CUNJC Cambridge
Summer School Programme

Academic programme • Team projects • Professional development

Focused on applied quantitative fields that power today's data-driven economy — data science, AI, economics and finance — our Academic Programme connects theory with practice from day one. A one-week online prep course (maths, stats and coding basics) precedes arrival. Each two-week course combines daily lectures and practical Python/R classes with team projects, producing skills directly transferable to postgraduate study and data-intensive roles.

Programme overview

Programme Dates 2026

Course	Location	Dates
Prep course	Online	20–26 July
Session one	Cambridge	1–16 August
Session two	Cambridge	16–30 August

Sessions & Courses

Choose to attend one or two sessions. Choose one of twelve courses per session. One course lasts for two weeks, and contains one module and one related team project per week. Total teaching is Lectures (15h) → Classes (15h) → Project (30h).

Equivalence & Class size

Each course is equivalent in the amount of material to a full-term Cambridge undergraduate course. Maximum 20 students per course in lectures and classes. Hackathon team projects in groups of 4-5 students.

Recognition & Credit

Receive 3-4 credits (US) / 7.5 ECTS (EU), subject to approval from your university. Receive an official Certificate of Achievement from the Cambridge University - Nanjing Centre. Recommendation letter available for top performing students.

Prerequisites & Preparation

Basic understanding of quantitative methods, including calculus and probability, is essential. Our Online Prep Course will cover core maths, stats and coding (Python/R) concepts you will need to follow the courses.

Summer School Timetable

Applications are now open for Summer 2026. Secure your place at Cambridge Summer School

Structure & timetable

9-12am: Academic Programme

Award-winning instructors deliver lectures with short concept bursts and live demos of empirical data tools in Python/R, then you apply them to real datasets in hands-on classes with our guidance.

2-5pm: Hackathon Projects

You carry that momentum into mentored team projects, applying class tools to real problems—forecasting, causal inference, portfolio optimisation, text mining—to build a portfolio-ready project, and present it at the end of the week.

5-6pm: Professional Development

We host keynote speakers, career panels by business leaders in AI and finance, leadership coaching, and postgraduate admissions workshops so you can grow your academic and professional network and strengthen future opportunities.

6-8pm: Social Programme

We curate speaker receptions, informal networking, and Selwyn College formal hall dinners so you can unwind, build relationships, and enjoy Cambridge—city and college tours, punting, museums, and more.

Summer School Timetable

Applications are now open for Summer 2026. Secure your place at Cambridge Summer School

Download full timetable

Summer School Timetable

Applications are now open for Summer 2026. Secure your place at Cambridge Summer School

Academic programme

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 courses

Learn the quantitative tools — from maths and stats to ML and AI — that power modern analysis and research.

Key idea • Which mathematical tools power modern analysis in data, markets, and policy?

Week 1: Calculus & Linear Algebra

We introduce functions, differentiation, integration, vectors, and matrices as the essential building blocks of quantitative analysis. You will develop the fluency to apply these tools across economics, finance, and data science, from modelling consumer choice to solving portfolio allocation problems.

Sample project: Use matrices to build a multi-factor equity risk model

Week 2: Optimisation in Theory & Practice

We cover unconstrained optimisation and the Lagrangian method for constrained problems, with a focus on economic and financial applications. You will learn how optimisation underpins complex models, from economic dynamics and growth theory to pricing and risk management in financial markets.

Sample project: Optimise a global e-commerce warehouse and delivery network

Key idea • How can we describe, quantify, and simulate uncertainty?

Week 1: Probability Theory

We introduce the core concepts of probability, including random variables, distributions, expectation, and variance, as the mathematical foundations of uncertainty. You will learn how to calculate and interpret probabilities in real-world contexts, from lotteries and insurance to economic risk assessment.

Sample project: Quantify climate-catastrophe risk for an insurance portfolio.

Week 2: Statistical Inference

We cover the principles of statistical reasoning, including estimation, confidence intervals, and hypothesis testing. You will gain the skills to analyse data and make valid inferences, with applications ranging from economic theory testing to policy evaluation to financial forecasting.

Sample project: Test whether ESG funds outperform conventional funds using returns data.

Key idea • How do we estimate effects and make credible causal claims from real-world data?

Week 1: Potential Outcomes & RCT

We introduce the potential outcomes framework and the design of randomised controlled trials as the foundation for modern causal inference. You will learn how to evaluate the impact of policies and interventions, with applications ranging from healthcare and education to development economics.

Sample project: Design an RCT to test pension auto-enrolment savings nudges.

Week 2: Causal Inference & Identification

We cover advanced strategies for identifying causal effects, including instrumental variables, difference-in-differences, and regression discontinuity designs. You will gain the skills to apply these methods to real-world settings, from assessing the returns to education to evaluating the effectiveness of new regulations.

