Combine mathematics with analytics, mix in programming skills, and add a dash of artificial intelligence, and you have the recipe for creating a data scientist. These professionals use complex technical skills to parse, analyze, and draw insights from complex datasets, enabling more accurate decision-making in the process.

As companies gather more data than ever before (both about their customers and themselves), these skills are in increasingly high demand. That’s demonstrated by data from the U.S. Bureau of Labor Statistics, which says that the number of data science jobs in the U.S. alone looks set to increase by 36% between 2021 and 2031.

That higher-than-average growth rate creates an opportunity for students, though grasping that opportunity requires a dedication to learning. This article explores the question of what is data science course material and highlights a selection of courses that set you on a data-propelled career path.

What to Expect From a Data Science Course

Answering the question of “what is data science course?” starts with examining the components of the typical course. Bear in mind that these components vary in nature and complexity depending on the specific course you take, though all are usually present.

Overview of Course Content

The content of a data science course is usually split into four core categories:

  • Statistics and Probability – Math underpins everything a data scientist does, as they use numbers to spot patterns and determine the likelihood of various potential outcomes. Most data science courses delve into statistics and probability for this reason, with more advanced courses often requiring a degree in a field related to these areas.
  • Programming – Whether it’s Python (the most popular data science programming language), R, or SQL, your course will teach you how to write in a language that machines understand.
  • Data Visualization and Analysis – Anybody can collect reams of data. It’s the ability to visualize that data (and draw insights from it) that sets data scientists apart from other professionals. A good course equips you with the ability to use visualization tools to shine a spotlight on what a dataset actually tells you.
  • Machine Learning and AI – The rise of machine learning transformed data science. Using algorithms created by data scientists, machines can analyze datasets presented to them and learn from the patterns to predict probabilities for different outcomes and even predict market trends. Your course will teach you how to create the algorithms that serve as a machine learning model’s “brain.”

Hands-On Projects and Real-World Applications

If you had the desire, you could read pages and pages on how to tune a car’s engine. But without practical and real-world wrench-in-hand experience working on an engine, you’ll never figure out how what you learn from books applies in the field.

The same line of thinking applies to data science, which is often so technically complex that it’s difficult to see how what you learn applies in the real world. A good data science course incorporates a real-world component through projects and exposure to faculty members who have direct experience in using the skills they teach.

Peer Collaboration and Networking

What is data science course for if not to learn how to become a data scientist? While learning the technical side is crucial, of course, a good course also puts you in contact with like-minded individuals who have the same (or similar) goals as you.

That contact helps you to build the collaborative skills you’ll need when you enter the workforce. But perhaps more importantly, it aids you in creating a network of peers who could lead you to job opportunities or work with you on entrepreneurial ventures.

Top Data Science Courses Available

With the components of a data science course established, you have a vital question to answer – what data science course should you take? The following are four suggestions (two online courses and two university courses) that give you a solid grounding in the subject.

Online Courses

Taking a data science course online gives you flexibility, though you may miss out on some of the collaborative and networking aspects that university-led courses provide.

Course 1 – What Is Data Science? (IBM via Coursera)

Coming with the stamp of approval from IBM, a leading name in the computer science field, this nine-hour course is suitable for beginners who want a self-paced learning approach. It’s part of a multi-part program (the IBM Data Science Professional Certificate) that’s designed to give you an industry-recognized qualification that could fast-track your entry into the field.

As for the course itself, it’s split into three parts, each containing multiple instructor-led videos and quizzes to test what you’ve learned. By the end, you’ll understand what data scientists do, build a basic understanding of various data science-related topics, and see how the profession relates to the modern business world. Granted, the course offers a surface-level understanding of the subject, with more complex topics examined in other classes. But it’s a superb tool for developing the foundation on which you can build with other courses.

Course 2 – Introduction to Data Science With Python (Harvard via edX)

Where IBM’s course equips you with general knowledge, Harvard’s online offering digs into the practical side of data science. Specifically, it focuses on using Python (and its many libraries) to solve data science problems drawn from real-world examples.

