As a well-known programming language, Python dominates the data science field. Its prominence in the industry represents the main reason why so many job offers include Python skills as a hard requirement.

Of course, all of the hype around Python has practical ramifications. This programming language is suitable for people without a programming background. If you have a sufficient grasp of technology, chances are you’ll get how Python works in a few weeks.

Besides being beginner-friendly, Python is practically built for math and statistical analysis. Plus, data visualization becomes nearly effortless when you use specific Python libraries dedicated to the task.

The point is that Python makes numerous data science tasks and operations easier. If you’re interested in data science, learning this versatile programming language will take your professional development to a new level.

Fortunately, you can find plenty of courses teaching everything from the basics to advanced functions in Python. Let’s look at the best Python data science tutorial and course options.

Factors to Consider When Choosing a Python Data Science Course

Before you start a particular course, it would be best to consider the specifics. The criteria that should guide your decision include:

  • The content of the course: Some courses will be introductory, while others will offer advanced lessons. You should start with a course that aligns with your proficiency level.
  • Instructor’s expertise: Ideally, you’ll want an industry expert to teach you about Python. Experienced lecturers or proven professionals will know all of the ins and outs, and they’ll be able to transfer that knowledge to you.
  • Course duration and flexibility: If you’re looking for a course, you don’t want an experience that will last an entire year. On the other hand, you shouldn’t expect too much from an hour-long course. Additionally, the course structure should be flexible enough to allow you to complete it at your own pace.
  • Practical projects and applications: Python is a living programming language that sees plenty of use in the real world. On that note, the course you take should offer a hands-on experience and show you how to apply your new knowledge in practice.
  • Course reviews and ratings: Although this shouldn’t be your primary clue when making a decision, taking a look at what others say about the course certainly won’t hurt. You’ll want to stay away from courses with mostly negative reviews, especially if the reviewers make unsubstantiated claims.
  • Pricing and value: Course pricing may vary from ludicrously expensive to free. While our list doesn’t include any outrageously overpriced courses, you’ll find a quality free one in there. The bottom line here is straightforward: Does the course fit in with your budget and what do you get for the price?

Top Python Data Science Courses and Tutorials

ILX Group – Python E-Learning

This Python data science course deals with the basic functionality of the programming language and teaches you how to apply it in practice. It contains in-depth information about command running, dictionaries, methods, and shell scripting. No final exam is necessary to complete the course.

Key Topics

  • The basics of Python programming
  • File and data operations
  • Logging and test infrastructure
  • Conditional statements
  • Networking
  • Shell scripting
  • Django web framework

Instructor’s Background

Information about the instructor for this course isn’t available on ILX Group.

Course Duration and Format

The course is in e-learning format and is delivered entirely online. It will take you about eight hours to complete. Instead of a final exam, you’ll complete the course by submitting the required project that must meet specific set criteria.

Pricing and Enrollment

Enrolling in this course will cost €450 +VAT. You won’t need to fulfill any additional requirements to make a start. Paying the one-time fee will grant you a full year of access to the course resources.

Pros

  • Provides a solid foundation for Python programming
  • No limitations on enrollment or availability
  • Offers practical knowledge and projects

Cons

  • E-learning tools used throughout the course aren’t defined
  • No information about the instructor or their credentials

Python Institute – Data Analysis Essentials With Python

The Python Institute is a group devoted to Python education. The Data Analysis Essentials with Python is only one of the courses this institution provides. It’s an intermediate-level program focused on data analysis using the tools within the Python programming language.

Key Topics

  • Data analysis
  • Algorithmic and analytical thinking
  • Data visualization
  • Statistics
  • Data mining and modeling
  • Programming
  • Data-based decision-making

Instructor’s Background

No instructor information can be found on the Python Institute site regarding this particular course. However, it’s worth mentioning that the institute is run by industry experts with substantial experience in the IT sector. These experts are also responsible for the institute courses.

Course Duration and Format

The Data Analysis Essentials with Python course will last for up to six weeks, provided you devote about eight hours weekly to studying the material. The course is delivered online.

Pricing and Enrollment

One of the greatest advantages of this course is its pricing: Data Analysis Essentials with Python is completely free. However, this course isn’t for beginners. You’ll need previous knowledge of the key concepts in Python programming. The Python Institute recommends completing their beginner courses or coming into this program with some experience.

Pros

  • Course designed by industry professionals
  • Free for all users
  • May serve as a preparatory course for Python Certified Associate in Data Analytics (PCAD) certification

Cons

  • No information about the lecturer
  • Exact delivery methods aren’t specified

Python-Course – Fundamental Python Course

The Fundamental Python Course is designed as a comprehensive introduction to programming methods in Python. The course will take you through the fundamentals of the programming language and include practical solutions in the Python environment.

Key Topics

  • Python introductory lessons
  • Script editing and execution
  • Working in the Python shell
  • Expressions, operators, assignments, and variables
  • Dictionaries, stacks, loops, and lists
  • Handling files and exceptions
  • Conditional statements
  • Packages and modules

Instructor’s Background

The instructor for live courses is Bernd Klein. A Python expert with a Saarland University diploma in Computer Science, specializing in computer languages, Klein has taught at the Saarland University, EWH, Koblenz, and the University of Freiburg, where he still holds a teaching position.

Klein is also the founder of the programming language teaching platform, Bodenseo.

Course Duration and Format

The course lasts for five days and includes a live class format. While Klein usually holds classes in person, courses are currently provided online. To participate on this course, you’ll need a network-ready computer with a microphone. No additional software is needed.

Pricing and Enrollment

The on-site variant of the course costs €1,450 per day, while open classes start from €349 daily. There are no other requirements for the course.

Pros

  • Taught by an experienced lecturer
  • Offers a complete coverage of Python-related subjects
  • Advanced optional topics

Cons

  • Very pricey compared to the competitors
  • Doesn’t provide a certificate

Additional Resources for Mastering Python Data Science

If you want an alternative to an actual Python data science course, you may wish to turn to other resources that will help you master the subject. In particular, these would be books and digital resources like forums, eBooks, podcasts, YouTube channels, websites, and blogs.

For some of the best Python forums and online communities, check out the following:

Great books on Python include:

  • Head-First Python, by Paul Barry
  • Think Python, by Allen B. Downey
  • Learn Python 3 the Hard Way, by Zed A. Shaw
  • Python Crash Course, by Eric Matthes

If printed media isn’t your style, you can find an excellent list of free Python eBooks on Codeburst.io.

On the other hand, you might not want to read too much while learning Python. In that case, you’ll be glad to learn that there are numerous podcasts on the subject that you can tune in to right now:

Unsurprisingly, YouTube also has plenty of Python data science course and tutorial channels. Here are our top picks:

  • The New Boston
  • Sentdex
  • Real Python
  • PyCon – This isn’t a particular YouTube channel, but rather a search query. Browse the search results on YouTube, and you’ll find videos for Python-dedicated conferences from around the world.
  • Michael Kennedy

Finally, there’s an abundance of blogs and websites dedicated to Python resources and knowledge:

Learn to Program in Python Like a Pro

The internet is full of quality Python data science tutorial and course pages. You can find free and premium resources to hone your skills in the programming language or get familiar with the fundamental concepts.

Whichever resource type you choose, rest assured that learning practical Python skills will be a valuable addition to your resume. After all, data science is a constantly developing field in which expanding your knowledge base and skillset can only be a huge plus. If you’ve found a program you like in this article, don’t hesitate to jump right into it and expand your horizons.

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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

Source:


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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