Big Data: Mathematical Modelling

Learning Outcome 1

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.