# Big Data: Mathematical Modelling

Learn how to apply selected mathematical modelling methods to analyse big data in this free online course.

39,119 enrolled on this course

• Duration

3 weeks
• Weekly study

3 hours

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## Learn how mathematics underpins big data analysis and develop your skills.

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

Skip to 0 minutes and 7 seconds Hi everyone and welcome to our Big Data Analytics collection of courses. My name is Kerrie Mengersen. You might be thinking, what is the relationship between mathematics and Big Data?

Skip to 0 minutes and 21 seconds Well, let me paint a picture for you: imagine you’re a marketing analyst for a big company about to launch a brand new product. Your job is to use people’s social media usage to determine the target market for your product and sell it. These days the increase is social media is explosive - every day there are 4.5 billion likes on Facebook, over 500 million tweets on Twitter and more than 75 million people using Instagram. With this much data, your job in making any sense of this information becomes extremely difficult. Enter mathematics! With the right mathematical techniques and tools, your job quickly becomes much easier.

Skip to 1 minute and 8 seconds We can use mathematics to not only effectively store and manage data, but to efficiently analyse it and extract information that is not immediately obvious. In this course we present a numerical linear algebra framework for data analytics. This includes a wide range of mathematical methods for big data analytics such as the singular value decomposition, tensor products, matricization, graph Laplacian and clustering. We show you how to apply these techniques to store, manage and analyse Big Data. Here at ACEMS we use a multi-lensed approach to Big Data analytics and this is reflected in the case studies we’ll share with you. Mathematics is everywhere, and can help you solve lots of different problems.

## 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

## Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

## 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

## Who is the course for?

This course is designed for anyone looking to add mathematical methods for data analytics to their skill set. We provide a multi-layered approach, so you can learn about the methods even if you don’t have a strong maths background, but we provide further information for those with a sound knowledge of undergraduate mathematics. We will assume basic MATLAB (or other) programming skills for some of the practical exercises.

## What software or tools do you need?

MathWorks will provide you with free access to MATLAB Online for the duration of the course so you can complete the programming exercises. Please visit MATLAB Online to ensure your system meets the minimum requirements.

## Who will you learn with?

### Ian Turner

I am a Professor of Computational Mathematics at QUT. My interests are in the modelling of complex systems using finite volume methods, fractional calculus and numerical linear algebra.

### Steven Psaltis

I'm a Postdoctoral Fellow in the ARC Centre of Excellence for Mathematical and Statistical Frontiers at QUT. I'm interested in numerical simulation of physical systems, gpu computing and visualisation

## Queensland University of Technology

QUT is a leading Australian university ranked in the top 1% of universities worldwide by the 2019 Times Higher Education World University Rankings. Located in Brisbane, it attracts over 50,000 students.

• Established

1989
• Location

Brisbane, Australia
• World ranking

Top 180Source: Times Higher Education World University Rankings 2019

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