Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
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Published by Taylor & Francis Ltd, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
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Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days. 185.
Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
Seller: California Books, Miami, FL, U.S.A.
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
Seller: Books Puddle, New York, NY, U.S.A.
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
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Add to basketCondition: New. Dr. Crista Arangala is Professor of Mathematics and Chair of the Department of Mathematics and Statistics at Elon University in North Carolina. She and has been teaching and researching in a variety of fields including in inverse problems, applied partia.
Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
Seller: Revaluation Books, Exeter, United Kingdom
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Add to basketHardcover. Condition: Brand New. 366 pages. 9.19x6.13x0.87 inches. In Stock.
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
Seller: Revaluation Books, Exeter, United Kingdom
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Add to basketHardcover. Condition: Brand New. 366 pages. 9.19x6.13x0.87 inches. In Stock. This item is printed on demand.
Published by Taylor & Francis Ltd Mai 2023, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application. This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on Decision Trees and their applications in regression analysis. The text can be considered in two different but overlapping general data analytics categories: clustering and interpolation. Knowledge of mathematical techniques related to data analytics and exposure to interpretation of results within a data analytics context are particularly valuable for students studying undergraduate mathematics. Each chapter of this text takes the reader through several relevant case studies using real-world data. All data sets, as well as Python and R syntax, are provided to the reader through links to Github documentation. Following each chapter is a short exercise set in which students are encouraged to use technology to apply their expanding knowledge of linear algebra as it is applied to data analytics. A basic knowledge of the concepts in a first Linear Algebra course is assumed; however, an overview of key concepts is presented in the Introduction and as needed throughout the text. 290 pp. Englisch.
Published by Chapman And Hall/CRC, 2023
ISBN 10: 036745839X ISBN 13: 9780367458393
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
US$ 134.95
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Add to basketBuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application. This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on Decision Trees and their applications in regression analysis. The text can be considered in two different but overlapping general data analytics categories: clustering and interpolation. Knowledge of mathematical techniques related to data analytics and exposure to interpretation of results within a data analytics context are particularly valuable for students studying undergraduate mathematics. Each chapter of this text takes the reader through several relevant case studies using real-world data. All data sets, as well as Python and R syntax, are provided to the reader through links to Github documentation. Following each chapter is a short exercise set in which students are encouraged to use technology to apply their expanding knowledge of linear algebra as it is applied to data analytics. A basic knowledge of the concepts in a first Linear Algebra course is assumed; however, an overview of key concepts is presented in the Introduction and as needed throughout the text.