Apply Mathematical Modeling Methods to Analyze Big Data - Free Course

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.

Requirements

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.