國立高雄大學圖資館 |

Language: English

Back

Visual analytics for data scientists

Andrienko, Natalia.

Visual analytics for data scientists

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visual analytics for data scientistsby Natalia Andrienko ... [et al.].
other author:	Andrienko, Natalia.
Published:	Cham :Springer International Publishing :2020.
Description:	xx, 440 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Visual analytics.
Online resource:	https://doi.org/10.1007/978-3-030-56146-8
ISBN:	9783030561468$q(electronic bk.)

Visual analytics for data scientists
Visual analytics for data scientists[electronic resource] /by Natalia Andrienko ... [et al.]. - Cham :Springer International Publishing :2020. - xx, 440 p. :ill., digital ;24 cm.

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 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.

ISBN: 9783030561468$q(electronic bk.)

Standard No.: 10.1007/978-3-030-56146-8doiSubjects--Topical Terms:

285731
Visual analytics.

LC Class. No.: QA76.9.I52

Dewey Class. No.: 001.4226

Visual analytics for data scientists
LDR:04226nmm a2200337 a 4500 001 585413
003 DE-He213
005 20201228091553.0
006 m d
007 cr nn 008maaau
008 210311s2020 sz s 0 eng d
020 $a 9783030561468$q(electronic bk.)
020 $a 9783030561451$q(paper)
024 7 $a 10.1007/978-3-030-56146-8 $2 doi
035 $a 978-3-030-56146-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.I52
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 001.4226 $2 23
090 $a QA76.9.I52 $b V834 2020
245 0 0 $a Visual analytics for data scientists $h [electronic resource] / $c by Natalia Andrienko ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xx, 440 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Visual analytics. $3 285731
650 0 $a Information visualization. $3 248886
650 1 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Pattern Recognition. $3 273706
700 1 $a Andrienko, Natalia. $3 243715
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-56146-8
950 $a Mathematics and Statistics (SpringerNature-11649)