AI ​​Act and to guide companies towards a path of regulated adoption of the technology.

Read the full article below (in Italian):

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The Lucky Future: How AI Aims to Change Everything
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 10, 2025 7 min read

There is no question that the spread of artificial intelligence (AI) is having a profound impact on nearly every aspect of our lives.

But is an AI-powered future one to be feared, or does AI offer the promise of a “lucky future.”

That “lucky future” prediction comes from Zorina Alliata, principal AI Strategist at Amazon and AI faculty member at Georgetown University and the Open Institute of Technology (OPIT), in her recent webinar “The Lucky Future: How AI Aims to Change Everything” (February 18, 2025).

However, according to Alliata, such a future depends on how the technology develops and whether strategies can be implemented to mitigate the risks.

How AI Aims to Change Everything

For many people, AI is already changing the way they work. However, more broadly, AI has profoundly impacted how we consume information.

From the curation of a social media feed and the summary answer to a search query from Gemini at the top of your Google results page to the AI-powered chatbot that resolves your customer service issues, AI has quickly and quietly infiltrated nearly every aspect of our lives in the past few years.

While there have been significant concerns recently about the possibly negative impact of AI, Alliata’s “lucky future” prediction takes these fears into account. As she detailed in her webinar, a future with AI will have to take into consideration:

  • Where we are currently with AI and future trajectories
  • The impact AI is having on the job landscape
  • Sustainability concerns and ethical dilemmas
  • The fundamental risks associated with current AI technology

According to Alliata, by addressing these risks, we can craft a future in which AI helps individuals better align their needs with potential opportunities and limitations of the new technology.

Industry Applications of AI

While AI has been in development for decades, Alliata describes a period known as the “AI winter” during which educators like herself studied AI technology, but hadn’t arrived at a point of practical applications. Contributing to this period of uncertainty were concerns over how to make AI profitable as well.

That all changed about 10-15 years ago when machine learning (ML) improved significantly. This development led to a surge in the creation of business applications for AI. Beginning with automation and robotics for repetitive tasks, the technology progressed to data analysis – taking a deep dive into data and finding not only new information but new opportunities as well.

This further developed into generative AI capable of completing creative tasks. Generative AI now produces around one billion words per day, compared to the one trillion produced by humans.

We are now at the stage where AI can complete complex tasks involving multiple steps. In her webinar, Alliata gave the example of a team creating storyboards and user pathways for a new app they wanted to develop. Using photos and rough images, they were able to use AI to generate the code for the app, saving hundreds of hours of manpower.

The next step in AI evolution is Artificial General Intelligence (AGI), an extremely autonomous level of AI that can replicate or in some cases exceed human intelligence. While the benefits of such technology may readily be obvious to some, the industry itself is divided as to not only whether this form of AI is close at hand or simply unachievable with current tools and technology, but also whether it should be developed at all.

This unpredictability, according to Alliata, represents both the excitement and the concerns about AI.

The AI Revolution and the Job Market

According to Alliata, the job market is the next area where the AI revolution can profoundly impact our lives.

To date, the AI revolution has not resulted in widespread layoffs as initially feared. Instead of making employees redundant, many jobs have evolved to allow them to work alongside AI. In fact, AI has also created new jobs such as AI prompt writer.

However, the prediction is that as AI becomes more sophisticated, it will need less human support, resulting in a greater job churn. Alliata shared statistics from various studies predicting as many as 27% of all jobs being at high risk of becoming redundant from AI and 40% of working hours being impacted by language learning models (LLMs) like Chat GPT.

Furthermore, AI may impact some roles and industries more than others. For example, one study suggests that in high-income countries, 8.5% of jobs held by women were likely to be impacted by potential automation, compared to just 3.9% of jobs held by men.

Is AI Sustainable?

While Alliata shared the many ways in which AI can potentially save businesses time and money, she also highlighted that it is an expensive technology in terms of sustainability.

Conducting AI training and processing puts a heavy strain on central processing units (CPUs), requiring a great deal of energy. According to estimates, Chat GPT 3 alone uses as much electricity per day as 121 U.S. households in an entire year. Gartner predicts that by 2030, AI could consume 3.5% of the world’s electricity.

To reduce the energy requirements, Alliata highlighted potential paths forward in terms of hardware optimization, such as more energy-efficient chips, greater use of renewable energy sources, and algorithm optimization. For example, models that can be applied to a variety of uses based on prompt engineering and parameter-efficient tuning are more energy-efficient than training models from scratch.

Risks of Using Generative AI

While Alliata is clearly an advocate for the benefits of AI, she also highlighted the risks associated with using generative AI, particularly LLMs.

  • Uncertainty – While we rely on AI for answers, we aren’t always sure that the answers provided are accurate.
  • Hallucinations – Technology designed to answer questions can make up facts when it does not know the answer.
  • Copyright – The training of LLMs often uses copyrighted data for training without permission from the creator.
  • Bias – Biased data often trains LLMs, and that bias becomes part of the LLM’s programming and production.
  • Vulnerability – Users can bypass the original functionality of an LLM and use it for a different purpose.
  • Ethical Risks – AI applications pose significant ethical risks, including the creation of deepfakes, the erosion of human creativity, and the aforementioned risks of unemployment.

Mitigating these risks relies on pillars of responsibility for using AI, including value alignment of the application, accountability, transparency, and explainability.

The last one, according to Alliata, is vital on a human level. Imagine you work for a bank using AI to assess loan applications. If a loan is denied, the explanation you give to the customer can’t simply be “Because the AI said so.” There needs to be firm and explainable data behind the reasoning.

OPIT’s Masters in Responsible Artificial Intelligence explores the risks and responsibilities inherent in AI, as well as others.

A Lucky Future

Despite the potential risks, Alliata concludes that AI presents even more opportunities and solutions in the future.

Information overload and decision fatigue are major challenges today. Imagine you want to buy a new car. You have a dozen features you desire, alongside hundreds of options, as well as thousands of websites containing the relevant information. AI can help you cut through the noise and narrow the information down to what you need based on your specific requirements.

Alliata also shared how AI is changing healthcare, allowing patients to understand their health data, make informed choices, and find healthcare professionals who meet their needs.

It is this functionality that can lead to the “lucky future.” Personalized guidance based on an analysis of vast amounts of data means that each person is more likely to make the right decision with the right information at the right time.

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