This book provides thorough and comprehensive coverage of most of the new and important quantitative methods of data analysis for graduate students and practitioners. In recent years, data analysis methods have exploded alongside advanced computing power, and it is critical to understand such methods to get the most out of data, and to extract signal from noise. The book excels in explaining difficult concepts through simple explanations and detailed explanatory illustrations. Most unique is the focus on confidence limits for power spectra and their proper interpretation, something rare or completely missing in other books. Likewise, there is a thorough discussion of how to assess uncertainty via use of Expectancy, and the easy to apply and understand Bootstrap method. The book is written so that descriptions of each method are as self-contained as possible. Many examples are presented to clarify interpretations, as are user tips in highlighted boxes.
- Develops new, unfamiliar methods from basic principles
- Features detailed illustrations to clarify difficult concepts
- Self-contained chapters stand alone as individual resources
Read more
Reviews & endorsements
'This text is suitable for undergraduates and graduates, as well as seasoned scientists and engineers seeking to broaden their statistical skills. It will have lasting value as it is comprehensive, containing detailed explanations of a wide range of statistical methods. The book is clearly written by a meticulous scientist who is an expert in the field and an award winning teacher.' James Hays, Columbia University, New York
'At last: a guide for getting the most out of your data analysis while avoiding the many pitfalls, hazards and common mistakes. This book is an invaluable and inspired opus on the fundamentals of quantitative data analysis. It is both comprehensive and illuminating, with many a nugget of enlightened wisdom, as well as succinctly summarized 'take-home' points in each and every section. A very accessible must-have guide for exploring data in the most informed way, and a gem of a textbook for students, teachers and practitioners alike.' Sharon Stammerjohn, University of Colorado, Boulder
'Coherent book-length treatments are so valuable in the Data Age: the internet is full of algorithms - but described flatly, and in myriad notations and nomenclatures. This long-time teacher's lucid text expresses the spirit and strategy of data analysis, as well as the details. Boxes set off optional advanced derivations, appendices survey matrix algebra and uncertainty analysis, and the chapters aim for standalone readability, making this a valuable reference as well as a flexible textbook (with questions). Spectral estimation is especially well covered.' Brian Mapes, University of Miami
'This is a competent development of many data analysis methods … Overall, the book is the outgrowth of teaching the subject for 30 years, which shows in the well-developed, clear narrative descriptions accompanying the theory.' D. A. Vaccari, Choice
See more reviews
Customer reviews

17th Oct 2024 by UName-21846
The best book about data processing in Earth sciences since a long time. Congratulations.

Log in to review

Review was not posted due to profanity
×

, create a review

(If you're not , sign out)

Please enter the right captcha value

Please enter a star rating.

Your review must be a minimum of 12 words.

How do you rate this item?

Reviews must contain at least 12 words about the product.

×

Product details
- Date Published: September 2018
- format: Hardback
- isbn: 9781107029767
- length: 626 pages
- dimensions: 253 x 178 x 33 mm
- weight: 1.38kg
- availability: Available
Table of Contents
Part I. Fundamentals:
1. The nature of data and analysis
2. Probability theory
3. Statistics
Part II. Fitting Curves to Data
4. Interpolation
5. Smoothed curve fitting
6. Special curve fitting
Part III. Sequential Data Fundamentals:
7. Serial products
8. Fourier series
9. Fourier transform
10. Fourier sampling theory
11. Spectral analysis
12. Cross spectral analysis
13. Filtering and deconvolution
14. Linear parametric models
15. Empirical orthogonal function (EOF) analysis
A1. Overview of matrix algebra
A2. Uncertainty analysis
References
Index.
Look Inside

Lecturer Resources

Figures

Find resources associated with this title

Back to resources home

Type	Name	Unlocked *	Format	Size

Showing of

This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to lecturers whose faculty status has been verified. To gain access to locked resources, lecturers should sign in to or register for a Cambridge user account.

Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other lecturers may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.

Supplementary resources are subject to copyright. Lecturers are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.

If you are having problems accessing these resources please contact [email protected].

Author
Douglas G. Martinson, Columbia University, New York
Douglas G. Martinson is a Lamont Research Professor in the Division of Ocean and Climate Physics at Columbia University's Lamont-Doherty Earth Observatory. A physical oceanographer who researches the role of polar oceans in global climate, his research involves the collection of a large amount of data and considerable quantitative analysis. He developed the course on Quantitative Methods of Data Analysis as an Adjunct Professor for the Department of Earth and Environmental Sciences at Columbia University, New York, and received an Outstanding Teacher Award in 2004.

This title is available for institutional purchase via Cambridge Core

Related Books

Computational Statistics in the Earth Sciences

With Applications in MATLAB

Data Science for the Geosciences

Stochastic Analysis of Scaling Time Series

From Turbulence Theory to Applications

Bayesian Logical Data Analysis for the Physical Sciences

A Comparative Approach with Mathematica® Support

Products and services

About us

Careers

Unlocking potential with the best learning and research solutions

Quantitative Methods of Data Analysis for the Physical Sciences and Engineering