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 methodsEncourages both problem- and project-based learning through extensive examples, exercises, and projects drawn from practical applicationsIntroduces the main concepts in modeling, python programming, number representation, and errorsExplains the essential details of numerical calculus, linear, and nonlinear equations, including the multivariable Newton methodDiscusses interpolation and the numerical solution of differential equations, covering polynomial interpolation, splines, and the Euler, Runge–Kutta, and shooting methodsPresents 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.