The course takes eight weeks, with study time between three and four hours per week. Ultimately, this class helps you build on your established programming skills and shows you how to apply them in a data science context.

As you may have guessed, that mention of building on existing skills means you’ll need a solid understanding of Python to participate in this free course. But assuming you have that, Harvard’s class is ideal for showing you just how flexible the language can be, especially when developing machine learning algorithms. Furthermore, simply having the word “Harvard” on your online certification adds credibility to your CV when you start applying for jobs.

University Programs

University programs demand a larger time (and monetary) commitment than purely online programs, though the upside is that you get a more prestigious qualification at the end. These two courses are ideal, with one even being a hybrid of online and university-level courses.

Course 1 – Master in Applied Data Science & AI (OPIT)

Let’s get the obvious out of the way first – you’ll need a BSc degree, or an equivalent, in a computer science or mathematical subject to take OPIT’s data science Master’s degree course.

Assuming you meet that prerequisite, this course comes in 18 and 12-month varieties, with the latter being a fast-tracked version that delivers the same content while asking you to dedicate more time to studying. It costs €6,500 to take, though early bird discounts are available, and an EU-accredited university delivers it.

The course eschews traditional exams by taking a progressive assessment approach to determine how well you’re absorbing the materials. It’s also focused on the practical side of things, with the application of data science in business problem-solving and communication being core modules.

Course 2 – MSc in Social Data Science (University of Oxford)

As the world’s leading university for seven consecutive years, according to Times Higher Education (THE) World University Rankings, the University of Oxford has outstanding credentials. And its MSc in Social Data Science is an interesting course to take because it specializes in a specific subject area – human behavior.

The degree stands on the precipice of an emerging field as it focuses on using data science to analyze, critique, and reevaluate existing social processes. It combines general machine learning models with more specialized data science tools, such as natural language processing and computer vision, to equip students with a high degree of technical knowledge.

That knowledge doesn’t come cheap, either in time or monetary commitment. The University of Oxford expects students to devote 40 hours per week to study, with overseas students having to pay £30,910 (approx. €35,795) to participate. While these investments are naturally intimidating, the university’s prestige makes the time and money you spend worthwhile when you start speaking to employers.

Factors to Consider When Choosing a Data Science Course

The four courses presented here each offer something different in terms of delivery and the expertise required of the student to participate. When choosing between them (and any other courses you find), you should consider the following questions:

  • Does the course content and curriculum align with your career goals?
  • Can you make time for the course within your schedule, and how much flexibility does it offer?
  • Do the instructors provide the expertise you need and teach in a style that suits your preferred way of learning?
  • Will you get an adequate return on your investment, both in terms of the prestige of the certification you receive and the knowledge you gain?
  • Have past (or current) students recommended the course as a good option for prospective data scientists?

The Benefits of Completing a Data Science Course

Given the technical nature of the subject, you may be asking yourself what is data science course content going to deliver in terms of benefits to your life. The answers are as follows:

  • Your skills improve your job prospects by putting you in pole position to enter a market that’s set for substantial growth over the next 10 years.
  • The problem-solving and analytical tools you gain are useful in the data science field and other career paths.
  • Any course you select puts you in contact with industry professionals who offer networking opportunities that could lead to a new job.
  • You get to learn about (and experiment with) cutting-edge tools and technologies that will become the standard for modern business, and more, in the coming years.

What Is Data Science Course – It’s Your Route Into a Great Career

Let’s conclude by reiterating something mentioned at the start of the article – the data science sector will grow by 36% over the next decade or so.

That growth alone demonstrates the importance of data science, as well as why choosing the right course is so critical to your future success. With the right course, you make yourself a desirable candidate to organizations that are quickly accepting that they need data scientists to help them make decisions for the future.

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OPIT Is Turning 2! What Have We Achieved in the Last 2 Years?
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Aug 7, 2025 6 min read

The Open Institute of Technology (OPIT) is turning two! It has been both a long journey and a whirlwind trip to reach this milestone. But it is also the perfect time to stop and reflect on what we have achieved over the last two years, as well as assess our hopes for the future. Join us as we map our journey over the last two years and look forward to future plans.

