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Big Data: Mathematical Modelling

Queensland University of Technology via FutureLearn

This course may be unavailable.


Learn how mathematics underpins big data analysis and develop your skills.

Mathematics is everywhere, and with the rise of big data it becomes a useful tool when extracting information and analysing large datasets. We begin by explaining how maths underpins many of the tools that are used to manage and analyse big data. We show how very different applied problems can have common mathematical aims, and therefore can be addressed using similar mathematical tools. We then introduce three such tools, based on a linear algebra framework: eigenvalues and eigenvectors for ranking; graph Laplacian for clustering; and singular value decomposition for data compression.

This course is designed for anyone looking to add mathematical methods for data analytics to their skill set. We provide a multi-layered approach, so you can learn about the methods even if you don’t have a strong maths background, but we provide further information for those with a sound knowledge of undergraduate mathematics. We will assume basic MATLAB (or other) programming skills for some of the practical exercises.

MathWorks will provide you with free access to MATLAB Online for the duration of the course so you can complete the programming exercises. Please visit MATLAB Online to ensure your system meets the minimum requirements.

Taught by

Tomasz Bednarz, Ian Turner and Kerrie Mengersen


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