PDF Download Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data, by J. Nathan Kutz
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Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data, by J. Nathan Kutz
PDF Download Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data, by J. Nathan Kutz
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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:
DT statistics,
DT time-frequency analysis, and
DT 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 problems.
Data-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.
- Sales Rank: #95807 in Books
- Published on: 2013-09-15
- Original language: English
- Number of items: 1
- Dimensions: 7.50" h x 1.30" w x 9.60" l, 3.13 pounds
- Binding: Paperback
- 656 pages
Most helpful customer reviews
11 of 11 people found the following review helpful.
Terrific work, very thorough and engagingly presented
By Ervin Peretz
Excellent, detailed presentation of Singular Value Decomposition and other essentials of computational data science. Strong emphasis on a groundwork for image processing.
Many interesting historical references. Large easy font with great figures and illustrations.
Includes a quick/indirect Linear Algebra review; but you really need to have that beforehand (I recommend Gilbert Strang's intro). Also an intro to Differential Equations would be a prerequisite for some later chapters.
Some of the math is above my level; and I'll be revisiting those chapters. I learned of Dr. Kutz through his online course, which is great; and this led me to his book.
8 of 8 people found the following review helpful.
Nathan is the man
By S. Masry
I have to admit that I owe Nathan alot. I have listened to his online Data Analysis course, and I was amazed as to how he is able to make dry mathematical concepts be accessible, as well as intuitive.
I purchased the book as a companion to the course, and I think that Nathan, should write other books in a similar way, to cover other aspects of mathematics in the same way. For example, optimal estimation, and Kalman filtering, and others.
Kudos Nathan, good job.
3 of 3 people found the following review helpful.
superior learning aid for Matlab
By Michael George
I have just started reading this book, but it has repaid my serious study. It is written in a very clever way, focusing on giving the big picture, while not neglecting considerations related to direct application. The concentration on analysis of illustrative examples really aids self-study. The author displays a brilliance in this book in presenting Matlab and interweaving it with the study of important topics in numerical mathematics. The emphasis is on the mathematics, understanding, and applying numerical methods, rather than strictly in technical details of Matlab. I recommend this book as a learning aid for Matlab, if one is interested in certain areas of numerical analysis that the book addresses. The first part of the book is pretty elementary, but the presentation is interesting and engaging, and aspires, to a certain extent, to serve as a nice survey introductory treatment at the high school and freshman college level of applied mathematics. There is a kind of juvenile cuteness about the first several chapters, but the level does start to pick up when differential equations are discussed. Applied mathematics is establishing itself, more and more, as a competitor for traditional pure mathematics, thanks to the power of modern computation. The problems the applied mathematicians work on are growing in significance and originality, due to the impact of improvements in computers and computational techniques. This is the "experimental" or "engineering" side, perhaps, of mathematics, and it can often be as exciting, clever and original as the "theoretical" or pure side of mathematics. My own feeling is that computational science (as we might call this area of mathematics now) hearkens back to the ancient Pythagoreans, and an interest in understanding and grasping the harmony of the cosmos.
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