The amount of digital data that exists in the world is growing at a rapid rate. Recent years have witnessed a dramatic increase of data in many fields of science and engineering, due to the advancement of sensors, mobile devices, biotechnology, digital communication, and internet applications. Data is generated continuously from multiple sources by companies, users and devices in a huge velocity, volume and variety.
Big data refers to data that is so large that it cannot be processed by using traditional applications. Although significant computer technology exists, new skills are needed to fully understand the power of Big Data. Very little targeted training is provided to address a growing skills gap in this area.
Since its foundation in 2017, the Liverpool Big Data Science (LIV.DAT) Centre for Doctoral Training (CDT) has quickly established itself a hub for training students in managing, analysing and interpreting large, complex datasets and high rates of data flow.
It features a unique training approach addressing some of the biggest challenges in data intensive science to tackle a growing skills gap in this important area. Between 2017-2019, 29 students joined the Centre and 7 additional students, recruited from all over the world, have started in October 2020, keeping LIV.DAT one of the largest CDTs in the country.
The training centre is supported by the Science and Technology Facilities Council (STFC) and hosted by the University of Liverpool and Liverpool John Moores University / Astrophysics Research Institute.
The CDT offers a comprehensive training in data intensive science through cutting edge research projects and a targeted academic training programme, complemented by secondments to national and international partners.
It also capitalises on the Liverpool Big Data Network (LBDN), an initiative set up in 2013 in response to Big Data being one of the 'Eight Great technologies'. As well as providing a focus for relevant existing MSc provision, LBDN now comprises some 100 academics drawn from many different academic disciplines and united by a common interest in developing and applying Big Data.