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Publicado por Springer Nature Switzerland AG, Cham, 2020
ISBN 10: 3030543706 ISBN 13: 9783030543709
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Añadir al carritoHardcover. Condición: new. Hardcover. Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form.The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the data-aptitude built by the material in this book. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Idioma: Inglés
Publicado por Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10: 3030543730 ISBN 13: 9783030543730
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Idioma: Inglés
Publicado por Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030543706 ISBN 13: 9783030543709
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Añadir al carritoHardback. Condición: New. 2020 ed. Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form.The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the "data-aptitude" built by the material in this book.
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Añadir al carritoTaschenbuch. Condición: Neu. Introduction to Data Systems | Building from Python | Thomas Bressoud (u. a.) | Taschenbuch | xxix | Englisch | 2021 | Springer | EAN 9783030543730 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Idioma: Inglés
Publicado por Springer-Nature New York Inc, 2020
ISBN 10: 3030543706 ISBN 13: 9783030543709
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Idioma: Inglés
Publicado por Springer Nature Switzerland AG, Cham, 2020
ISBN 10: 3030543706 ISBN 13: 9783030543709
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Añadir al carritoHardcover. Condición: new. Hardcover. Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form.The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the data-aptitude built by the material in this book. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Idioma: Inglés
Publicado por Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030543706 ISBN 13: 9783030543709
Librería: Rarewaves.com UK, London, Reino Unido
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Añadir al carritoHardback. Condición: New. 2020 ed. Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form.The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the "data-aptitude" built by the material in this book.
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