Sample project: Estimate employment effects of minimum-wage hikes in hospitality.

Key idea • How do we test economic theories and measure relationships using real-world data?

Week 1: Linear Regression & OLS Estimation

We introduce the classical econometric framework, focusing on estimation, key assumptions, and hypothesis testing. You will develop practical skills in applying regression models to economic data, with applications including wage gap analysis, demand estimation, and macroeconomic forecasting.

Sample project: Measure returns to education using micro-data on wages and schooling.

Week 2: Maximum Likelihood Estimation

We explore maximum likelihood (MLE) as a general approach to statistical estimation beyond the classical regression model. You will practice applying MLE to economic and financial data, with applications ranging from modelling consumer demand to estimating credit risk.

Sample project: Build an MLE model for credit-card default risk.

Key idea • How can machines learn patterns from data to predict outcomes and inform decisions?

Week 1: Supervised Learning

We introduce predictive modelling with regression and classification methods for labelled data. You will learn to train, validate, and evaluate models, with applications such as credit risk scoring, predicting loan defaults, and forecasting consumer demand.

Sample project: Predict which early-stage startups become unicorns from firm metrics.

Week 2: Unsupervised Learning

We cover techniques for discovering hidden structures in data, including clustering and dimensionality reduction. You will apply these methods to economic and financial problems such as market segmentation, detecting fraud or anomalies in transactions, and analysing high-dimensional investment data.

Sample project: Cluster retail-bank customers to design targeted financial products.

Key idea • How can we build systems that reason, perceive, and learn to solve real-world problems?

Week 1: Search & Reasoning

We introduce the core concepts of AI, including search algorithms, problem-solving, and knowledge representation. You will learn logic-based methods that underpin early AI, with applications to auction design, supply chain optimisation, and algorithmic trading.

Sample project: Optimise bidding strategies in online advertising auctions with AI search.

Week 2: NLP & Reinforcement Learning

We cover key areas of modern AI, including natural language processing and reinforcement learning. You will explore applications such as analysing central bank communications, building financial sentiment indicators, and designing adaptive trading algorithms, while also reflecting on ethical implications.

Sample project: Train an RL trading agent using news and central-bank NLP signals.

Economics & Finance courses

Apply models and data-driven methods to understand markets, economies, networks, and systemic risks.

Key idea • How do individuals and firms make choices when facing strategic rivals and uncertainty?

Week 1: Game Theory

We introduce the principles of game theory, including Nash equilibrium, strategic dominance, and repeated games. You will learn to analyse competitive and cooperative situations in markets and policy, with applications to auctions, pricing strategies, and international negotiations.

Sample project: Model competition in the streaming-platform “subscription wars” with game theory.

Week 2: Decision Theory

We cover decision-making under uncertainty using expected utility theory, risk preferences, and the economics of information. You will understand how asymmetric information shapes outcomes in markets such as insurance, banking, and investment.

Sample project: Optimise household insurance decisions under climate and disaster risk.

Key idea • How do economies grow, fluctuate, and react to policy interventions?

Week 1: Macroeconomics Before AI

We provide an overview of traditional macroeconomic theory, from growth to business cycles. You will apply these frameworks to real-world debates including recessions, inflation surges, productivity trends, and the effect of monetary and fiscal policy on economic outcomes.

Sample project: Build an early-warning index for emerging-market currency crises.

Week 2: Macroeconomics After AI

We explore how AI is reshaping macroeconomic realities and research. You will learn about the consequences of AI, including job displacement, shifts in productivity, and new growth dynamics; and about AI-driven research approaches, including natural language processing and predictive analytics.

Sample project: Simulate how generative AI reshapes labour markets across occupations.

Key idea • How do connections and network structure shape economic and social outcomes?

Week 1: Networks & Connections

We introduce the foundations of network theory, focusing on how structures form in social, economic, and financial contexts. You will learn to model and interpret the role of connections in shaping outcomes such as international trade flows, interbank lending, and supply chain resilience.

Sample project: Map key nodes in the global semiconductor supply chain network.

Week 2: Contagion & Market Dynamics

We cover how networks influence dynamic processes like the spread of shocks, innovations, and information. You will analyse how network position shapes market power, diffusion, and inequality, with applications including financial contagion, viral marketing, and systemic risk in banking.

Sample project: Stress-test an interbank network for systemic contagion after a bank failure.

Key idea • What emergent behaviours arise when many adaptive agents interact in markets?

