Visual Data Science
Organizations are collecting an increasing amount of data every day. To make use of this rich source of information, more and more employees have to deal with data analysis and data science. Exploring data, understanding its structure, and finding new insights, can be greatly supported by data visua...
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| Autor principal: | |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2021
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/76821 |
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| _version_ | 1879164724600373248 |
|---|---|
| author | Schmidt, Johanna |
| author_browse | Schmidt, Johanna |
| author_facet | Schmidt, Johanna |
| author_sort | Schmidt, Johanna |
| building | IntechOpen |
| collection | Acceso abierto |
| description | Organizations are collecting an increasing amount of data every day. To make use of this rich source of information, more and more employees have to deal with data analysis and data science. Exploring data, understanding its structure, and finding new insights, can be greatly supported by data visualization. Therefore, the increasing interest in data science and data analytics also leads to a growing interest in data visualization and exploratory data analysis. We will outline how existing data visualization techniques are already successfully employed in different data science workflow stages. In some cases, visualization is beneficial, while still future research will be needed for other categories. The vast amount of libraries and applications available for data visualization has fostered its usage in data science. We will highlight the differences among the libraries and applications currently available. Unfortunately, there is still a clear gap between visualization research developments over the past decades and the features provided by commonly used tools and data science applications. Although basic charting options are commonly available, more advanced visualization techniques have hardly been integrated as new features yet. |
| doi_str_mv | 10.5772/intechopen.97750 |
| first_indexed | 2026-10-15T21:38:53Z |
| format | Book Chapter |
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<dc:title>Visual Data Science</dc:title>
<dc:creator>Johanna Schmidt</dc:creator>
<dc:subject>Physical Sciences, Engineering and Technology</dc:subject>
<dc:description>Organizations are collecting an increasing amount of data every day. To make use of this rich source of information, more and more employees have to deal with data analysis and data science. Exploring data, understanding its structure, and finding new insights, can be greatly supported by data visualization. Therefore, the increasing interest in data science and data analytics also leads to a growing interest in data visualization and exploratory data analysis. We will outline how existing data visualization techniques are already successfully employed in different data science workflow stages. In some cases, visualization is beneficial, while still future research will be needed for other categories. The vast amount of libraries and applications available for data visualization has fostered its usage in data science. We will highlight the differences among the libraries and applications currently available. Unfortunately, there is still a clear gap between visualization research developments over the past decades and the features provided by commonly used tools and data science applications. Although basic charting options are commonly available, more advanced visualization techniques have hardly been integrated as new features yet.</dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2021-05-20</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/76821</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.97750</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-1-83962-943-3</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/9971 ; Data Science, Data Visualization, and Digital Twins</dc:source>
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| id | intech-76821 |
| institution | IntechOpen |
| isbn | 978-1-83962-943-3 |
| language | English |
| last_indexed | 2026-10-15T21:38:53Z |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-768212021-05-20 Visual Data Science Johanna Schmidt Physical Sciences, Engineering and Technology Organizations are collecting an increasing amount of data every day. To make use of this rich source of information, more and more employees have to deal with data analysis and data science. Exploring data, understanding its structure, and finding new insights, can be greatly supported by data visualization. Therefore, the increasing interest in data science and data analytics also leads to a growing interest in data visualization and exploratory data analysis. We will outline how existing data visualization techniques are already successfully employed in different data science workflow stages. In some cases, visualization is beneficial, while still future research will be needed for other categories. The vast amount of libraries and applications available for data visualization has fostered its usage in data science. We will highlight the differences among the libraries and applications currently available. Unfortunately, there is still a clear gap between visualization research developments over the past decades and the features provided by commonly used tools and data science applications. Although basic charting options are commonly available, more advanced visualization techniques have hardly been integrated as new features yet. IntechOpen 2021-05-20 Chapter, Part Of Book https://www.intechopen.com/chapters/76821 doi:10.5772/intechopen.97750 en ISBN:978-1-83962-943-3 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/9971 ; Data Science, Data Visualization, and Digital Twins |
| spellingShingle | Physical Sciences, Engineering and Technology Schmidt, Johanna Visual Data Science |
| title | Visual Data Science |
| title_full | Visual Data Science |
| title_fullStr | Visual Data Science |
| title_full_unstemmed | Visual Data Science |
| title_short | Visual Data Science |
| title_sort | visual data science |
| topic | Physical Sciences, Engineering and Technology |
| url | https://www.intechopen.com/chapters/76821 |
| work_keys_str_mv | AT schmidtjohanna visualdatascience |