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Cambridge Spark Upskill your workforce with the data and AI specialists.

Cambridge Spark provides continuous professional development training for developers and data scientists. We offer intensive, part-time programmes, weekend bootcamps and regular community events on the latest tools and techniques in data science and software development, to equip individuals with the most relevant skills for industry needs. Cambridge Spark also runs specialised in-house training for corporate clients and teams looking to further develop their workforce.

✨ Alumni Spotlight: From community pharmacy to healthcare data, Roshen’s journey shows how combining frontline experienc...
09/10/2026

✨ Alumni Spotlight: From community pharmacy to healthcare data, Roshen’s journey shows how combining frontline experience with data skills can improve patient care.🏥

Starting as a pharmacy volunteer, Roshen discovered a passion for data analytics alongside his deep understanding of healthcare. Through the Data Analyst Apprenticeship, he developed skills in SQL, Python and Power BI, applying them to real-world challenges and identifying opportunities to improve hospital workflows and clinical care. 🚀

Today, as a Chief Pharmacy Technician at King’s College Hospital NHS Foundation Trust, Roshen combines healthcare expertise with data to uncover insights, improve processes and support better-informed decisions. From analysing prescribing patterns to developing tools for healthcare teams, his work demonstrates how data can translate into meaningful improvements for patients.

👏 We’re proud to have Roshen in our learner community. His journey shows how combining industry knowledge with data skills can turn real-world challenges into opportunities to improve healthcare and patient outcomes.

👉 Thinking about developing your career in data? Explore our Data Analyst Apprenticeship: https://bit.ly/4y20mJr

07/10/2026

What happens when tomorrow stops behaving like yesterday?

Most forecasting models are built on historical relationships. A rise in household income might predict higher demand. Seasonal patterns repeat because, historically, they always have.

But those relationships only hold until something changes the structure underneath them.

A pandemic is the clearest example. Behaviour shifts overnight, established correlations can disappear, and no amount of retraining on last year’s data can prepare a model for a discontinuity it has never seen before.

That’s not a problem with one particular algorithm. It’s a structural limitation of forecasting systems built on historical pattern-matching.
As organisations embed forecasting more deeply into planning, pricing and operations, there’s an important distinction to make: confidence in a model isn’t the same as resilience when the world changes.

In this clip from Inside the Algorithm, we explore why understanding where a model’s assumptions end is just as important as building the model itself.

Listen to the full episode. Link in the comments.

What does it really take to turn AI ambition into practical workforce capability?Today, Cambridge Spark and the British ...
06/10/2026

What does it really take to turn AI ambition into practical workforce capability?

Today, Cambridge Spark and the British Retail Consortium brought together senior people, L&D and transformation leaders for Building the AI Skills Roadmap for Retail.

The conversation focused on some of the challenges retailers are navigating right now, from leadership alignment and limited time for learning, to the gap between knowing AI tools exist and applying them meaningfully to day-to-day workflows.

Through peer discussion and practical role redesign, attendees explored how AI could reshape work across different parts of the retail workforce, and what skills need to be built alongside that change.

One message came through clearly:
People, not platforms, define retail AI success.

A big thank you to everyone who joined us and contributed so openly to the discussion.

Automation works best when it starts with the workflow, not the tool.Too often, organisations begin with the question:“W...
05/10/2026

Automation works best when it starts with the workflow, not the tool.

Too often, organisations begin with the question:
“What can we automate?”

But the better question is:
“Where is work getting stuck?”

Repetitive tasks, manual handovers, duplicated effort, inconsistent processes and avoidable errors are often where AI and automation can create the most practical value.

Join our webinar next week, When Workflows Meet AI: Rethinking Productivity Through Automation, to explore how organisations can identify the right opportunities for AI-enabled automation and apply it in ways that improve productivity without adding unnecessary complexity.

Register now to save your place: https://bit.ly/4ht1qRJ

01/10/2026

We're officially one of the UK's fastest-growing companies! 🎉

We're proud to share that Cambridge Spark has been recognised in the inaugural Financial Times UK's Fastest-Growing Companies 2027 ranking, placing in the top 100 overall and 4th in the Education and Social Services category.

This recognition marks another exciting milestone for us, following four consecutive years of being featured in the FT1000 Europe's Fastest-Growing Companies ranking.

As we continue to help organisations build the AI and data capabilities they need to thrive, we're incredibly grateful to our team, learners and partners who have been part of our journey.