July 2023: Launching OPIT

OPIT officially launched as an EU-accredited online higher education institution in July 2023, and offered two core programs: a BSc in Modern Computer Science and an MSc in Applied Data Science and AI. Its first class matriculated in September of that year.

The launch of OPIT was several years in the making. Founder Riccardo Ocleppo was planning OPIT ever since he launched his first company, Docsity, in 2010, an online platform for students to share access to educational resources. As part of working on that project, Ocleppo had the chance to talk to thousands of students and professors and discovered just how big a gap there is between what is taught in universities today and job market demands. Ocleppo felt that this gap was especially wide in the field of computer science, and OPIT was his concept to fill that gap.

The vision was to provide university-level teaching that was accessible around the world through digital learning technologies and that was also affordable. Ocleppo’s vision also involved international professors and building strong relationships with global companies to ensure a truly international and fit-for-purpose learning experience.

One of the most important parts of launching OPIT was the recruitment of the faculty of professors, which Ocleppo was personally involved in. The idea was to build a roster of expert teachers and professionals who were leaders in the field and urge them to unite the teaching fundamentals with real-world applications and experience. The process involved screening more than 5,000 CVs, interviewing over 200 candidates, and recruiting 25 professors to form the core of OPIT’s faculty.

September 2023: The Inaugural Cohort

When OPIT officially launched, its first cohort included 100 students from 38 different countries. Divided between the BSc and MSc courses, students were also allowed to participate in one of two different tracks. Some chose the standard track to accommodate their existing work commitments, while others chose to fast-track to complete their studies sooner.

OPIT was pleased with its success in making the courses international and accessible, with notable representation from Africa. In the first cohort, 40% of MSc students were also from non-STEM fields, showing OPIT’s success at engaging professionals looking to develop skills for the modern workplace.

July 2024: A Growing Curriculum

Building on this initial success, in 2024, OPIT expanded its academic offering to include a second BSc program in Digital Business, and three new MSc programs in Digital Business & Innovation, Responsible Artificial Intelligence, and Enterprise Cybersecurity. These were all offered in addition to the original two programs.

The new course offerings led to total student numbers growing to over 300, hailing from 78 different countries. This also led to an expansion of the faculty, with professionals recruited from major business leaders such as Symantec, Microsoft, PayPal, McKinsey, MIT, Morgan Stanley, Amazon, and U.S. Naval Research. This focus on professional experience and real-world applications is ideal for OPIT as 80% of the student body are active working professionals.

January 2025: First Graduating Class

OPIT held its first-ever graduation ceremony in Valletta, Malta, on March 8, 2025. The ceremony was a hybrid event, with students attending both in person and virtually. The first graduating class consisted of 40 students who received an MSc in Applied Data Science and AI.

OPIT’s MSc programs include a capstone project that sees students apply their learning to real-world challenges. Projects included the use of large language models for the creation of chatbots in the ed-tech field, the digitalization of customer support processes in the paper and non-woven industry, personal data protection systems, AI applications for environmental sustainability, and predictive models for disaster prevention linked to climate change. Since many OPIT students realized their capstone projects within their organizations, OPIT also saw itself successfully facilitating digital innovation in the field.

July 2025: New Learning Environments

The next step for OPIT is not just to teach others how to leverage AI to work smarter, but to start applying AI solutions in our own business environment. To this end, OPIT unveiled its OPIT AI Copilot at the Microsoft AI Agents and the Future of Higher Education event in Milan in June 2025.

The OPIT AI Copilot is a specialist AI Agent designed to enhance learning in OPIT’s fully digital environment. OPIT AI Copilot acts as a personal tutor and study companion, and but rather than being trained on the World Wide Web, it is specifically trained on OPIT’s educational archive of around 3,500 hours of lectures and 3,000 proprietary documents.

