|
| Titre : |
Data science for the geosciences |
| Type de document : |
texte imprimé |
| Auteurs : |
Wang, Lijing ; Yin, Zhen Caers, Jef, Auteur |
| Editeur : |
Cambridge : Cambridge University Press |
| Année de publication : |
2024 |
| Importance : |
(XIII-250 p.) ; ill. en noir et blanc, p. de pl. en coul., cartes, graphiques |
| Format : |
26cm |
| ISBN/ISSN/EAN : |
978-1-00-920140-7 |
| Langues : |
Anglais (eng) |
| Catégories : |
551 Géologie Générale
|
| Tags : |
Sciences la Terre Méthodes statistiques Informatique Manuels d'enseignement supérieur |
| Index. décimale : |
551/105 |
| Résumé : |
Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide. KEY FEATURES : Provides step-by-step instruction for each method, allowing students to easily put their skills into practice ; Uses real earth datasets and case studies to address Earth science questions across a range of disciplines ; Focuses on intuitive reasoning without the need for complex mathematics or prior coding experience |
| Permalink : |
https://web.univ-oran2.dz/pmbfstu/index.php?lvl=notice_display&id=2634 |
Data science for the geosciences [texte imprimé] / Wang, Lijing ; Yin, Zhen Caers, Jef, Auteur . - Cambridge : Cambridge University Press, 2024 . - (XIII-250 p.) ; ill. en noir et blanc, p. de pl. en coul., cartes, graphiques ; 26cm. ISBN : 978-1-00-920140-7 Langues : Anglais ( eng)
| Catégories : |
551 Géologie Générale
|
| Tags : |
Sciences la Terre Méthodes statistiques Informatique Manuels d'enseignement supérieur |
| Index. décimale : |
551/105 |
| Résumé : |
Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide. KEY FEATURES : Provides step-by-step instruction for each method, allowing students to easily put their skills into practice ; Uses real earth datasets and case studies to address Earth science questions across a range of disciplines ; Focuses on intuitive reasoning without the need for complex mathematics or prior coding experience |
| Permalink : |
https://web.univ-oran2.dz/pmbfstu/index.php?lvl=notice_display&id=2634 |
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