Visual Analytics for Data Scientists. 1st ed. 2020

種類:

電子ブック

責任表示:

by Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, Stefan Wrobel

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2020

著者名:

Andrienko, Natalia.

Andrienko, Gennady.

Fuchs, Georg.

Slingsby, Aidan.

Turkay, Cagatay.

Wrobel, Stefan.

SpringerLink (Online service)

続きを見る

ISBN:

9783030561468 [3030561461]    

注記:

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.
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 i

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