The OPIT AI Copilot then provides real-time, personalized guidance that adapts to where the student is in the course and the progress they have shown in grasping the material. As well as pulling from existing materials, the OPIT AI Copilot can generate content to deepen learning, such as code samples and practical exams. It can also answer questions posed by the students with answers grounded in the official course material. The tool is available 24/7, and also has an intelligent examination mode, which prevents cheating.

In this way, OPIT AI Copilot enriches the OPIT learning environment by providing students with 24/7 personalized support for their learning journey, ideal for busy professionals balancing work and study. It is a step towards facing the challenge of “one-size-fits-all” education approaches that have plagued learning institutions for millennia.

September 2025: A New Cohort

On the heels of the OPIT AI Copilot launch, OPIT is excited about recruiting its next round of students, with applications open until September 2025. If you are interested in joining OPIT, you can learn more about its courses here.

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Authority Magazine: Paola Tirelli of RWS Group on the Future of Artificial Intelligence
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Aug 4, 2025 9 min read

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By Kate Mowbray, 7 min read


“To engage more women in the AI industry, I believe we need to start by highlighting the diversity of roles available. Not all of them are purely technical. AI needs linguists, designers, ethicists, project managers, and many other profiles. Showing that there’s space for different kinds of expertise can make the field feel more accessible. We also need more visible role models: women who are leading, innovating, and mentoring in AI.”

As part of our series about the future of Artificial Intelligence, I had the pleasure of interviewing Paola Tirelli, linguistic AI specialist with RWS Group. Paola is also an MSc in Applied Data Science and AI graduate of OPIT — Open Institute of Technology, a global online educational institution.

With over a decade in translation and project management, Paola is passionate about integrating technology with language services. She considers bridging language barriers and leading teams to success her strength.

Thank you so much for joining us in this interview series! Can you share with us the ‘backstory” of how you decided to pursue this career path in AI?

Mybackground is in linguistics and localization, and I’ve spent years working with translation, quality assurance, and automation tools. I’ve always been fascinated by the intersection of language and technology. The turning point came when I realized I had reached a plateau in my role and felt a strong urge to grow, contribute more meaningfully, and understand the changes reshaping the industry.

That curiosity naturally led me to AI, a space where my linguistic expertise could meet innovation. I began to see how powerful AI could be in solving specific challenges in localization, especially around quality and efficiency. This inspired me to pursue a Master’s in Applied Data Science and AI at OPIT, to deepen my skills and explore how to bridge my domain knowledge with the new tools AI offers.

What lessons can others learn from your story?

It’s never too late to reinvent yourself. You don’t need to have a technical background from the start to enter the AI field. With strong motivation, curiosity, and a willingness to learn, you can go very far.

Embracing your own expertise, whatever it may be, can actually become your greatest asset. AI isn’t just about code and algorithms; it’s about solving real-world problems, and that requires diverse perspectives. If you’re driven by purpose and open to growth, you can not only adapt to change, but you can help shape it.

Can you tell our readers about the most interesting projects you are working on now?

What I find most exciting about my current work is the opportunity to experiment and explore where AI can truly be a game changer in the localization space. I’m particularly interested in projects that would have been unthinkable just a few years ago, initiatives involving massive amounts of data or complex workflows that no client would have considered feasible due to time, cost, or resource constraints. Thanks to AI, we can now approach these challenges in entirely new ways, unlocking value and enabling solutions that were previously out of reach, such as automated terminology extraction or adapting content across different language variants.

None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?

I’m especially grateful to the person who would later become my manager, Marina Pantcheva. At the time, I had already started my Master’s at OPIT and was looking for the right direction to apply what I was learning. I knew I wanted to stay within my company, but I wasn’t sure where to focus.

Then I attended a talk she gave on AI. It was clear, engaging, and incredibly inspiring. It felt like a calling. I knew I wanted to work with her and be part of her team. When I eventually joined the AI team, she believed in my potential from the start. She gave me the space to ask questions, explore ideas, and gradually take on more responsibility. That trust and support made all the difference. It helped me grow into this new field with confidence and purpose.

What are the 5 things that most excite you about the AI industry? Why?

