Statistics and Data Visualization in Climate Science with R and Python
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27 830 Ft
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Product details:
- Publisher Cambridge University Press
- Date of Publication 30 November 2023
- ISBN 9781108842570
- Binding Hardback
- No. of pages458 pages
- Size 260x207x25 mm
- Weight 1160 g
- Language English 558
Categories
Short description:
Comprehensive overview of essential statistical concepts, useful statistical methods, data visualization, and computing tools for the climate and related sciences.
MoreLong description:
A comprehensive overview of essential statistical concepts, useful statistical methods, data visualization, and modern computing tools for the climate sciences and many others such as geography and environmental engineering. It is an invaluable reference for students and researchers in climatology and its connected fields who wish to learn data science, statistics, R and Python programming. The examples and exercises in the book empower readers to work on real climate data from station observations, remote sensing and simulated results. For example, students can use R or Python code to read and plot the global warming data and the global precipitation data in netCDF, csv, txt, or JSON; and compute and interpret empirical orthogonal functions. The book's computer code and real-world data allow readers to fully utilize the modern computing technology and updated datasets. Online supplementary resources include R code and Python code, data files, figure files, tutorials, slides and sample syllabi.
'Statistics and Data Visualization in Climate Science with R and Python by Sam Shen and Jerry North is a fabulous addition to the set of tools for scientists, educators and students who are interested in working with data relevant to climate variability and change ... I can testify that this book is an enormous help to someone like me. I no longer can simply ask my grad students and postdocs to download and analyze datasets, but I still want to ask questions and find data-based answers. This book perfectly fills the 40-year gap since I last had to do all these things myself, and I can't wait to begin to use it ... I am certain that teachers will find the book and supporting materials extremely beneficial as well. Professors Shen and North have created a resource of enormous benefit to climate scientists.' Phillip A. Arkin, University of Maryland
Table of Contents:
1. Basics of Climate Data Arrays, Statistics, and Visualization; 2. Elementary Probability and Statistics; 3. Estimation and Decision Making; 4. Regression Models and Methods; 5. Matrices for Climate Data; 6. Covariance Matrices, EOFs, and PCs; 7. Introduction to Time Series; 8. Spectral Analysis of Time Series; 9. Introduction to Machine Learning; References and Further Reading; Exercises; Index.
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