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

CCN: Australia Tightens Crypto Oversight as Exchanges Expand, Testing Industry’s Appetite for Regulation
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 3 min read

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  • CCN, published on March 29th, 2025

By Kurt Robson

Over the past few months, Australia’s crypto industry has undergone a rapid transformation following the government’s proposal to establish a stricter set of digital asset regulations.

A series of recent enforcement measures and exchange launches highlight the growing maturation of Australia’s crypto landscape.

Experts remain divided on how the new rules will impact the country’s burgeoning digital asset industry.

New Crypto Regulation

On March 21, the Treasury Department said that crypto exchanges and custody services will now be classified under similar rules as other financial services in the country.

“Our legislative reforms will extend existing financial services laws to key digital asset platforms, but not to all of the digital asset ecosystem,” the Treasury said in a statement.

The rules impose similar regulations as other financial services in the country, such as obtaining a financial license, meeting minimum capital requirements, and safeguarding customer assets.

The proposal comes as Australian Prime Minister Anthony Albanese’s center-left Labor government prepares for a federal election on May 17.

Australia’s opposition party, led by Peter Dutton, has also vowed to make crypto regulation a top priority of the government’s agenda if it wins.

Australia’s Crypto Growth

Triple-A data shows that 9.6% of Australians already own digital assets, with some experts believing new rules will push further adoption.

Europe’s largest crypto exchange, WhiteBIT, announced it was entering the Australian market on Wednesday, March 26.

The company said that Australia was “an attractive landscape for crypto businesses” despite its complexity.

In March, Australia’s Swyftx announced it was acquiring New Zealand’s largest cryptocurrency exchange for an undisclosed sum.

According to the parties, the merger will create the second-largest platform in Australia by trading volume.

“Australia’s new regulatory framework is akin to rolling out the welcome mat for cryptocurrency exchanges,” Alexander Jader, professor of Digital Business at the Open Institute of Technology, told CCN.

“The clarity provided by these regulations is set to attract a wave of new entrants,” he added.

Jader said regulatory clarity was “the lifeblood of innovation.” He added that the new laws can expect an uptick “in both local and international exchanges looking to establish a foothold in the market.”

However, Zoe Wyatt, partner and head of Web3 and Disruptive Technology at Andersen LLP, believes that while the new rules will benefit more extensive exchanges looking for more precise guidelines, they will not “suddenly turn Australia into a global crypto hub.”

“The Web3 community is still largely looking to the U.S. in anticipation of a more crypto-friendly stance from the Trump administration,” Wyatt added.

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Agenda Digitale: Generative AI in the Enterprise – A Guide to Conscious and Strategic Use
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 6 min read

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By Zorina Alliata, Professor of Responsible Artificial Intelligence e Digital Business & Innovation at OPIT – Open Institute of Technology

Integrating generative AI into your business means innovating, but also managing risks. Here’s how to choose the right approach to get value

The adoption of generative AI in the enterprise is growing rapidly, bringing innovation to decision-making, creativity and operations. However, to fully exploit its potential, it is essential to define clear objectives and adopt strategies that balance benefits and risks.

Over the course of my career, I have been fortunate to experience firsthand some major technological revolutions – from the internet boom to the “renaissance” of artificial intelligence a decade ago with machine learning.

However, I have never seen such a rapid rate of adoption as the one we are experiencing now, thanks to generative AI. Although this type of AI is not yet perfect and presents significant risks – such as so-called “hallucinations” or the possibility of generating toxic content – ​​it fills a real need, both for people and for companies, generating a concrete impact on communication, creativity and decision-making processes.

Defining the Goals of Generative AI in the Enterprise

When we talk about AI, we must first ask ourselves what problems we really want to solve. As a teacher and consultant, I have always supported the importance of starting from the specific context of a company and its concrete objectives, without inventing solutions that are as “smart” as they are useless.

AI is a formidable tool to support different processes: from decision-making to optimizing operations or developing more accurate predictive analyses. But to have a significant impact on the business, you need to choose carefully which task to entrust it with, making sure that the solution also respects the security and privacy needs of your customers .

