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| fixed length control field |
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| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
210302b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9783030561451 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
001.4226 |
| Item number |
AND |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Andrienko, Natalia |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Visual analytics for data scientists |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2020. |
| Place of publication, distribution, etc |
Cham : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xx, 440 p. ; |
| Other physical details |
ill. (chiefly col.), map, |
| Dimensions |
25 cm. |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
84.99 |
| Price type code |
EUR |
| Unit of pricing |
93.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Visual analytics |
|
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| Topical term or geographic name as entry element |
Visualization of information |
|
|---|
| Topical term or geographic name as entry element |
Pattern perception |
|
|---|
| Topical term or geographic name as entry element |
Artificial intelligence |
|
|---|
| Topical term or geographic name as entry element |
Big data |
|
|---|
| Topical term or geographic name as entry element |
computer science |
|
|---|
| Topical term or geographic name as entry element |
Image recognition |
|
|---|
| Topical term or geographic name as entry element |
Data mining |
| 710 ## - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Andrienko, Gennady |
|
|---|
| Corporate name or jurisdiction name as entry element |
Fuchs, Georg |
|
|---|
| Corporate name or jurisdiction name as entry element |
Slingsby, Aidan |
|
|---|
| Corporate name or jurisdiction name as entry element |
Turkay, Cagatay |
|
|---|
| Corporate name or jurisdiction name as entry element |
Wrobel, Stefan |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |