Scientific Computing with Python : Mastering Numpy and Scipy
Language: English
Published by Nobletrex Press, 2024
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 40.97
Quantity: 1 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 'Scientific Computing with Python: Mastering Numpy and Scipy' is a comprehensive guide designed to equip readers with the knowledge and skills necessary for efficient numerical computations and data analysis. Whether you're a beginner or an advanced user, this book delves into essential topics such as array manipulation, advanced Numpy techniques, and the vast functionalities of Scipy, including optimization, linear algebra, signal processing, and statistical analysis. Each chapter builds on the previous one, offering detailed explanations, practical examples, and best practices. With an emphasis on real-world applications and case studies, this book is an invaluable resource for researchers, engineers, data scientists, and educators aiming to excel in the field of scientific computing. Discover the power of Python's robust libraries and elevate your computational skills to solve complex scientific problems.
Seller Inventory # 9781964899046
- Title
- Scientific Computing with Python : Mastering Numpy and Scipy
- Author
- John Smith
- Publisher
- Nobletrex Press
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1964899044
- ISBN 13
- 9781964899046
- Item weight
- 510 grams
- Dimensions
- 229x152x20 mm
"Scientific Computing with Python: Mastering Numpy and Scipy" is a comprehensive guide designed to equip readers with the knowledge and skills necessary for efficient numerical computations and data analysis. Whether you're a beginner or an advanced user, this book delves into essential topics such as array manipulation, advanced Numpy techniques, and the vast functionalities of Scipy, including optimization, linear algebra, signal processing, and statistical analysis. Each chapter builds on the previous one, offering detailed explanations, practical examples, and best practices. With an emphasis on real-world applications and case studies, this book is an invaluable resource for researchers, engineers, data scientists, and educators aiming to excel in the field of scientific computing. Discover the power of Python's robust libraries and elevate your computational skills to solve complex scientific problems.
"Synopsis" may belong to another edition of this title.
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