The course develops analytical skills for financial decision making using a range of quantitative tools. Students will learn to evaluate asset risk through regression analysis, construct optimal portfolios using optimization techniques, and price a variety of financial options—including American, exotic, and real options—using binomial trees and Monte Carlo simulation. The course covers applications such as portfolio management, capital budgeting, and risk measurement, with a strong emphasis on real-world case studies. By the end of the course, students will be equipped to both build and critically assess financial models.

In this course, students will learn how to structure and communicate their reasoning, perform rigorous analysis, and defend it to an adversarial challenge. Students will understand and make use of various sources of data, organise the inputs of experts and colleagues, and use R/RStudio to provide analytical support to our reasoning. The overall objective of this course is to equip students with analytical thinking and techniques that help them be more effective in these tasks. The goal is to learn how to perform data analysis to support decision-making, build simple but powerful models that test your intuitive reasoning, develop managerial thinking and facilitate the communication of recommendations.

The course combines lectures, breakout sessions, and computer workshops to develop practical skills in decision modelling, risk assessment, and business analytics. Students will use Microsoft Excel alongside industry tools such as @Risk for simulation, Precision Tree for decision analysis, and Solver for optimisation. Emphasising clarity and structured thinking over black-box solutions, the course teaches students to build transparent, robust models that test intuition and support managerial decision making. Through real-life cases and interactive exercises, students will learn to identify where analytics can add the most value and apply tools in a high-impact, fast-turnaround manner.

This course covers the emerging field of data mining and expands and develops the students’ analytical tool kit in analyzing large data sets. Using case studies and hands-on data sets, students will learn advanced data query techniques, data cleaning and organization, explore various machine learning techniques including supervised and unsupervised classification schemes, text classification, clustering techniques as well as predictive analytics.

The core course examines important issues in finance from an empirical perspective. Upon completing the course, students will be able to implement and evaluate portfolios and market efficiency, assess systematic risks of financial strategies, understand various aspects of fixed income securities, and reduce the dimensionality of problems that involve big data.

This course focuses on optimization techniques using Python. Students will learn how to decompose a problem into its objective functions, decisions, and constraints. They will learn how to express an optimization problem algebraically and solve it using Python. They will gain hands-on experience in applying these models to real-world problems in areas such as resource allocation, production planning, logistics, and portfolio optimization. Emphasis will be placed on interpreting results, validating models, and understanding the trade-offs involved in decision making. By the end of the course, students will be equipped to build, solve, and critically evaluate optimization models in a variety of practical contexts.