Topics covered include:

• Data acquisition and quality This will be a hands-on guide on how to deal with data in the beginning of the processing pipeline and understand how the data quality has an impact on the overall results.
• Data Processing and Curation To introduce the idea of data curation and how is it different from a data processing pipeline.
• Interactive Data Visualisation To teach the basics in creating pragmatic interactive data visualisation by using modern web technologies.
• Large scale data visualisation To introduce the concepts of processing datasets that are large enough to not fit onto a single machine and using the Open Visualisation Environment to create a virtually infinite canvas to render your graphics.

On completion of this masterclass, participants will be able to:

• Describe the latest development of data curation and processing methods
• Understand the basic knowledge about various types of data, storage, encoding and decoding techniques
• Apply the knowledge and experience gained to develop good data visualisation stories
• Be able to “debug” data quality issues by using various data curation and visualisation technique

Team based learning via group project:

As part of this masterclass, students will have the opportunity to work in small teams on a group project.   Students will be asked first to select a novel dataset that has not been cleaned up. A list of sample datasets will be provided on the first week lecture, but students are also encouraged to bring their own data to work on. The group should be focusing on developing a project that uses some of the data curation, processing and visualisation techniques presented in the course work. 

This masterclass is designed for undergraduate or postgraduate students studying a technical subject, i.e. Engineering, Computing, Software Engineering, Math, Physics or related disciplines.