CUNJC Cambridge
Summer School
AI • Machine Learning • Data Science • Economics • Finance
Join our academic summer school in Cambridge for ambitious
students and early-career professionals. It’s an official
programme of a University of Cambridge Centre, designed
and delivered by award-winning Cambridge academics. Choose
from twelve courses across AI, data science, economics and
finance. Learn in small groups, work with real data, and
build portfolio-ready team projects with close support.
Live and learn at Selwyn College, grow your leadership,
and get admissions and career guidance, leaving with
in-demand skills, strong references and a global
network.
Webinar 10 March 11:00 GMT
Join our Academic Director live to find out more about our programme. Ask Questions. Get Answers.
Register nowMake our programme the highlight on your résumé this summer!
Official programme
Award-winning faculty
Cambridge teaching
12 academic courses
Team projects
Applied data skills
Who should apply
Cambridge experience
Recognition & outcomes
Key dates in 2026
31 March
Early-bird application deadline14 June
Final application deadline20–26 July
Online preparation course1–16 August
Academic Session One16–30 August
Academic Session TwoOur teaching is recognised by global awards and nominations
Four pillars of the CUNJC Cambridge Summer School
Academic programme
‣ Learn from award-winning faculty
‣ Cambridge teaching style & standards
‣ Pick from 12 courses on AI, ML, Econ
‣ Learn core idea & tool demo in lecture
‣ Practise tools on real data in class
‣ Achievement certificate & references
‣ Deeper understanding & retention
‣ Daily progress with live coaching
‣ Mastery of analytical methods
‣ Independent problem-solving
Hackathon projects
‣ Work in small teams + mentor
‣ Apply classroom tools to real problem
‣ Analyse real-world data in Python/R
‣ Build shareable output
‣ Present findings at the showcase
‣ Hands-on coding/data experience
‣ Teamwork and project skills
‣ Confidence using applied data tools
‣ A portfolio-ready finished project
‣ Feedback from expert instructors
Professional development
‣ Keynotes by renowned academics
‣ Career panels with top executives
‣ Leadership coaching sessions
‣ Cambridge-led admission workshops
‣ Networking events with speakers
‣ Actionable career goals
‣ Insider admissions know-how
‣ Practical leadership toolkit
‣ References & recommendations
‣ Professional & academic network
Social programme
‣ Experience Cambridge student life
‣ Live & learn at Selwyn College
‣ Attend formal hall dinners
‣ Join guided city tours & punting
‣ Take a memorable trip to London
‣ Strong sense of belonging
‣ Deeper cultural fluency
‣ Lasting friendships worldwide
‣ International mentor network
‣ Unforgettable Cambridge memories
Academic programme courses
Academic programme courses
We run two sessions in Cambridge: Session One, 1–16 August,
and Session Two, 16–30 August. In each session we offer all 12
of our courses. You can join us for one or both sessions,
choosing one course per session. Every course runs for two
weeks and combines lectures, small-group classes and team
projects.
Data Science & AI
Mathematical methods
Calculus, linear algebra, and optimisation as modelling tools in economics and data science.
Calculus, linear algebra, and optimisation as modelling tools in economics and data science.
Statistical methods
Probability and statistics for empirical analysis, including estimation and hypothesis testing.
Probability and statistics for empirical analysis, including estimation and hypothesis testing.
Causal data science
RCTs, IVs, DiD and quasi-experiments for causal inference and treatment effects estimation.
RCTs, IVs, DiD and quasi-experiments for causal inference and treatment effects estimation.
Econometrics
Regression analysis and maximum likelihood to estimate statistical models and test theories.
Regression analysis and maximum likelihood to estimate statistical models and test theories.
Machine learning
Supervised and unsupervised learning to train and evaluate prediction & classification models.
Supervised and unsupervised learning to train and evaluate prediction & classification models.
Artificial intelligence
Reasoning, natural language processing, and reinforcement learning for intelligent systems.
Reasoning, natural language processing, and reinforcement learning for intelligent systems.
Economics & Finance
Microeconomics
Game theory, decision-making and information to analyse strategies and incentives.
Game theory, decision-making and information to analyse strategies and incentives.
Macroeconomics
Growth, business cycles, and policy to study how economies evolve and are managed.
Growth, business cycles, and policy to study how economies evolve and are managed.
Network economics
Network structure and diffusion processes to model systemic connections and spillovers.
Network structure and diffusion processes to model systemic connections and spillovers.
Complexity economics
Equilibrium, emergence, and agent-based models to understand systemic risk and crises.
Equilibrium, emergence, and agent-based models to understand systemic risk and crises.
Quantitative finance
Time-series modelling and derivatives pricing to analyse and forecast financial market behaviour.
Time-series modelling and derivatives pricing to analyse and forecast financial market behaviour.
Financial markets
Asset pricing theory and behavioural biases to understand what drives risk/returns & bubbles.
Asset pricing theory and behavioural biases to understand what drives risk/returns & bubbles.
Programme brochure
Download the full brochure to discover how our summer school can transform your academic and career trajectory.
