Publisher Description

The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientificcomputing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from:· statistics,· time-frequency analysis, and ·low-dimensional reductions The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of -Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.

Table of Contents

I Basic Computations and Visualization1: MATLAB Introduction2: Linear Systems3: Curve Fitting4: Numerical Differentiation and Integration5: Basic Optimization6: VisualizationII Differential and Partial Differential Equations7: Initial and Boundary Value Problems of Differential Equations1448: Finite Difference Methods9: Time and Space Stepping Schemes: Method of Lines10: Spectral Methods11: Finite Element MethodsIII Computational Methods for Data Analysis12: Statistical Methods and Their Applications13: Time-Frequency Analysis: Fourier Transforms and Wavelets14: Image Processing and Analysis15: Linear Algebra and Singular Value Decomposition16: Independent Component Analysis17: Image Recognition18: Basics of Compressed Sensing19: Dimensionality Reduction for Partial Differential Equations20: Dynamic Mode Decomposition21: Data Assimilation Methods22: Equation Free ModelingIV Scientific Applications23: Applications of Differential Equations and Boundary Value Problems24: Quantum Mechanics25: Applications of Partial Differential Equations26: Applications of Data Analysis

Review

The book allows methods for dealing with large data to be explained in a logical process suitable for both undergraduate and post-graduate students ... With sport performance analysis evolving into deal with big data, the book forms a key bridge between mathematics and sport science * John Francis, University of Worcester *

Long Description

The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientific
computing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from:· statistics,· time-frequency analysis, and · low-dimensional reductions The blend of these ideas provides meaningful insight into the data
sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various -Driven Modeling and Scientific Computation is a survey of practical
numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text,
this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.

Review Quote

The book allows methods for dealing with large data to be explained in a logical process suitable for both undergraduate and post-graduate students ... With sport performance analysis evolving into deal with big data, the book forms a key bridge between mathematics and sport science

Feature

First book focused on integration of scientific computing with data analysis
Complete integration with MATLAB
Contains standalone sections which make it ideal for various courses and purposes
Includes examples that provide full algorithm development and implementation
Application of the scientific computing and data analysis techniques to real problems

Details