Have you ever wondered how mathematics can be used to solve big data problems? .
Mathematics is everywhere, and with the rise of big data it becomes a useful tool when extracting information and analyzing large datasets. The free online course “Big Data: Mathematical Modeling” will show you how maths underpins big data analysis.
Big Data: Mathematical Modeling Course Content
Learn how to apply selected mathematical modeling methods to analyse big data in this course. It includes the following topics:
- How maths underpins many of the tools that are used to manage and analyze big data;
- How very different applied problems can have common mathematical aims, and therefore can be addressed using similar mathematical tools;
- Three tools based on a linear algebra framework:
- eigenvalues and eigenvectors for ranking;
- graph Laplacian for clustering;
- singular value decomposition for data compression.
- Develop your analysis skills with big data case studies;
- How these methods can be applied to a variety of case studies, including ranking websites, profiling leukaemia patients, taking selfies, and so on.
The course’s hands-on approach will allow you to develop your analytic skills using self-contained datasets, and explore how these methods can be applied to big data in your area.
- You can learn about the modeling methods in this course even if you don’t have a strong maths background.
- The course assumes 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.
Summary of Main Course Features
- Educators: Ian Turner, Steven Psaltis, Phil Gough, Miles McBain, Samuel Rathmanner and Matthew Sutton;
- In association with: ARC Centre of Excellence for Mathematical and Statistical Frontiers;
- Content Contributors: Kevin Burrage, Giuseppe De Martino, Steve Psaltis and Ian Turner;
- Duration: 2 weeks x 2 hours per week;
- Starts: 30 May;
- Certificates available.