While the idea of using data to improve decision making is by no means new, various methodological advances and a proliferation of "big data" has sparked a quickly growing appreciation and practice of business analytics. However, the growing importance of data-driven and evidence-based decision making is not confined to tech giants like Amazon, Apple, Facebook, and Google. Companies across all industries, and of all sizes, have begun to build data-driven business models, resulting in a rising need to hire managers that combine business domain knowledge with a deep understanding of the methodological toolbox that can unlock the value inherent in data. This is the essential premise of the Master in Management Data & Business Analytics concentration.
Optimisation & Decision Models
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.
Business Simulation & Algorithms
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.
Machine Learning for Big Data
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.
Data Visualisation & Storytelling
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.
Designing & Analysing Business Experiments
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.
Applied Analytics Challenge
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.
In order to unlock the value inherent in data, companies need managers who combine business domain knowledge with solid methodological skills. This is the essential premise of the Data & Business Analytics concentration, which equips students with a state-of-the-art toolkit to solve business problems with real-world data.
Read more insights from Mirko Kremer, Professor of Supply Chain Management, and Jochen Schlapp, NORMA Group Associate Professor of Operations and Technology Management, on the blog.