Week 1: General Equilibrium & Market Efficiency

We introduce general equilibrium theory as a framework for analysing interconnected markets and their efficiency properties. You will assess when theoretical assumptions hold and when frictions lead to failures, with applications to global supply shocks, policy interventions, and resource allocation.

Sample project: Trace effects of a major shipping blockage on global prices and trade.

Week 2: Complex Systems & Dynamics

We explore economics as a complex adaptive system, emphasising bounded rationality, feedback loops, and emergent behaviour. You will apply complexity thinking to real-world challenges such as financial crises, asset bubbles, and systemic risk in global markets.

Sample project: Simulate crypto-asset bubbles and crashes with an agent-based market model.

Key idea • How can data and models help us understand, forecast, and trade financial markets?

Week 1: Financial Data Analytics

We introduce econometric tools for financial time series, including ARMA models, cointegration, volatility dynamics, and event studies. You will apply these methods to stocks, cryptocurrencies, and trading strategies in Python, gaining skills essential for analysts, traders, and policy makers.

Sample project: Forecast volatility in Bitcoin and equity indices with time-series models.

Week 2: Options & Derivatives Pricing

We cover the valuation of derivatives such as options and futures using arbitrage arguments, binomial trees, and the Black–Scholes model. You will learn how derivatives are used for hedging, speculation, and risk management in real-world financial markets.

Sample project: Price and hedge options on tech stocks in a simulated market.

Key idea • How do human psychology and incentives drive asset prices and market anomalies?

Week 1: Asset Pricing

We introduce modern asset pricing theory, including portfolio optimisation, the efficiency frontier, CAPM, and multi-factor models. You will learn how to link risk and return using these frameworks, which underpin investment decisions in global financial markets.

Sample project: Test CAPM and factor models on ESG versus non-ESG portfolios.

Week 2: Behavioural Finance

We examine how psychological biases and heuristics shape financial decision-making and market outcomes. You will apply behavioural insights and AI tools to case studies such as the GameStop short squeeze, exploring how bubbles, crashes, and anomalies emerge.

Sample project: Quantify the role of Reddit sentiment in driving meme-stock bubbles.

A day in the academic programme

The Academic Programme takes place in the mornings and contains two parts. First, in a 1.5-hour lecture we introduce one core idea and demonstrate a practical tool in Python or R. Then, in a 1.5-hour class, you practise that tool on real structured data with our step-by-step coaching. This daily learn-demo-practise cycle gradually builds mastery, so you can confidently apply each method in your team projects on real questions and new unstructured datasets

Sample day: Causal data science

Here is what you can expect your mornings to look like for the course in Causal Data Science, week 1 module on Causal Inference, discussing the Difference-in-Differences (diff-in-diff) estimation method.

Lecture

Idea

Diff-in-diff compares changes over time between a treatment and a control group

Demo

Use diff-in-diff to estimate whether free school meals improve student test scores

Data

Online UK Education Endowment Foundation (EEF) school trial dataset

Code

Analyse treatment × post-intervention effects using statsmodels in Python

Class

Practice

Use diff-in-diff to assess how Uber affected taxi employment across UK cities

Guidance

Estimate treatment effect, conduct testing, interpret results, discuss

Data

UK Labour Force Survey (LFS) data for taxi drivers before and after Uber entry

Code

Diff-in-diff in Python, pandas for data prep and statsmodels for estimation.

Team projects

One project per week — mentored sprints tackling hot topics in economics, finance, data, and AI. In teams of maximum five people you’ll design your approach, share roles, and code in Python/R to analyse real data using tools from class, turning results into a sharp report and final presentation. Our projects mirror how work gets done outside class — short cycles, quick feedback, clear ownership — so learning sticks and confidence grows. You leave Cambridge with a portfolio-ready project, actionable insights, and proven teamwork and communication — wins you can showcase in CVs, applications, and interviews.

Challenge

Fast and focused: one week, one module, one project.
Applied projects: apply classroom methods and Python/R tools to real data.
Impactful themes: tackle hot topics in economics, finance, data science and AI.

Process

Small teams: work in mixed roles across analysis, coding, writing, and visuals.
Real-world workflow: plan together, split tasks, integrate, and review.
Live coaching: receive daily guidance and feedback from instructors.

Outcome

Shareable output: produce visuals, a slide deck, and a one-page summary.
Showcase finish: deliver a short demo to explain what you built and why it matters.
Portfolio-ready: graduate with a project you can show to employers / grad schools.

Sample project: Machine learning

In this project for the Machine Learning course (Unsupervised Learning module), you’ll work with real crypto market data to spot bubble periods and uncover the patterns behind booms and crashes using clustering and PCA. Guided by instructors and teaching assistants, you’ll build the analysis step by step in Python using tools from lectures and classes.

Title

Clustering cryptocurrency bubbles.

