Big Data: Mathematical Modelling

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. Learn how maths underpins big data analysis 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: eigenvalues and eigenvectors for ranking graph Laplacian for clustering singular value decomposition for data compression. Develop your analysis skills with big data case studies 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. More courses in the Big Data series 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