Gain the big data analytics skills that are in demand today
Big data is a fast-growing field and skills in the area are some of the most in demand today.
The Big Data Analytics program from Queensland University of Technology (QUT) & ACEMS comprises four online courses that each look at a different element of big data.
You’ll begin by examining how big data is collected and stored, before going on to explore how statistical inference, machine learning, mathematical modelling and data visualisation are used in its analysis.
You’ll become familiar with predictive analysis, dimension reduction, machine learning, clustering techniques and decision trees, before going on to look at the maths that underpins many of the tools you can use to manage and analyse big data.
Accessible for free on desktop, tablet or mobile and delivered in bite-sized chunks, the courses provide a flexible way to develop your big data analytics skills.
When you complete all four courses, upgrade and earn a Certificate of Achievement for each, you will receive a FutureLearn Award as proof of completing the program of study.
Many datasets can provide solutions to important problems and inform decisions. However, the size, complexity, quality and diversity of these datasets make them difficult to process and analyse. Join us and we’ll share new technological or methodological solutions you can use to meet the demand for analytics in your field.
We will introduce big data and some of the statistical and mathematical approaches for analysing it. Then we explore the power of big data and the process of getting from data to decisions before you use some of the tools available for storing and managing large datasets.
Many people have big data but only some people know what to do with it. Why? Well, the big problem is that the data is big - the size, complexity and diversity of datasets increases every day. This means we need new solutions for analysing data.
This course equips you for working with these solutions by introducing you to selected statistical and machine learning techniques used for analysing large datasets and extracting information. We also expose you to three software packages so you can develop your coding skills by completing practical exercises.
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.
Data visualisation is an important visual method for effective communication and analysing large datasets. Through data visualisations we are able to draw conclusions from data that are sometimes not immediately obvious and interact with the data in an entirely different way.
This course will provide you with an informative introduction to the methods, tools and processes involved in visualising big data. We will also take the time to examine briefly the use of visualisation throughout history dating back as far as 17000 BC.