Introduction to scientific computing and data analysis /

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Bibliographic Details
Author / Creator:Holmes, Mark H., author.
Imprint:Switzerland : Springer, 2016.
Description:1 online resource (xiv, 497 pages) : illustrations (some color)
Language:English
Series:Texts in computational science and engineering, 1611-0994 ; 13
Texts in computational science and engineering ; 13.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11263678
Hidden Bibliographic Details
ISBN:9783319302560
3319302566
9783319302546
331930254X
Notes:Includes bibliographical references and index.
Online resource; title from PDF title page (SpringerLink, viewed June 14, 2016).
Summary:"This textbook provides and introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimisation and regression based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used."--
Other form:Print version: Holmes, Mark H. Introduction to scientific computing and data analysis. Switzerland : Springer, 2016 9783319302546 331930254X
Standard no.:10.1007/978-3-319-30256-0

MARC

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245 1 0 |a Introduction to scientific computing and data analysis /  |c Mark H. Holmes. 
264 1 |a Switzerland :  |b Springer,  |c 2016. 
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505 0 |a Introduction to Scientific Computing -- Solving a Nonlinear Equation -- Matrix Equations -- Eigenvalue Problems -- Interpolation -- Numerical Integration -- Initial Value Problems -- Optimization -- Data Analysis -- Appendices. 
520 |a "This textbook provides and introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimisation and regression based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used."--  |c Provided by publisher. 
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