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Titlebook: Visual Analytics for Data Scientists; Natalia Andrienko,Gennady Andrienko,Stefan Wrobel Textbook 2020 Springer Nature Switzerland AG 2020

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11#
發(fā)表于 2025-3-23 10:33:48 | 只看該作者
12#
發(fā)表于 2025-3-23 16:53:59 | 只看該作者
Visual Analytics for Understanding Texts help when it is necessary to gain an overall understanding of characteristics and contents of large volumes of text or to find specific information in these volumes. Computer support in text analysis involves derivation of various kinds of structured data, such as numeric attributes and lists of si
13#
發(fā)表于 2025-3-23 18:09:06 | 只看該作者
14#
發(fā)表于 2025-3-24 00:00:14 | 只看該作者
Computational Modelling with Visual Analyticsarning, data mining, and various specialised disciplines, such as spatial statistics, transportation research, and animal ecology. However, valid and useful computerbased models cannot be obtained by mere application of some modelling software to available data. Modelling requires understanding of t
15#
發(fā)表于 2025-3-24 05:12:18 | 只看該作者
Conclusionsual analytics approaches and workflows using general techniques of visual analytics: abstraction, decomposition, selection, arrangement, and visual comparison.We take an example of an analysis scenario where the standard approaches presented earlier in this book do not work, and we demonstrate the
16#
發(fā)表于 2025-3-24 06:31:42 | 只看該作者
Textbook 2020ily 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
17#
發(fā)表于 2025-3-24 11:29:55 | 只看該作者
Introduction to Visual Analytics by an Example interactive operations, and computational processing. The underlying idea is to enable synergistic joint work of humans and computers, in which each side can effectively utilise its unique capabilities. The ideas and approaches of visual analytics are therefore very relevant to data science.
18#
發(fā)表于 2025-3-24 15:06:10 | 只看該作者
19#
發(fā)表于 2025-3-24 22:29:02 | 只看該作者
Visual Analytics for Understanding Multiple Attributessiest models to understand, becomes quite complex if you need to investigate the interactions between hundreds of variables. This chapter will discuss how not to get lost in these high-dimensional spaces and how visual analytics techniques can help you navigate your way through.
20#
發(fā)表于 2025-3-25 02:44:31 | 只看該作者
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