In a world of Big Data, companies need people who have the ability to analyze and reach conclusions from the reams of data they collect about customers. But data science extends far beyond the corporate. Any industry that uses data (i.e., practically all of them) needs data-minded people who can use the latest AI-driven tools to help them scour large datasets.

That’s where you come in. As a potential data scientist, you’ll enter an industry that’s experiencing enormous growth to the point where it will be worth $103 billion (approx. €96.37 billion) by 2027. That market growth translates into demand for talented data scientists, which is already seen today as Coresignal’s data – 8,000 available job postings across eight leading positions in the first five months of 2022 alone – demonstrates.

So, the benefits of earning a free data science certification are obvious – you’re entering a growing industry with huge demand that leads to a better salary. But you need to know which courses will help you break into that industry. This article highlights four of the best free data science courses around.

Top Four Free Data Science Courses

As wonderful as the word “free” may be to budget-conscious students, you still need to know that you’re getting something of value from your data science course. The following options deliver a stellar educational experience and leave you with a qualification that employers recognize.

An Introduction to Data Science (Udemy)

Every journey starts with a first step, and it’s crucial that you take the first step into data science with a course that covers the basics and lays a foundation on which you can build. An Introduction to Data Science does just that by teaching you what data science is and how it applies to the modern world.

That teaching starts with a history lesson that shows how interactions with data (and data collection methods) have evolved over the years. From there, you’ll learn how data science applies in modern industry and discover the difference between actual valuable data and the oodles of “noise” that are in datasets.

It’s a quick and easy course, weighing in at 43 minutes spread across six video lectures, so you don’t have to make a huge time commitment. It’s delivered online by a Google Certified Python Expert named Kumar Rajmani Bapat and is ideal for getting the basics of data science down before you move on to a more intensive or focused course.

But the focus on the basics is also the biggest issue with this course. Rather than showing you the techniques a data scientist uses, the course focuses on what data science is and offers a roadmap for getting into the industry. It’s more about “what” than “how,” which may make the course too rudimentary for people who already have some knowledge of the subject. It’s also worth noting that this isn’t one of those free data science courses with certificate, as you’ll need to pay for an Udemy subscription to get your hands on a certificate of completion. You can still watch the videos and complete the course for free, though.

Introduction to Data Science (SkillUP)

With a similar name to the above Udemy course, you’d be forgiven for assuming that SkillUP’s Introduction to Data Science program teaches the same stuff. Though the course is aimed squarely at beginners, it takes a more in-depth approach that makes it the ideal follow-up to Udemy’s offering.

You start with the basic spiel about what data science is and how it applies to modern industry. But from there, the course tips into actual application by demonstrating some of the best Python programming libraries to use in the field. You’ll also dig deep into the algorithms used in data science, with linear regression analysis, confusion matrices, and logistic regression all getting some time to shine.

Given this higher focus on the skills you’ll need to learn to become a data scientist, the course is longer than Udemy’s offering. It clocks in at seven hours of videos and tutorials, all of which you access online and work through at your own pace. The course also expects you to have a solid grasp of math and programming (some experience with Python is a must) so this isn’t ideal for complete beginners to computer science.

This is a data science free online course with certificate, though there is a caveat. SkillUP only provides 90 days of free access to the course. If you feel it will take longer than that to get through the seven hours of tutorials, you’ll need to enroll in a paid subscription. The best approach here is to only start the course when you’re confident that you can block out the time needed to wrap it up within 90 days.

IBM Data Science Professional Certificate (Coursera)

Aimed squarely at the career-focused individual, IBM’s data science course is all about building the skills that set you on the right path to a career in the field. It takes a more practical approach, starting you off with the fundamentals before pushing you into a project where you’ll work with a real-world dataset and publish a report that’s analyzed by stakeholders simulating what you’ll experience in the working world.

The good news is that you don’t need to know anything about data science to get started with the course. It holds your hand as you learn the basics of what data science is (including what a data scientist actually does) and teaches you about the tools and programming languages you’ll use in the field. Once you have a grasp on the fundamentals, you’ll learn how to analyze and visualize data, in addition to creating machine learning models using Python, before wrapping up with the previously mentioned project.

The IBM Data Science Professional Certificate is a more intensive course than the others on this list. It’s essentially a mini degree, requiring you to invest 10 hours per week for five months into your learning. However, the course is provided entirely online, allowing you to schedule that learning time as you see fit. You’ll work through 10 modules as part of the certificate.

That time commitment may be a downside for those who can’t put 10 hours per week into a course, though that downside is outweighed heavily by the fact that you come out with an IBM certification. Having one of the leading names in computing on your certificate is enough to make any employer sit up and take notice.

Data Analysis With Python (freeCodeCamp)

The Python programming language (along with SQL and a few others) underpins almost everything that the modern data scientist does. Data Analysis with Python takes that concept and runs with it by providing a course that digs into using Python to read, analyze, and visualize data.

Along the way, you’ll learn about the basics of both Python and data analysis, though the real highlight comes from the many libraries and tools the course introduces. You’ll use Seaborn, Numpy, Mayplotlib, and Pandas during the course. All of which are libraries used by professionals to extract and visualize data. The course wraps up with a series of five projects, each testing a different set of skills learned via the modules, with your certification coming after you’ve completed all five.

This is one of those free data science courses that’s entirely self-paced and there are no time constraints or commitments involved. Once you’ve signed up for freeCodeCamp, you can save your progress through the course at any point and return whenever you’re ready. Theoretically, this means you could start the course, save your progress, and then return to it months later, though that isn’t recommended if you want to keep the information fresh in your mind. All told, the course contains 37 modules, plus the five projects required for certification, making it one of the most in-depth Python courses around.

The focus on Python is great for those who are unfamiliar with the language, though it also creates some issues. Namely, this isn’t the right course for those who don’t understand data science fundamentals. It jumps straight into analyzing datasets using Python, so those who don’t really understand what datasets are or how they apply to the modern world should start with a more beginner-oriented course.

Tips for Choosing the Right Data Science Course

You get the same benefit from all of the listed data science online courses – free entry. But each course offers something different. Use these tips to determine which is the right choice for you:

  • Assess your current skill level to pick a course that delivers what you need to know right now rather than a course that forces you to run before you can walk.
  • Determine your learning goals so you can see how the course fits into your roadmap for becoming a data scientist.
  • Consider the course’s format and duration as both will play a huge role in how you schedule your learning around your other commitments, be they work-related or personal.
  • Look for courses that offer hands-on project work once you’ve moved beyond learning the basics of data science.
  • Read reviews and testimonials from other students to see if people in your position get actual value from the course.

Start Your Journey With Free Data Science Courses Online

Every journey starts with a first step, and that first step could take you into a career that has massive potential for growth if you opt for the data science path. The four courses listed here each offer something different, from exploring the basics of what data science is to digging deep into the programming tools you’ll use to conduct data analysis and visualization. Completing one of the four sets you on the right path, though completing all four gives you a solid grounding (and a set of certifications) that make you immensely attractive to employers.

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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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