Numerical Methods: An Inquiry Based Approach with Python
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Numerical Methods: An Inquiry Based Approach with Python
An inquiry-based approach to a first semester undergraduate Numerical Methods or Numerical Analysis course
Numerical Methods: An Inquiry Based Approach with Python
Numéro d'article: 56398992

Numerical Methods: An Inquiry Based Approach with Python

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Numéro d'article: 56398992

HTG 5824

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An inquiry-based approach to a first semester undergraduate Numerical Methods or Numerical Analysis course
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Ce qui se démarque

Inquiry-Based Learning
Engages students with hands-on exploration, fostering critical thinking and problem-solving skills through practical applications of numerical methods using Python.
Python Integration
Seamlessly incorporates Python programming, allowing learners to implement and visualize numerical methods efficiently, enhancing their coding proficiency alongside mathematical concepts.
Comprehensive Coverage
Covers a wide range of numerical methods thoroughly, providing in-depth explanations and real-world applications, making complex topics accessible for learners at all levels.

Détails du produit

Shop Numerical Methods: An Inquiry Based Approach with Python online at a best price in Haiti. B08JDTP4N8
  • Inquiry-based approach suitable for first semester undergraduate Numerical Methods or Numerical Analysis course
  • Covers topics such as floating point arithmetic, function approximation, numerical root finding, differentiation, integration, computational optimization, numerical linear algebra, ordinary and partial differential equations
  • Includes collections of exercises guiding students through building and analyzing numerical algorithms
  • Emphasizes numerical experimentation over mathematical proof
  • Each chapter concludes with challenging exercises and projects
  • Utilizes Python, with a focus on numpy, matplotlib, and scipy for solving computational problems
  • Intended for sophomore to junior level STEM majors with background in Calculus, Linear Algebra, and Differential Equations
Publisher Independently published
Publication date September 18, 2020
Language English
Print length 408 pages
ISBN-13 979-8687369954
Item Weight 1.26 pounds (570 grams)
Dimensions 6 x 0.92 x 9 inches (15.2 x 2.3 x 22.9 cm)

À qui est-ce destiné ?

Suitable For
  • Students in STEM

    Ideal for students in science, technology, engineering, and mathematics needing practical applications of numerical methods using Python.

  • Self-learners

    Great for self-learners who want to explore numerical methods independently and enhance their programming skills with Python.

  • Educators

    Utilized by educators to teach numerical methods effectively, as it incorporates inquiry-based learning and hands-on programming tasks.

Not Suitable For
  • Beginners in Programming

    Not suitable for complete programming novices since it assumes a basic understanding of Python and numerical concepts.

DESCRIPTION DU PRODUIT

Numerical Methods: An Inquiry Based Approach with Python

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Questions et réponses des clients

  • question: What is the primary focus of 'Numerical Methods: An Inquiry Based Approach with Python'?

    répondre: The primary focus of this book is to provide a thorough understanding of numerical methods through inquiry-based learning. It uses Python programming as a tool to explore and implement various numerical techniques, fostering a deeper conceptual grasp of the subject. This approach is particularly beneficial for students and professionals who wish to apply theoretical principles to real-world problems in fields such as engineering, physics, and computer science, making it ideal for hands-on learning and exploration.
  • question: Who is the target audience for this book?

    répondre: The target audience for 'Numerical Methods: An Inquiry Based Approach with Python' includes undergraduate and graduate students in mathematics, engineering, and computer science, as well as professionals who want to enhance their skills in numerical analysis. This book caters to readers with diverse backgrounds, especially those who seek to develop their programming proficiency alongside theoretical knowledge, making it a versatile resource for both academic and practical applications.
  • question: How does Python enhance the learning experience in this book?

    répondre: Python enhances the learning experience by providing a flexible and powerful programming environment that readers can use to implement numerical methods effectively. The book includes numerous coding examples and exercises that encourage learners to write and run Python code directly, allowing them to visualize concepts and explore outcomes. This hands-on approach enables a deeper understanding of numerical analysis, helping readers transition from theoretical understanding to practical skills in real-world applications.
  • question: What specific numerical methods are covered in the book?

