Learn how to apply selected mathematical modelling methods to analyse big data in this free online course. Mathematics is everywhere, and with the rise of big data it becomes a useful tool when extracting information and analysing large datasets.
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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.
What topics will you cover?
- Introduction to key mathematical concepts in big data analytics: eigenvalues and eigenvectors, principal component analysis (PCA), the graph Laplacian, and singular value decomposition (SVD)
- Application of eigenvalues and eigenvectors to investigate prototypical problems of ranking big data
- Application of the graph Laplacian to investigate prototypical problems of clustering big data
- Application of PCA and SVD to investigate prototypical problems of big data compression
What will you achieve?
By the end of the course, you'll be able to...
- Identify big data application areas
- Explore big data frameworks
- Model and analyse data by applying selected techniques
- Demonstrate an integrated approach to big data
- Develop an awareness of how to participate effectively in a team working with big data experts
