Introduction to Scientific Computing and Data Analysis

This textbook provides an 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 optimization 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. The codes used for most of the computational examples in the text are available on GitHub.  This new edition includes material necessary for an upper division course in computational linear algebra.

Preface.- Preface to Second Edition.- Introduction to Scientific Computing.- Solving a Nonlinear Equation.- Matrix Equations.- Eigenvalue Problems.- Interpolation.- Numerical Integration.- Initial Value Problems.- Optimization: Regression.- Optimization: Descent Methods.- Data Analysis.- Taylor's Theorem.- Vector and Matrix Summary.- Answers.- References.- Index.

Mark Holmes is a Professor at Rensselaer Polytechnic Institute.  His current research interests include mechanoreception and sleep-wake cycles. Professor Holmes has three published books in Springer's Texts in Applied Mathematics series: Introduction to Perturbation Methods, Introduction to the Foundations of Applied Mathematics, and Introduction to Numerical Methods in Differential Equations.