
Environmental Data Analysis with MatLab or Python
Principles, Applications, and Prospects
Description
Key Features
- Provides a clear learning path for researchers and students using data analysis techniques which build upon one another, choosing the right order of presentation to substantially aid the reader in learning material
- Includes crib sheets to summarize the most important data analysis techniques, results, procedures, and formulas and worked examples to demonstrate techniques
- Uses real-world environmental examples and case studies formulated using the readily-available software environment in both MATLAB® and Python
- Completely updated and expanded to include coverage of Python and reorganized for better navigability
- Includes access to both an instructor site with exemplary lectures and solutions to problems and a supplementary site with MATLAB LiveScripts and Python Notebooks
Readership
Researchers and students in upper-level undergraduate or graduate courses in environmental data
Table of Contents
1. Data Analysis with MATLAB or Python
2. Systematic explorations of a new dataset
3. Modeling observational noise with random variables
4. Linear models as the foundation of data analysis
5. Least squares with prior information
6. Detecting periodicities with Fourier analysis
7. Modeling time-dependent behavior with filters
8. Undirected data analysis using factors, empirical orthogonal functions and clusters
9. Detecting and understanding correlations among data
10. Interpolation, Gaussian Process Regression and Kriging
11. Approximate methods, including linearization and artificial neural networks
12. Assessing the significance of results
Product details
- No. of pages: 466
- Language: English
- Copyright: © Academic Press 2022
- Published: August 15, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323955768
About the Author
William Menke
Affiliations and Expertise
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