Visual Analytics for Data Scientists

Natalia and Gennady Andrienko are lead scientists responsible for visual analytics research at the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) in Germany since 2007 and part-time professors at City, University of London since 2013.  They co-authored monographs “Exploratory Analysis of Spatial and Temporal Data” (Springer, 2006) and “Visual Analytics of Movement” (Springer, 2013), and more than 100 peer-reviewed journal papers. Their research interests include geovisualization, information visualization with a focus on spatial and temporal data, visual analytics, interactive knowledge discovery and data mining, and data science. Natalia and Gennady Andrienko received test of time award at IEEE VAST 2018, best paper awards at AGILE 2006, IEEE VAST 2011 and 2012, and EuroVis 2015 conferences and EuroVA 2018 and 2019 workshops, honorable mention awards at IEEE VAST 2010 and EuroVis 2017 conferences, VAST challenge awards 2008 and 2014, and best posterawards at AGILE 2007 and 2018, ACM GIS 2011 and IEEE VAST 2016 conferences. Georg Fuchs is head of the Big Data Analytics and Intelligence division at Fraunhofer IAIS. His research focuses on visual analytics, in particular for the exploration and analysis of interactive spatio-temporal and movement data, as well as in the context of creating methods and tools for explainable and trustworthy AI in a variety of application domains. His further research interests include information visualization and computer graphics. Aidan Slingsby is a Lecturer in the Department of Computer Science as part of the giCentre Research Centre att City, University of London. His research focuses on the role of data visualisation in the analysis of data, particularly those that are spatial and temporal. He adapts, designs, applies and implements static and interactive information visualisation for data exploration, analysis and presentation. He works in variety of application areas includinginsurance, demographics, transport and ecology. Cagatay Turkay is an Associate Professor at the Centre for Interdisciplinary Methodologies at the University of Warwick, UK. His research investigates the interactions between data, algorithms and people, and explores the role of interactive visualisation and other interaction mediums such as natural language at this intersection. He designs techniques and algorithms that are sensitive to their users in various decision-making scenarios involving primarily high-dimensional and spatio-temporal phenomena, and develops methods to study how people work interactively with data and computed artefacts. Stefan Wrobel is Professor of Computer Science at University of Bonn and Director of the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS. His work is focused on questions of the digital revolution, in particular intelligent algorithms and systems for the large-scale analysis of data and the influence of Big Data/Smart Data on the use of information in companies and society. He is the author of a large number of publications on data mining and machine learning, is on the Editorial Board of several leading academic journals in his field, and is an elected founding member of the “International Machine Learning Society”. He is engaged nationally and internationally in pushing forward the benefits of digitization, big data and artificial intelligence.

• Part I: Introduction to Visual Analytics in Data Science.- 1. Introduction to Visual Analytics by an Example.- 2. General Concepts.- 3. Principles of Interactive Visualisation.- 4. Computational Techniques in Visual Analytics.- Part II: Visual Analytics along the Data Science Workflow.- 5. Visual Analytics for Investigating and Processing Data.- 6. Visual Analytics for Understanding Multiple Attributes.- 7. Visual Analytics for Understanding Relationships between Entities.- 8. Visual Analytics for Understanding Temporal Distributions and Variations.- 9. Visual Analytics for Understanding Spatial Distributions and Spatial Variation.- 10. Visual Analytics for Understanding Phenomena in Space and Time.- 11. Visual Analytics for Understanding Texts.- 12. Visual Analytics for Understanding Images and Video.- 13. Computational Modelling with Visual Analytics.- 14. Conclusion.