The burgeoning field of data analysis is expanding at an incrediblepace due to the proliferation of data collection in almost every areaof science. The enormous data sets now routinely encountered in thesciences provide an incentive to develop mathematical techniques andcomputational algorithms that help synthesize, interpret and givemeaning to the data in the context of its scientific setting. Aspecific aim of this book is to integrate standard scientific computingmethods with data analysis. By doing so, it brings together, in aself-consistent fashion, the key ideas from:
DT statistics,
DT time-frequency analysis, and
DT low-dimensional reductions
Theblend of these ideas provides meaningful insight into the data sets oneis faced with in every scientific subject today, including thosegenerated from complex dynamical systems. This is a particularlyexciting field and much of the final part of the book is driven byintuitive examples from it, showing how the three areas can be used incombination to give critical insight into the fundamental workings ofvarious problems.

Data-Driven Modeling and ScientificComputation is a survey of practical numerical solution techniques forordinary and partial differential equations as well as algorithms fordata manipulation and analysis. Emphasis is on the implementation ofnumerical schemes to practical problems in the engineering, biologicaland physical sciences.

An accessible introductory-to-advancedtext, this book fully integrates MATLAB and its versatile andhigh-level programming functionality, while bringing togethercomputational and data skills for both undergraduate and graduatestudents in scientific computing.


Paperback: 608 pages
Publisher: Oxford University Press (September 15, 2013)
Language: English
ISBN-10: 0199660344
ISBN-13: 978-0199660346
Product Dimensions:         9.6 x 7.4 x 1.3 inches
Shipping Weight: 2.8 pounds
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