Here's to continuing to grow, innovate and make an impact! 🚀

30/09/2026

Large language models can draft a legal brief and still tell you to walk to the car wash without your car.

In this special episode of Inside the Algorithm, Dr. Vaishakh Belle explores that contradiction and why it matters for organisations deploying AI.

LLMs learn from correlations in text, nothing more. No sensors, no physical world, no lived experience of what a car wash is for. That they produce coherent, useful answers at all is remarkable given how little they actually understand.

The failures show up in the gaps. Ask a frontier model whether to walk or drive to a car wash 50 metres away, and some will tell you to walk. The logic reads fine on the page. It falls apart the moment you ask why anyone goes to a car wash in the first place.

For anyone deploying these systems in production, this is the detail that matters more than benchmark scores. A model can pass every language test you throw at it and still lack any grounding in the real-world structure of the problem it's solving. That gap doesn't show up until the model meets a case nobody thought to test.

Enterprise AI teams need to treat this as a design constraint, not an edge case. Build in verification, keep humans in the loop where consequences are real, and stay honest about what your model actually knows versus what it's guessing well.

Where in your own AI systems is fluency standing in for understanding?

Listen to the full episode. Link in the comments.

Congratulations to The Shrewsbury and Telford Hospital NHS Trust, winners of the Advancing Patient Safety with Data and ...
29/09/2026

Congratulations to The Shrewsbury and Telford Hospital NHS Trust, winners of the Advancing Patient Safety with Data and Analytics category at the HSJ Patient Safety Awards 2026.

Their winning project, Orthodontic Oral Surgery Improvements, was recognised for demonstrating how data-driven pathway redesign can improve safety, experience and efficiency.

As proud partners of this category, we are delighted to celebrate the teams using data and analytics to drive meaningful improvements in patient safety across healthcare.

Congratulations again to the winners, and to all the shortlisted nominees for their inspiring work in advancing safer, smarter and more patient-centred care.

This afternoon at the HSJ Patient Safety Congress and Awards 2026, Cambridge Spark delivered a roundtable on: Turning da...
28/09/2026

This afternoon at the HSJ Patient Safety Congress and Awards 2026, Cambridge Spark delivered a roundtable on: Turning data into patient safety insight: barriers, behaviours, and breakthroughs

Moderated by Tilly Allen, NHS & Public Sector Accounts Director at Cambridge Spark, the discussion explored how NHS organisations can turn the data they already hold into meaningful patient safety insight, and what needs to change in skills, confidence, culture and ways of working to support wider adoption. Tilly was joined at the event by our colleagues Danny Erasmus and Nicole Greene.

We’re proud to be partnering with HSJ and supporting important conversations around data, AI and patient safety.

24/09/2026

"Everyone already knows how to use AI."

It’s a common corporate assumption, but baseline exposure doesn't equal deep operational fluency. Without structured capability building, tool adoption remains inconsistent, often leaving teams feeling hesitant or disconnected.

Aviva recognised this gap and chose to tackle it head-on. By partnering with Cambridge Spark, they’ve launched a 14-month AI Academy designed to meet colleagues exactly where they are, dispelling technology anxieties and building practical, role-based confidence.

The cultural focus? Reassurance over replacement.

"It’s not AI that will replace you; it’s the people who know how to use it effectively. Once you show people that these tools can get rid of the stuff they don’t like about their job... they realise they can focus on the reasons why they loved their role in the first place." - Eleanor Huggett, AI Learning and Academy Lead at Aviva

Learn how Aviva is using modern technical apprenticeships to future-proof their operations and deliver frictionless, empathetic service for their customers.

👉 Dive into the success story: https://eu1.hubs.ly/H0yzJZ_0

23/09/2026

"It was a black box" isn't an excuse anymore.

In this special compilation episode of Data & AI Mastery, we sit back down with guests from the FCA, Santander, Aviva and TalkTalk - and their view is clear across the board: if you deploy AI in your business, you own how it works and its impact on customers.

That doesn't mean ticking ten boxes and calling it done. It means building models you can actually explain, with performance metrics you can hand to the C-suite and say, with confidence, "we trust this." It means treating regulation like BCBS 239 and SS1/23 not as red tape, but as the foundation that lets you innovate safely.

The organisations getting this right aren't choosing between governance and creativity, but realising real accountability of the kind that survives scrutiny, is what makes bold AI adoption possible in the first place.

Where does accountability sit in your AI strategy? We'd love to hear how your organisation is thinking about it. Listen to the full conversation.
Link in the comments.

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