Visual Analytics for Data Scientists - Aidan Slingsby

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Résumé :
This textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail. The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified. The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.

Biographie:
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....

Sommaire:

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.