| 書目名稱 | Visual Analytics for Data Scientists | | 編輯 | Natalia Andrienko,Gennady Andrienko,Stefan Wrobel | | 視頻video | http://file.papertrans.cn/984/983671/983671.mp4 | | 概述 | Presents the main principles, techniques and approaches of visual analytics in a practice-oriented way.Describes the use of visual analytics methods, organised by various data types including multidim | | 圖書封面 |  | | 描述 | .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 prope | | 出版日期 | Textbook 2020 | | 關(guān)鍵詞 | Data Science; Data Analytics; Visual Analytics; Data Mining; Machine Learning | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-56146-8 | | isbn_softcover | 978-3-030-56148-2 | | isbn_ebook | 978-3-030-56146-8 | | copyright | Springer Nature Switzerland AG 2020 |
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