· We’re writing the future — AI is still in its early stages, and we don’t yet know the limits of what it can do. Being part of this journey feels like contributing to something truly transformative.

· Unthinkable opportunities are now possible — Tasks that once required enormous manual effort or were simply out of reach due to scale or complexity are now achievable. AI opens doors to projects that were previously unimaginable.

· Access to knowledge like never before — AI enhances how we interact with information, making it faster and more intuitive to explore, learn, and apply knowledge across domains.

· Cross-disciplinarity — AI touches every field, so it’s full of opportunities for people from different backgrounds.

· Problem-solving at scale — AI can help automate tedious tasks and improve decision-making in complex workflows.

What are the 5 things that concern you about the AI industry? Why?

· AI systems are not 100% reliable, and their outputs can sometimes be inaccurate or misleading. This raises questions about how much we can (or should) trust them, especially in high-stakes contexts.

· As we integrate AI into more aspects of our work and lives, there’s a risk of becoming overly reliant on it, potentially at the expense of human judgment, creativity, and critical thinking.

· If we delegate too much to machines, we may gradually lose some of our own cognitive abilities, like problem-solving, memory, or even language skills, simply because we’re not exercising them as much.

· Without clear communication and reskilling strategies, AI can be perceived as a threat rather than a tool. This fear can create resistance and anxiety, especially in industries undergoing rapid transformation.

· From bias in algorithms to the misuse of generative tools, the ethical challenges are real. We need strong frameworks to ensure AI is developed and used responsibly, with transparency and accountability.

As you know, there is an ongoing debate between prominent scientists, (personified as a debate between Elon Musk and Mark Zuckerberg,) about whether advanced AI poses an existential danger to humanity. What is your position about this?

I think it’s important to separate science fiction from science. While I don’t believe current AI poses an existential threat, I do believe that we need to be very intentional about how we develop and use it. The real risks today are more about misuse, bias, and lack of transparency than about a doomsday scenario.

What can be done to prevent such concerns from materializing? And what can be done to assure the public that there is nothing to be concerned about?

Transparency and education are key. We need to involve more people in the conversation; not just engineers, but also linguists, ethicists, teachers, and everyday users. Clear communication about what AI can and cannot do would help build trust. Regulation also has to catch up with the speed of innovation, without stifling it.

As you know, there are not many women in the AI industry. Can you advise what is needed to engage more women into the AI industry?

My perception is slightly different, because I come from the localization industry, where there’s a strong presence of women. So, when I transitioned into AI, I brought with me a sense of belonging and confidence that not everyone may feel when entering a more male-dominated space.

To engage more women in the AI industry, I believe we need to start by highlighting the diversity of roles available. Not all of them are purely technical. AI needs linguists, designers, ethicists, project managers, and many other profiles. Showing that there’s space for different kinds of expertise can make the field feel more accessible. We also need more visible role models: women who are leading, innovating, and mentoring in AI.

Representation matters. When you see someone like you doing something you thought was out of reach, it becomes easier to imagine yourself there too.

What is your favorite “Life Lesson Quote”? Can you share a story of how that had relevance to your own life?

It’s never too late to be what you might have been,” by George Eliot.

This quote really resonated with me when I decided to shift my career path toward AI. Starting a Master’s in Applied Data Science and AI while working full-time wasn’t easy, but that quote gave me the courage to step into a field that initially felt far from my comfort zone, and to trust that my unique background could actually be a strength, not a limitation.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger.

If I could start a movement, it would focus on democratizing access to AI education and tools, especially for people from non-technical backgrounds. I truly believe that AI should not be limited to engineers or data scientists. It has the potential to empower professionals from all fields, from linguists to educators to healthcare workers. I’d love to see a world where people feel confident using AI not just as a tool, but as a partner in creativity, problem-solving, and innovation, regardless of their background, gender, or location.

How can our readers further follow your work online?

I usually share updates on LinkedIn: https://www.linkedin.com/in/paola-tirelli-9abbb32a9/

This was very inspiring. Thank you so much for joining us!

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