When Thijs Verhulst first considered the Data Science with Machine Learning & AI Career Accelerator, he had no coding experience. As a business innovator and founder of Angle of Impact, he focuses on creating new commercial models in music and sport.

When we asked what motivated him to pursue advanced data skills, Thijs said he knew it was time to deepen his technical capabilities: 

“Working closely with emerging tech and tech hubs, I saw the impact that AI, but also data, was making. I knew this was going to be a big part of any future role and project I would work on.” 

And not having much experience in the field didn’t deter him:

“I did not have any technical skills prior to the Data Science Career Accelerator. I had once tried HTML for one or two weeks, but nothing more than that. All technical skills and code writing were completely new to me.”

So, what changed? Two things: a structured way to build the basics, and a mindset shift about what Python really is.

Like many others, Thijs did a Python preparation course to build confidence in the prerequisite skills before starting the Career Accelerator.

“It mainly took away the concern that Python is difficult to learn. The language actually makes a lot of sense and the basics are quite easy to understand.”

From day one to first wins

When Thijs joined the Career Accelerator, he shared a common concern among non-technical learners — that “starting from zero” might slow him down. Instead, his early momentum came quickly:

“After the first 2–3 weeks into the programme, I started to enjoy exploring data sets and being able to uncover simple insights. That was the moment I knew that I wanted to learn this more in-depth.”

That confidence mattered. He didn’t need to be a full-time data scientist to get real value from the skills he was learning; he needed enough fluency to ask better questions, test ideas, and communicate evidence with stakeholders.

The Data Science Career Accelerator from the University of Cambridge Professional and Continuing Education (PACE) is designed to build both the deep technical expertise and the commercial understanding needed to use data science effectively in real-world settings.

Applying data in the real world

For Thijs, the goal of the Cambridge PACE Data Science Career Accelerator wasn’t about changing careers to data science — it was about deepening the way he applied data in his existing work.

“Working with LLMs and extracting insights from text data — I’ve just done this in a research project I did for my MBA, and I never would have imagined I could even think about adding this.”

That’s the goal of the programme: to help professionals from all backgrounds develop the ability to apply data science techniques to real-world challenges — whether that means analysing trends, improving decision-making, or unlocking new opportunities in their field.

For learners starting with little or no Python skills

Many professionals begin this journey just like Thijs: motivated, but unsure where to start with coding. That’s why we offer our own free Python Preparatory Course, a short, self-paced introduction that builds confidence before starting the programme.

You’ll cover all the essential programming concepts required, practise writing Python through structured exercises, and get guidance from your Enrolment Advisor throughout the process — so you know exactly what to do and when.

Once you’ve completed your preparatory Python studies, your journey on the Data Science with Machine Learning & AI Career Accelerator begins with a short technical assessment (which only takes about an hour to complete). This step helps ensure every learner starts the programme ready to thrive in the first technical modules.

Is this programme still ideal for people who already code?

Absolutely. Many learners enter the Career Accelerator with strong Python experience and use it to deepen their expertise in areas such as supervised learning, natural language processing, and time-series analysis. 

The combination of Cambridge PACE academics, industry mentors, and a live Employer Project ensures that even experienced technical professionals sharpen how they apply data science — not just how they code.

Building confidence without pretending it’s easy

We’ll never tell you it’s effortless — both the preparatory Python course and the programme itself require time and persistence. Basic programming skills are a prerequisite before enrolling, and there’s real work involved if you’re new to Python. Thijs’s preparation ahead of the programme paid off:

“Coding is less intimidating than it sounds… the best way to learn it is to just start doing it.”

Importantly, he also found that a non-technical background isn’t a weakness — it’s part of your edge:

“Sometimes I did feel behind… however, I also had a wide range of other skills and knowledge my peers did not have and therefore we could learn a lot from each other.”

That sense of collaboration is central to the programme. Learners come from diverse industries and backgrounds, creating a peer network where technical specialists and domain experts learn side by side — sharing insights, tackling projects together, and building confidence as a community.

Thijs’s advice if you’re hesitating

Thijs’s journey is proof that with focus and the right preparation, the programme is completely achievable, even for those who start with no Python experience.

“Think about what you would like to achieve, and also what other great skills you already have. The most important thing is just do it, a lot, and not be afraid to make mistakes.”

That mindset — curious, consistent, and outcome-focused — is exactly what turns non-coders into confident data practitioners.

What’s next? 

In just two years, Thijs built an advanced data science skillset, launched his business while completing the programme, and finished his MBA in International Sports and Entertainment at LALIGA Business School in October 2025.

“I’ve been able to add and expand both my data science and analytical skills, and add deep knowledge about the sports industry to my skill set in just two years. This helps me to become a versatile business leader.”

Now, he’s focused on growing Angle of Impact, uniting the sports and music sectors to develop business solutions that help shape the future of both industries.

Ready to see your next step?

Download the programme brochure to start mapping your Python starting point and the shortest route to data science readiness.