    répondre: The book covers a variety of essential numerical methods, including interpolation, numerical integration, differentiation, and solving ordinary differential equations. These methods are crucial for tackling complex mathematical problems and are applicable in various domains, such as simulations in engineering and data science. By covering fundamental and advanced techniques, the book provides learners with a comprehensive toolkit for analyzing and solving mathematical problems using computational approaches.
  • question: Is prior knowledge of Python required to understand this book?

    répondre: While prior knowledge of Python can be helpful, it is not strictly required to understand this book. The author introduces the necessary programming concepts alongside the numerical methods, ensuring that readers can learn both simultaneously. Beginners can follow along with the provided examples, and more experienced programmers can benefit from the advanced applications discussed throughout. This makes the book a great resource whether you're a novice or looking to refine your skills.
  • question: Can this book be used for self-study, and how effectively?

    répondre: Yes, 'Numerical Methods: An Inquiry Based Approach with Python' is designed for self-study. Its structured format, combined with introductory explanations, practical coding exercises, and inquiry-based learning techniques, enables readers to progress at their own pace. The clear examples and comprehensive explanations allow learners to grasp complex concepts effectively, making it suitable for independent study and practical application in various technical ventures.
  • question: Are there any supplementary resources available for this book?

    répondre: The book is accompanied by supplementary resources such as online materials, code repositories, and problem sets that enhance the learning experience. These resources provide additional exercises, practical examples, and updates that can deepen understanding and support learners as they apply numerical methods. Access to these materials enhances the value of the book, making it a rich resource for both classroom use and self-directed study.
  • question: What are the key benefits of using an inquiry-based approach in learning numerical methods?

    répondre: The inquiry-based approach encourages active learning and critical thinking, allowing learners to engage deeply with the material. By prompting students to ask questions, formulate hypotheses, and explore solutions, it fosters a sense of ownership and curiosity in their learning process. This method enhances retention of concepts and prepares learners for real-world problem-solving, making it particularly effective for complex subjects like numerical analysis.
  • question: How can I apply the skills learned from this book in a professional setting?

    répondre: The skills acquired from 'Numerical Methods: An Inquiry Based Approach with Python' can be applied in various professional settings, including data analysis, engineering simulations, and scientific research. Understanding numerical methods allows professionals to model complex systems, optimize performance, and analyze large datasets efficiently. These competencies are invaluable in fields such as finance, technology, and academia, where quantitative analysis plays a crucial role in decision-making and innovation.
  • question: Where can I buy 'Numerical Methods: An Inquiry Based Approach with Python' in Haiti?

    répondre: You can buy 'Numerical Methods: An Inquiry Based Approach with Python' at Ubuy, a reliable online retailer that offers a wide selection of books and educational resources. Ubuy provides convenient shopping experiences, allowing you to easily find and purchase this title along with a variety of other products in Haiti, making it an excellent option for expanding your knowledge in numerical methods and Python programming.

Mathematics Editorial Review

Numerical Methods: An Inquiry Based Approach with Python is a comprehensive resource published independently, focusing on the integration of numerical methods and Python programming. Spanning 408 pages, this book delves into various computational techniques essential for solving mathematical problems. It offers a unique exploratory approach that enhances learning through inquiry, making complex concepts more accessible to readers. With its dimensions of 6 x 0.92 x 9 inches and a weight of 1.26 pounds, it's convenient for both academic and personal use. The diverse topics and hands-on applications make it a reliable companion for anyone interested in numerical analysis and programming.

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Avantages

  • Unique inquiry-based learning approach
  • Comprehensive coverage of numerical methods
  • Hands-on programming applications with Python
  • Ideal for students and professionals alike
  • Well-structured content for easy understanding

Les inconvénients

  • Might be challenging for complete beginners

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Caractéristiques et avantages

  • Inquiry-based approach
  • Covers various topics in numerical methods
  • Heavy emphasis on numerical experimentation
  • Challenging exercises and projects at the end of each chapter
  • Python programming language with emphasis on numpy, matplotlib, and scipy
  • Intended for sophomore to junior level STEM majors

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