Environmental Data Analysis with MATLAB, Third Edition, is a new edition that expands fundamentally on the original with an expanded tutorial approach, more clear organization, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios, including case studies in each chapter. The new edition is expanded to include applications to Python, an open source software environment.
Significant content in Environmental Data Analysis with MATLAB, Third Edition is devoted to teaching how the programs can be effectively used in an environmental data analysis setting. This new edition offers chapters that can both be used as self-contained resources or as a step-by-step guide for students, and is supplemented with data and scripts to demonstrate relevant use cases.
- 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
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
William Menke is a Professor of Earth and Environmental Sciences at Columbia University. His research focuses on the development of data analysis algorithms for time series analysis and imaging in the earth and environmental sciences and the application of these methods to volcanoes, earthquakes, and other natural hazards. He has thirty years of experience teaching data analysis methods to both undergraduates and graduate students. Relevant courses that he has taught include, at the undergraduate level, Environmental Data Analysis and The Earth System, and at the graduate level, Geophysical Inverse Theory, Quantitative Methods of Data Analysis, Geophysical Theory and Practical Seismology.
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