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ISBN 10: 1032942312 ISBN 13: 9781032942315
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Paperback. Condition: new. Paperback. This text focuses on the primary topics in a first course in Linear Algebra. The author includes additional advanced topics related to data analysis, singular value decomposition, and connections to differential equations. This is a lab text that would lead a class through Linear Algebra using Mathematica demonstrations and Mathematica coding.The book includes interesting examples embedded in the projects. Examples include the discussions of Lights Out, Nim, the Hill Cipher, and a variety of relevant data science projects.The 2nd Edition contains:Additional Theorems and Problems for students to prove/disprove (these act as theory exercises at the end of most sections of the text)Additional sections that support Data Analytics techniques, such as Kronecker sums and products, and LU decomposition of the Vandermonde matrixUpdated and expanded end-of-chapter projectsInstructors and students alike have enjoyed this popular book, as it offers the opportunity to add Mathematica to the Linear Algebra course.I would definitely use the book (specifically the projects at the end of each section) to motivate undergraduate research.Nick Luke, North Carolina A&T State University. This text focuses on the primary topics in a first course in Linear Algebra including additional advanced topics related to data analysis, singular value decomposition and connections to differential equations. This is a lab text that would lead a class through Linear Algebra using Mathematica demonstrations and Mathematica coding Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 1032942312 ISBN 13: 9781032942315
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Add to basketPaperback. Condition: Brand New. 2nd edition. 168 pages. 9.18x6.12x9.21 inches. In Stock.
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ISBN 10: 1032942312 ISBN 13: 9781032942315
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Published by Taylor & Francis Ltd Feb 2025, 2025
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Add to basketTaschenbuch. Condition: Neu. Neuware - This text focuses on the primary topics in a first course in Linear Algebra including additional advanced topics related to data analysis, singular value decomposition and connections to differential equations. This is a lab text that would lead a class through Linear Algebra using Mathematica demonstrations and Mathematica coding.
Published by Chapman and Hall/CRC, 2025
ISBN 10: 1032942312 ISBN 13: 9781032942315
Language: English
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Add to basketPaperback. Condition: Brand New. 2nd edition. 168 pages. 9.18x6.12x9.21 inches. In Stock.
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ISBN 10: 1032942312 ISBN 13: 9781032942315
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ISBN 10: 1032942312 ISBN 13: 9781032942315
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Add to basketPaperback. Condition: new. Paperback. This text focuses on the primary topics in a first course in Linear Algebra. The author includes additional advanced topics related to data analysis, singular value decomposition, and connections to differential equations. This is a lab text that would lead a class through Linear Algebra using Mathematica demonstrations and Mathematica coding.The book includes interesting examples embedded in the projects. Examples include the discussions of Lights Out, Nim, the Hill Cipher, and a variety of relevant data science projects.The 2nd Edition contains:Additional Theorems and Problems for students to prove/disprove (these act as theory exercises at the end of most sections of the text)Additional sections that support Data Analytics techniques, such as Kronecker sums and products, and LU decomposition of the Vandermonde matrixUpdated and expanded end-of-chapter projectsInstructors and students alike have enjoyed this popular book, as it offers the opportunity to add Mathematica to the Linear Algebra course.I would definitely use the book (specifically the projects at the end of each section) to motivate undergraduate research.Nick Luke, North Carolina A&T State University. This text focuses on the primary topics in a first course in Linear Algebra including additional advanced topics related to data analysis, singular value decomposition and connections to differential equations. This is a lab text that would lead a class through Linear Algebra using Mathematica demonstrations and Mathematica coding Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Published by Taylor & Francis Ltd, 2025
ISBN 10: 1032942312 ISBN 13: 9781032942315
Language: English
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Add to basketPaperback. Condition: new. Paperback. This text focuses on the primary topics in a first course in Linear Algebra. The author includes additional advanced topics related to data analysis, singular value decomposition, and connections to differential equations. This is a lab text that would lead a class through Linear Algebra using Mathematica demonstrations and Mathematica coding.The book includes interesting examples embedded in the projects. Examples include the discussions of Lights Out, Nim, the Hill Cipher, and a variety of relevant data science projects.The 2nd Edition contains:Additional Theorems and Problems for students to prove/disprove (these act as theory exercises at the end of most sections of the text)Additional sections that support Data Analytics techniques, such as Kronecker sums and products, and LU decomposition of the Vandermonde matrixUpdated and expanded end-of-chapter projectsInstructors and students alike have enjoyed this popular book, as it offers the opportunity to add Mathematica to the Linear Algebra course.I would definitely use the book (specifically the projects at the end of each section) to motivate undergraduate research.Nick Luke, North Carolina A&T State University. This text focuses on the primary topics in a first course in Linear Algebra including additional advanced topics related to data analysis, singular value decomposition and connections to differential equations. This is a lab text that would lead a class through Linear Algebra using Mathematica demonstrations and Mathematica coding Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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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 has been teaching and researching in a variety of fields, including inverse problems, applied parti.