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9783319895741: Applied Scientific Computing: With Python (Texts in Computer Science)

Sinopsis

This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content. Emphasis is placed on the need for, and methods of, scientific computing for a range of different types of problems, supplying the evidence and justification to motivate the reader. Practical guidance on coding the methods is also provided, through simple-to-follow examples using Python.

Topics and features: provides an accessible and applications-oriented approach, supported by working Python code for many of the methods; encourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applications; introduces the main concepts in modeling, python programming, number representation, and errors; explains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton method; discusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge-Kutta, and shooting methods; presents largely self-contained chapters, arranged in a logical order suitable for an introductory course on scientific computing.

Undergraduate students embarking on a first course on numerical methods or scientific computing will find this textbook to be an invaluable guide to the field, and to the application of these methods across such varied disciplines as computer science, engineering, mathematics, economics, the physical sciences, and social science.

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Acerca del autor

Dr. Peter R. Turner is the Founding Director of the Institute for STEM Education at Clarkson University, Potsdam, NY USA.

Dr. Thomas Arildsen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark.

Dr. Kathleen Kavanagh is a Professor in the Department of Mathematics at Clarkson University.

De la contraportada

This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content. Emphasis is placed on the need for, and methods of, scientific computing for a range of different types of problems, supplying the evidence and justification to motivate the reader. Practical guidance on coding the methods is also provided, through simple-to-follow examples using Python.

Topics and features:

  • Provides an accessible and applications-oriented approach, supported by working Python code for many of the methods
  • Encourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applications
  • Introduces the main concepts in modeling, python programming, number representation, and errors
  • Explains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton method
  • Discusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge–Kutta, and shooting methods
  • Presents largely self-contained chapters, arranged in a logical order suitable for an introductory course on scientific computing

Undergraduate students embarking on a first course on numerical methods or scientific computing will find this textbook to be an invaluable guide to the field, and to the application of these methods across such varied disciplines as computer science, engineering, mathematics, economics, the physical sciences, and social science.

Dr. Peter R. Turner  is the Founding Director of the Institute for STEM Education at Clarkson University, Potsdam, NY, USA.  Dr. Thomas Arildsen  is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark.  Dr. Kathleen Kavanagh  is a Professor in the Department of Mathematics at Clarkson University.

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9783319895765: Applied Scientific Computing: With Python

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ISBN 10:  3319895761 ISBN 13:  9783319895765
Editorial: Springer, 2018
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Turner, Peter R.,Arildsen, Thomas,Kavanagh, Kathleen
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Turner, Peter R.; Arildsen, Thomas; Kavanagh, Kathleen
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Peter R. Turner And Thomas Arildsen And Kathleen Kavanagh
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Hardcover. Condición: Very Good+. 1st Edition (Unstated). Texts In Computer Science; 272 pages; Graphs. Charts. Very Good condition. No noteworthy defects. No markings. ; - Your satisfaction is our priority. We offer free returns and respond promptly to all inquiries. Your item will be carefully cushioned in bubble wrap and securely boxed. All orders ship on the same or next business day. Buy with confidence. Nº de ref. del artículo: HVD-52011-A-0

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Peter R. Turner
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content. Emphasis is placed on the need for, and methods of, scientific computing for a range of different types of problems, supplying the evidence and justification to motivate the reader. Practical guidance on coding the methods is also provided, through simple-to-follow examples using Python.Topics and features: provides an accessible and applications-oriented approach, supported by working Python code for many of the methods; encourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applications; introduces the main concepts in modeling, python programming, number representation, and errors; explains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton method; discusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge-Kutta, and shooting methods; presents largely self-contained chapters, arranged in a logical order suitable for an introductory course on scientific computing.Undergraduate students embarking on a first course on numerical methods or scientific computing will find this textbook to be an invaluable guide to the field, and to the application of these methods across such varied disciplines as computer science, engineering, mathematics, economics, the physical sciences, and social science. 284 pp. Englisch. Nº de ref. del artículo: 9783319895741

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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content. Emphasis is placed on the need for, and methods of, scientific computing for a range of different types of problems, supplying the evidence and justification to motivate the reader. Practical guidance on coding the methods is also provided, through simple-to-follow examples using Python.Topics and features: provides an accessible and applications-oriented approach, supported by working Python code for many of the methods; encourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applications; introduces the main concepts in modeling, python programming, number representation, and errors; explains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton method; discusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge-Kutta, and shooting methods; presents largely self-contained chapters, arranged in a logical order suitable for an introductory course on scientific computing.Undergraduate students embarking on a first course on numerical methods or scientific computing will find this textbook to be an invaluable guide to the field, and to the application of these methods across such varied disciplines as computer science, engineering, mathematics, economics, the physical sciences, and social science. Nº de ref. del artículo: 9783319895741

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Turner, Peter R.; Arildsen, Thomas; Kavanagh, Kathleen
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides practical programming examples and exercises in the increasingly popular, free and open-source Python language Presents a project-oriented approach that helps readers practice the introduced concepts and methods to improve understanding . Nº de ref. del artículo: 220486291

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Hardback. Condición: New. 2018 ed. This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content. Emphasis is placed on the need for, and methods of, scientific computing for a range of different types of problems, supplying the evidence and justification to motivate the reader. Practical guidance on coding the methods is also provided, through simple-to-follow examples using Python.Topics and features: provides an accessible and applications-oriented approach, supported by working Python code for many of the methods; encourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applications; introduces the main concepts in modeling, python programming, number representation, and errors; explains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton method; discusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge-Kutta, and shooting methods; presents largely self-contained chapters, arranged in a logical order suitable for an introductory course on scientific computing.Undergraduate students embarking on a first course on numerical methods or scientific computing will find this textbook to be an invaluable guide to the field, and to the application of these methods across such varied disciplines as computer science, engineering, mathematics, economics, the physical sciences, and social science. Nº de ref. del artículo: LU-9783319895741

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