Big Data: Mathematical Modelling - Queensland University of Technology

Have you ever wondered how mathematics can be used to solve big data problems? This course will show you how.

Mathematics is everywhere, and with the rise of big data it becomes a useful tool when extracting information and analysing large datasets.

We will begin by explaining how maths underpins many of the tools that are used to manage and analyse big data. We will show you how very different applied problems can have common mathematical aims, and therefore can be addressed using similar mathematical tools.

We will then proceed to introduce three such tools, based on a linear algebra framework. These tools and the problems that they address are:

1. eigenvalues and eigenvectors for ranking

1. graph Laplacian for clustering

1. singular value decomposition for data compression.

We will show you how these methods can be applied to a variety of case studies, including ranking websites, profiling leukaemia patients, taking selfies, and so on.

Our hands-on approach will allow you to develop your analytic skills using self-contained datasets, deepen your understanding of the underlying mathematical methods, and explore how these methods can be applied to big data in your area.

This is the third in a series of four short courses from the ARC Centre of Excellence for Mathematical and Statistical Frontiers at Queensland University of Technology (QUT).

You can also join the other three courses in the series:

• From Data to Decisions

• Statistical Inference and Machine Learning

• Data Visualisation