Goal

Detect bubble episodes in crypto markets to uncover patterns in their formation and collapse.

Data

Daily prices, returns, and trading volume for Bitcoin, Ethereum, and major altcoins.  

Method

k-means clustering, PCA, rolling-window volatility measures, anomaly detection.

Code

Python pandas/numpy for data, scikit-learn for clustering, matplotlib / seaborn for visuals.  

Output

A portfolio-ready analysis of crypto bubble dynamics with cluster profiles (demo + report).

Professional development

A distinctive part of our Summer School is a series of talks with global academic leaders, senior finance and AI executives, professional leadership coaches and Cambridge admissions tutors. They offer practical guidance, insider perspectives and inspiration for your academic and professional path. Drinks receptions and dinners with speakers let you build meaningful connections in an informal setting. These conversations often develop into mentoring and networking that support your ambitions, so you leave Cambridge with a network that lasts.

Keynote lectures

Hear Cambridge professors connect economics, finance and AI research to insights you'll apply to global challenges in projects and real life.

Career talks

Gain first-hand career insights from industry leaders in AI , finance, and economics through talks and panels on real-world challenges.

Leadership coaching

Work with professional coaches in interactive workshops to develop your leadership, confidence, resilience and collaboration skills.

Admission workshop

Get insider guidance from Cambridge academics and admissions experts on applications, selection criteria and strategies to secure master’s or PhD places worldwide.

Confirmed speakers

Romans PopovsVP M&A Customers & Products, Trading & Shipping at BP

Ayoub SemaanPartner at Cultivating Leadership, a professional training firm

Maksim SiposCo-founder and CTO at causaLens, a leading AI company

Ready to join us?

Secure your place at the CUNJC Cambridge Summer School 2026.

Apply now

Academic programme courses

Data Science & AI

DS-1: Data Science for Economics and Business
For students with no programming background, this course introduces the core tools of applied econometrics, namely regression, statistical inference, experiments and difference-in-differences, and uses them to separate correlation from causation in real economic and business data.

DS-2: Machine Learning for Prediction
For students who already know linear regression and basic matrix algebra, this course covers the main supervised learning methods, from regularised regression to decision trees and ensembles, together with the validation discipline needed to trust their predictions and turn them into decisions.

AI-1: Applied AI for Business and Management
For students with no programming background, this course covers what large language models and other AI tools can and cannot do, and how organisations test, deploy and govern them, ending with students building and evaluating a quantitative case for using AI in a real business process.

AI-2: Deep Learning and Large Language Models
For students with some calculus, statistics and Python, this course builds neural networks, backpropagation and attention from first principles and ends with students training a small transformer language model and fine-tuning a pretrained one.

Economics & Finance

FI-1: Financial Markets and Investment
For students with no prior background in finance, this course introduces how stock markets price risk, from returns, volatility and diversification to portfolio choice, the CAPM and multi-factor models, and applies these ideas to real stock market data.

FI-1: Quantitative Finance with AI
For students who know linear regression and introductory finance, this course builds and backtests quantitative trading strategies, from factors to volatility modelling, and shows how AI, from machine learning on prices to language models on financial text, can improve return forecasts and portfolio decisions.

EC-1: Data-Driven Macroeconomics
For students with no prior background in economics, this course explains how economies grow, fluctuate and respond to policy, from productivity, technology and the promise of AI to inflation, interest rates and public debt, using real data from economies around the world.

EC-2: The Microeconomics of AI
For students who know linear regression and introductory economics, this course uses microeconomic models and evidence to analyse how AI changes the tasks workers do, the decisions firms make and the way markets compete, from automation and productivity to algorithmic pricing, platforms and regulation.

CUNJC CambridgeSummer School Programme

Academic programme • Team projects • Professional development

Programme overview

Programme Dates 2026

Sessions & Courses

Equivalence & Class size

Recognition & Credit

Prerequisites & Preparation

Summer School Timetable

Structure & timetable

9-12am: Academic Programme

2-5pm: Hackathon Projects

5-6pm: Professional Development

6-8pm: Social Programme

Summer School Timetable

Summer School Timetable

Academic programme

Data Science & AI courses

Economics & Finance courses

A day in the academic programme

Sample day: Causal data science

Lecture

Idea

Demo

Data

Code

Class

Practice

Guidance

Data

Code

Team projects

Challenge

Process

Outcome

Sample project: Machine learning

Title

Goal

Data

Method

Code

Output

Professional development

Keynote lectures

Career talks

Leadership coaching

Admission workshop

Confirmed speakers

Ready to join us?

Academic programme courses

Data Science & AI

Economics & Finance

CUNJC Cambridge
Summer School Programme