0425DataQ1 Data QualityInfo Location Attendee Categories More Info Event Information
DescriptionIn this 1-day interactive course you will learn the why, what, when and how of investigating data quality. You will put your knowledge into practice in a series of challenges that cover tabular, spatial and longitudinal data. Course objectives · To understand the range of tasks you need to perform to investigate data quality · To learn about computational and visualization techniques you can use · To gain experience of an efficient and rigorous workflow · To learn how to communicate your findings Is this course for me? The course is open to everyone, irrespective of your level of knowledge about data quality. However, you will get the most out of the course if you have familiarised yourself with the practical guide to characterising data and investigating data quality https://doi.org/10.5518/1481, which was written by a team led by the course tutor. No prior technical knowledge is assumed for this course. You may use any software for the practical work and you need to bring a laptop to use for that practical work, with the software installed.
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Additional ItemsMore InformationCourse Tutor Roy Ruddle is a Professor of Computing at the University of Leeds, and Director of Research Technology at the Leeds Institute for Data Analytics (LIDA). Roy has worked in both industry and academia, and specialises in interdisciplinary research into interactive visualization, data quality and data science workflows. His research spans application domains as diverse as retail, petrophysics and health, and his Leeds Virtual Microscope (LVM) has been commercialised by the healthcare company Roche and was a REF2021 Impact Case Study. He was an Alan Turing Institute Fellow from 2018 – 2023, and is currently principal investigator on two major projects: Making Visualization Scalable (MAVIS) for explainable AI (funded by the EPSRC) and AI for Dynamic prescribing optimisation and care integration in multimorbidity (DynAIRx; funded by the NIHR). | |||||||||||||||
