You will be taught exclusively by established academics and professors from the University of Cambridge. Our instructors are among the best lecturers at Cambridge and hold multiple awards for teaching excellence. Our teaching approach is entirely student-focused and evidence-based. We use most advanced and innovative learning methodologies, which stimulate deep learning and maximize long-term retention.
Our Programme offers a portfolio of eight specialized modules - two in each of four key disciplines: Microeconomics, Macroeconomics, Data Science, and Finance. Each module covers modern topics in Economics and Data Science and caters to diverse interests. Customise your academic journey by choosing any two modules to suit your career goals or intellectual passions.
You will learn by doing. Our course material is motivated by most recent academic and industry developments. Our instructors will carefully guide you through contemporary real-world empirical examples.You will learn to work with a wide range of data sets and to use theoretical models in practice through first-hand experience of problem solving and data analysis.
Choose two courses: one from Module (A) list and one from Module (B) list. This ensures that you can explore topics that align with your academic and career interests while balancing foundational and specialised learning.
| Stream |
Module (A) |
Module (B) |
| Macroeconomics |
Game Theory |
Economics of Networks |
| Macroeconomics |
Macroeconomic Theory |
Monetary Policy |
| Data Science |
Causal Inference |
Machine Learning |
| Finance |
Financial Econometrics |
Behavioural Finance |
Microeconomics
Microeconomics A: Game Theory
Instructor: Ruohan Qin
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Microeconomics B: Economics of Networks
Instructor: Ruohan Qin
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This is an introductory course in game theory - the mathematical study of strategic interactions among rational decision makers. The course aims to equip you with a deep understanding of game theory’s fundamental principles, methodologies, and its applications in analysing real-world phenomena. Through a series of lectures and seminars, you will explore the theoretical foundations of game theory, including equilibrium concepts, strategic dominance, and the impacts of dynamic interactions and information incompleteness. You will progress from understanding basic concepts and models to exploring the strategic complexities of dynamic games and the uncertainties inherent in real-world interactions.
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The decisions we make are deeply influenced by the connections we maintain. These connections, whether in friendships, online platforms, supply chains, or transportation systems, are the foundation of the networks that underpin modern life. This course offers a rigorous introduction to the economics of networks, starting from foundational models and progressing to advanced models and cutting-edge research. You will explore how networks form, evolve, and influence economic and social outcomes, with applications in production and supply chains, infrastructure, social behaviour, epidemics, and networked markets.
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Macroeconomics
Macroeconomics A: Macroeconomic Theory
Instructor: Myungun Kim
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Macroeconomics B: Monetary Policy
Instructor: Myungun Kim
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This course offers a comprehensive overview of macroeconomics, covering topics from short-run dynamics to long-run growth. Key themes include economic growth, the IS-LM framework, aggregate demand components (consumption, investment, government spending), and monetary and fiscal policy. Each lecture connects these concepts to compelling questions, such as why some countries grow richer while others falter, whether inflation can arise from expectations alone, and the limits of government debt. You will examine case studies, such as the US Federal Reserve’s recent monetary tightening to combat inflation and the UK’s fiscal responses to post-pandemic economic recovery. Designed for students transitioning from beginner to intermediate levels, the course emphasises applying macroeconomic principles to analyse contemporary challenges.
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This course provides an in-depth exploration of monetary policy frameworks, focusing on theoretical foundations and contemporary applications across major economies, including the US, UK, Europe, and key Asian economies. It examines the evolution of monetary policy from traditional interest rate rules to unconventional tools such as forward guidance and quantitative easing. The course integrates modern analytical techniques, including high-frequency data analysis and natural language processing (NLP), to assess central bank communications and policy effectiveness.
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Data Science
Data Science A: Casual Inference
Instructor: Weilong Zhang
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Data Science B: Machine Learning
Instructor: Oleg Kitov
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This course focuses on identifying and estimating causal effects in economics and social sciences, covering randomised controlled trials, instrumental variables, difference-in-differences, fixed effects, and regression discontinuity designs. Topics include the impact of minimum wage on employment, long-term outcomes of education policies, and vaccine effectiveness. You will explore innovative tools like ChatGPT, applying AI to analyse public responses using sentiment data. Emphasizing intuition and practical application, the course bridges cutting-edge methodologies with real-world policy evaluation, equipping you with skills to interpret and address complex causal questions in contemporary economic and social contexts.
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This course introduces modern methods of supervised machine learning. It will consider data analysis models that can substantially improve prediction accuracy and interpretability compared with simple linear models, especially when faced with large datasets. Specifically, it will cover selection and shrinkage models such as Lasso, Ridge, Principal Component Analysis and Factor Models. General principles of model assessment and selection will also be discussed. The course is centred around concrete modelling applications using economic and financial datasets in Python, as you will learn valuable skills of applied data science and machine learning.
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Finance
Finance A: Financial Econometrics
Instructor: Oleg Kitov
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Finance B: Behavioural Finance
Instructor: Weilong Zhang
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This course focuses on identifying and estimating causal effects in economics and social sciences, covering randomised controlled trials, instrumental variables, difference-in-differences, fixed effects, and regression discontinuity designs. Topics include the impact of minimum wage on employment, long-term outcomes of education policies, and vaccine effectiveness. You will explore innovative tools like ChatGPT, applying AI to analyse public responses using sentiment data. Emphasizing intuition and practical application, the course bridges cutting-edge methodologies with real-world policy evaluation, equipping you with skills to interpret and address complex causal questions in contemporary economic and social contexts.
|
This course introduces modern methods of supervised machine learning. It will consider data analysis models that can substantially improve prediction accuracy and interpretability compared with simple linear models, especially when faced with large datasets. Specifically, it will cover selection and shrinkage models such as Lasso, Ridge, Principal Component Analysis and Factor Models. General principles of model assessment and selection will also be discussed. The course is centred around concrete modelling applications using economic and financial datasets in Python, as you will learn valuable skills of applied data science and machine learning.
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中文
English