Understanding Generative AI to Adopt It Effectively

A widespread risk, in fact, is that of being guided by enthusiasm and deploying sophisticated technology where it is not really needed. For example, designing a system of reviews and recommendations for films requires a certain level of attention and consumer protection, but it is very different from an X-ray reading service to diagnose the presence of a tumor. In the second case, there is a huge ethical and medical risk at stake: it is necessary to adapt the design, control measures and governance of the AI ​​to the sensitivity of the context in which it will be used.

The fact that generative AI is spreading so rapidly is a sign of its potential and, at the same time, a call for caution. This technology manages to amaze anyone who tries it: it drafts documents in a few seconds, summarizes or explains complex concepts, manages the processing of extremely complex data. It turns into a trusted assistant that, on the one hand, saves hours of work and, on the other, fosters creativity with unexpected suggestions or solutions.

Yet, it should not be forgotten that these systems can generate “hallucinated” content (i.e., completely incorrect), or show bias or linguistic toxicity where the starting data is not sufficient or adequately “clean”. Furthermore, working with AI models at scale is not at all trivial: many start-ups and entrepreneurs initially try a successful idea, but struggle to implement it on an infrastructure capable of supporting real workloads, with adequate governance measures and risk management strategies. It is crucial to adopt consolidated best practices, structure competent teams, define a solid operating model and a continuous maintenance plan for the system.

The Role of Generative AI in Supporting Business Decisions

One aspect that I find particularly interesting is the support that AI offers to business decisions. Algorithms can analyze a huge amount of data, simulating multiple scenarios and identifying patterns that are elusive to the human eye. This allows to mitigate biases and distortions – typical of exclusively human decision-making processes – and to predict risks and opportunities with greater objectivity.

At the same time, I believe that human intuition must remain key: data and numerical projections offer a starting point, but context, ethics and sensitivity towards collaborators and society remain elements of human relevance. The right balance between algorithmic analysis and strategic vision is the cornerstone of a responsible adoption of AI.

Industries Where Generative AI Is Transforming Business

As a professor of Responsible Artificial Intelligence and Digital Business & Innovation, I often see how some sectors are adopting AI extremely quickly. Many industries are already transforming rapidly. The financial sector, for example, has always been a pioneer in adopting new technologies: risk analysis, fraud prevention, algorithmic trading, and complex document management are areas where generative AI is proving to be very effective.

Healthcare and life sciences are taking advantage of AI advances in drug discovery, advanced diagnostics, and the analysis of large amounts of clinical data. Sectors such as retail, logistics, and education are also adopting AI to improve their processes and offer more personalized experiences. In light of this, I would say that no industry will be completely excluded from the changes: even “humanistic” professions, such as those related to medical care or psychological counseling, will be able to benefit from it as support, without AI completely replacing the relational and care component.

Integrating Generative AI into the Enterprise: Best Practices and Risk Management

A growing trend is the creation of specialized AI services AI-as-a-Service. These are based on large language models but are tailored to specific functionalities (writing, code checking, multimedia content production, research support, etc.). I personally use various AI-as-a-Service tools every day, deriving benefits from them for both teaching and research. I find this model particularly advantageous for small and medium-sized businesses, which can thus adopt AI solutions without having to invest heavily in infrastructure and specialized talent that are difficult to find.

Of course, adopting AI technologies requires companies to adopt a well-structured risk management strategy, covering key areas such as data protection, fairness and lack of bias in algorithms, transparency towards customers, protection of workers, definition of clear responsibilities regarding automated decisions and, last but not least, attention to environmental impact. Each AI model, especially if trained on huge amounts of data, can require significant energy consumption.

Furthermore, when we talk about generative AI and conversational models , we add concerns about possible inappropriate or harmful responses (so-called “hallucinations”), which must be managed by implementing filters, quality control and continuous monitoring processes. In other words, although AI can have disruptive and positive effects, the ultimate responsibility remains with humans and the companies that use it.

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