Teaching & Learning

Award-winning Cambridge professors • Cambridge-style small-group teaching • Evidence-based methods • Learn-by-doing

Our teaching is built around how students actually learn best: by doing, reflecting, and applying ideas to real problems. We combine Cambridge-style small-group teaching with live coding demos, hands-on practice, and mentored team projects using real data. Concepts are introduced clearly, practised step by step, and reinforced through immediate feedback and application. The result is deep understanding, lasting confidence, and skills you can use well beyond the classroom.

Welcome message

Choosing a summer school is a big decision. If you’re asking, “Why this programme?”, my honest answer is: because of the people behind it and how we teach. We’re a close-knit team of Cambridge educators who’ve spent years developing innovative, award-winning methods that help our own students thrive. Our mission now is simple: to share that experience with you.

You’ll learn by doing: small-group teaching, real data, project-centred work, daily coaching and rapid feedback that turn concepts into confidence. We’ll meet you where you are and guide you to where you want to be.

By the end, you’ll leave with a certificate and references, a portfolio-ready project, mastery and confidence with applied tools, clearer goals, admissions know-how, practical leadership and teamwork skills, and a global network.

We can’t wait to welcome you and stand beside you as you build your future.

Oleg Kitov
Associate Professor, University of Cambridge
Academic Director, CUNJC Summer School

Programme faculty

Roman Berlanger
Teaching Associate
Complexity economics
Quantitative finance

Daniele Cassese
Assistant Professor
Network economics
Microeconomics

MuynGun Kim
Assistant Professor
Macroeconomics
Econometrics

Oleg Kitov
Associate Professor
Machine learning
Artificial intelligence

Vasileios Kotsidis
Associate Professor
Maths methods
Stats methods

Dmitrii Petrukhin
Teaching Associate
Econometrics
Causal data science

Ruohan Qin
Assistant Professor
Microeconomics
Network economics

Weilong Zhang
Associate Professor
Causal data science
Financial markets

Our instructors are recognised by global teaching awards and nominations

Our teaching philosophy

Cambridge-style teaching in small groups with award-winning academics. You’ll move from lectures and demos to hands-on Python and R with real datasets, going from example code to writing your own, with immediate feedback that sharpens skills. This evidence-based approach builds fluency and confidence, leaving you with industry-ready methods to apply and showcase — a foundation that strengthens your CV and supports future study, research and careers.

Evidence-based

We use research-backed methods that boost your outcomes and confidence.

Student-focused

Designed around your goals, interests and pace, maximising progress each week.

Active practice

Practise daily with real tasks and personalised feedback to master skills.

Gradual release

We model and guide; you practise independently until techniques feel natural.

Project-based

Work with real data to create portfolio-ready projects recruiters remember.

Collaborative work

Team up with peers, building communication, leadership and problem-solving.

Lifelong growth

Leave with tools, mindset and confidence to keep learning and progressing.