Unlock business impact with fully funded Data & AI apprenticeships. Build essential skills or master advanced AI at no cost to you!
Learn how to deliver real business impact with AI in your organisation.
Explore our enterprise solutions for data & AI upskilling. Empower your organisation to achieve transformation at scale with proven ROI.
Equip your leaders with the strategic vision and critical AI knowledge needed to confidently navigate the AI landscape and drive innovation.
Our Mission? To Enable corporate and government organisations to achieve their business goals by educating their workforce with critical data transformation skills to succeed in the AI era.
Cambridge Spark programmes use EDUKATE.AI for instant feedback, accelerating learning and fostering peer support.
Get the latest news and insights from Cambridge Spark.
Tune in and get cutting-edge insights from UK's top data & AI leaders.
Explore the Cambridge Spark Practitioner Pathway
.png)
If you're ready to boost your data capabilities and drive digital transformation, this session is for you. Learn how the Level 4 Data Analyst and Level 5 Data Engineer programmes help learners master technical concepts like data modelling, cloud-based data architecture, and automation. You’ll also gain a clear view of the full data career journey - from foundational roles like Data Citizen, to advanced roles like AI Engineer - and how Cambridge Spark’s pathway enables that progression.
🎙️ Ursah Sethi, Customer Solutions Advisor Team Lead at Cambridge Spark
🎙️ Alaa Abedrabbo, Curriculum Product Manager at Cambridge Spark
Agenda:
Apprenticeships provide a well-defined route for internal talent development in England*. Apprenticeships are available to both new and existing employees who work 30 hours or more per week. Funded by the Apprenticeship Levy, apprenticeships are a cost-effective way to upskill employees in analytics, data science and AI.
*If not employed in England, apprenticeships can be funded commercially.
“Doing the apprenticeship has helped me glue everything together and fill the gaps in the knowledge I had to make me more confident in delivering the data science models I want to create.”
