Data & Business Analytics Concentration

The module provides students with a sound foundation in the application of the many tools and techniques of management science. Students are expected to learn the tools and the applications of modelling, optimisation, computing and programming in solving practical problems drawn from different functional areas (operations, finance, marketing, and human resources, etc.) in different organisations. 

This module introduces computer simulation as a powerful (yet intuitive) method for modelling complex business environments, analysing their behaviour, and predicting the effects of managerial strategies. Students will learn how to develop representative models, analyse and provide data to adequately parameterise and validate their models, conduct sensitivity analyses, and interpret and communicate results, acquiring the knowledge and tools to conduct simulation-based projects in managerial practice. 

This module is a hands-on introduction to state-of-the-art data analytics and machine learning methods. The course covers both supervised learning algorithms (used to make accurate predictions about the future from current data) and unsupervised learning (used to discover unknown structures in given data). Students will understand how to use those algorithms in different business contexts, and how to obtain managerially relevant insights from their analyses. 

This module bridges data science techniques with managerial decision-making, by leveraging the powerful combination of data, visuals, and narratives. The module covers basic data visualisation techniques for data of different volume (small and big data), variety (structured and unstructured), and velocity. The module also covers the psychological foundations and principles of data visualisation – how does the human brain process information, and how can we manage the cognitive load and attention of the audience? Students will learn to build compelling stories around their data analysis, in order to effectively communicate their results to decision-makers in important business contexts. 

This hands-on module highlights the crucial role that well-designed business experiments can play in informing important managerial decisions in diverse areas such as pricing, new product development, operations management, and organisational incentive setting. A growing number of organisations recognize the power of experiments, but often fail in the execution phase; for instance, because they are using unsystematic trial-and-error approaches that are not geared towards solid causal inference and learning from robust empirical evidence. The module covers the scientific foundations of experimental design & analysis, including advanced topics such as quasi-experimental designs, natural experiments, and regression discontinuity designs. The module further teaches students to understand and solve practical implementation challenges of business experiments. 

The Applied Analytics Challenge presents students with a challenging real-world (big data) business analytics problem. The students will work on solving the problem by using the data analysis techniques that they have learned in previous courses. The course exposes students to the nature of big data projects and will teach them how to address challenging issues in management practice. It also introduces students to the issue of how to manage such projects.