Homerton College, University of Cambridge

Data Science & AI, Cambridge Careers Masterclass

18 - 31 Jul, 2027

Age: 16+

B2

5950

Real Cambridge Experience

Live and study at a Cambridge College in a true academic setting and receive an official certificate of completion from Homerton College, University of Cambridge.

Explore Data Science & AI

Discover how data analysis, machine learning and intelligent technologies are transforming industries, advancing scientific research and creating new possibilities for innovation.

Build a Stronger Profile

Earn 8 UCAS tariff points to support university applications. Develop key academic skills including research, critical thinking, analysis, and academic writing

Data Science & AI, Careers Masterclass

WHY THIS PROGRAMME MATTERS

Cambridge-Style Technology Education

Explore data science, computer science and AI through academic discussions and interactive learning that encourage computational thinking, creativity and independent problem-solving.

Discover AI & Machine Learning

Explore how intelligent systems work, how machine learning identifies patterns and how AI technologies are being applied across different fields.

Data-Driven Thinking & Problem-Solving

Discover how data is analysed to uncover patterns, support research and inform decisions, developing analytical reasoning and computational thinking skills.

Emerging Technologies & Scientific Innovation

Explore how advances in AI and data-driven technologies contribute to scientific discoveries, biomedical research and technological developments shaping our future.

Learn from Cambridge Academics & Industry Experts

Gain insights from qualified academics, researchers and industry professionals, connecting emerging technologies and scientific research with real-world applications.

Cultural Immersion

Experience the cultural richness of Cambridge through historic college life, iconic landmarks, punting on the River Cam, and a full-day excursion to "The Other Place"

Certified Achievement

Certificates upon successful programme completion

Programme Sample Schedule

CAMBRIDGE IS CLOSER THAN YOU THINK!

Day #

Breakfast in College

09.30-12.15

Final Group Project Presentations

12.15-14.00

Certificate Ceremony & Traditional Afternoon Tea

14.00-16.45

Free time and shopping in Cambridge

19.00-21.00

Final Formal Dinner in College

Prof. Pietro Liò

AI & Computational Biology Pioneer

Cambridge Professor using AI for precision medicine, Graph Neural Networks, and medical digital twins. Fellow at Clare Hall.

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Professor Pietro Liò is a Full Professor at the University of Cambridge, specialising in Computational Biology and a member of the Artificial Intelligence group. He holds an MA from Cambridge, a PhD in Complex Systems and Non-Linear Dynamics from the University of Firenze, and a PhD in Theoretical Genetics from the University of Pavia.

His research focuses on using AI and machine learning to develop models that integrate multi-scale biological and clinical data to address personalised and precision medicine. He is particularly interested in Graph Neural Networks, AI-based medical digital twins, and decision support systems.

He is a Fellow at Clare Hall College, a member of the Cambridge Centre for AI in Medicine, ELLIS, and the Academia Europaea. He also teaches Geometric Deep Learning and serves on several key committees, including the MPhil in Computational Biology programme.

Dr Tom Crawford

Mathematician | University of Cambridge & University of Oxford

Dr Tom Crawford is an award-winning mathematician, educator and science communicator holding academic positions at both Oxford and Cambridge. He is also the creator of Tom Rocks Maths, a popular educational platform making mathematics engaging and accessible to audiences worldwide.

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Dr Tom Crawford is a mathematician, educator and award-winning science communicator, holding academic positions at both the University of Cambridge and the University of Oxford.

At Cambridge, he is a Lecturer in Applied Mathematics at Robinson College. At Oxford, he is a Fellow by Special Election at St Edmund Hall and Public Engagement Lead at the Department for Continuing Education.

He is also the creator of Tom Rocks Maths, an award-winning educational platform dedicated to making mathematics engaging and accessible to wider audiences. His work has attracted over 25 million YouTube views, and he was recognised as YouTube's Creator on the Rise in 2020.

Cheng-Yu (Kou) Huang

Computational Neuroscience

PhD candidate in the Department of Physiology, Development, and Neuroscience at the University of Cambridge

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Cheng-Yu (Kou) Huang is a PhD candidate in the Department of Physiology, Development, and Neuroscience at the University of Cambridge. His research interests lie in efficiently processing and interpreting multivariate biological data. More specifically, his current research in computational neuroscience focuses on understanding how spatial representations of the external world in the brain degrade in dementia and how these changes contribute to difficulties in spatial navigation. Beyond his primary research, Cheng-Yu is dedicated to scientific outreach and education. With a background in physics and biology, he frequently teaches data science and image analysis to biological researchers at institutes across Japan and Taiwan.

Alexander Zheltov

Education, Artificial Intelligence, Business

CEO, Flylane. Forbes 30 Under 30, with a track record of scaling education to 20,000+ learners across 30 countries.

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A Forbes 30 Under 30 leader, Alexander has scaled education to more than 20,000 learners across 30 countries, raising over $20M to advance Flylane’s mission of transforming AI-enhanced learning.

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Matthew Ashman

Machine Learning

Matthew Ashman is a PhD candidate in the Machine Learning Group at the University of Cambridge. His research interests lie within probabilistic machine learning, and are are motivated by the need to improve decision making and prediction in the presence of uncertainty.

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Matthew Ashman is a PhD candidate in the Machine Learning Group at the University of Cambridge, where he is supported by the George and Lilian Schiff Foundation. His research interests lie within probabilistic machine learning, and are are motivated by the need to improve decision making and prediction in the presence of uncertainty. He has spent time working at Microsoft Research and the Australian National University. Prior to his PhD, Matthew received a MPhil in Machine Learning  and Machine Intelligence and a MEng  in Computer Engineering, both from the University of Cambridge.