1. Extreme value statistics; 2. Multi-variate analysis; 3. Spatial data aggregation; 4. Geostatistics; 5. Review of mathematical and statistical concepts.
An accessible text providing data science foundations to address earth science questions using real-world case studies.
Dr. Lijing Wang is a Postdoctoral Research Fellow in the Earth and Environmental Sciences Area at Lawrence Berkeley National Laboratory. She earned her Ph.D. from the Department of Earth and Planetary Sciences at Stanford University. Her research centers on integrating geoscientific data, such as geophysical surveys and in-situ hydrological measurements, with hydrological modeling to develop informed solutions for water resource management. She was a Stanford Data Science Scholar and had been a teaching assistant for the Data Science for Geosciences course at Stanford for over three years. She has received the Harriet Benson Fellowship from Stanford for her exceptional research accomplishments. Dr. Zhen Yin is a research scientist and co-founder of the Stanford Mineral-X (https://mineralx.stanford.edu/) at Stanford Doerr School of Sustainability. His research developments in data science have been implemented in various subjects, including Antarctica topographic modeling, critical mineral explorations in Asia/North America/Africa, and North Sea projects. He was previously a Research Associate at the Edinburgh Time-Lapse Project in Scotland, leading a research collaboration with Equinor from 2016 to 2018, and a Postdoctoral Fellow at the Stanford Chevron Center of Research Excellence from 2018 to 2021. Dr. Jef Caers is Professor of Earth and Planetary Sciences in the Stanford Doerr School of Sustainability. He is the author of a wide range of journal papers across mathematics, statistics, geological sciences, geophysics, engineering and computer science, and four other books. He has received several best paper awards, as well as the 2001 Vistelius Award from the International Association for Mathematical Geosciences (IAMG) and the 2014 Krumbein Medal from the IAMG for his career achievement.
'Literacy in data science and machine learning methods is a
necessity for the modern geoscientist. This is an accessible yet
thorough overview of key data science topics and their
applications. It uses real-world case studies from a variety of
geoscientific disciplines and is a valuable resource for students,
practitioners, and instructors alike.' Emma Mackie, University of
'This condensate of essential notions to deal with data typically found in geoscience offers a great toolbox for students who must perform analysis of big data that are spatially distributed or multivariate, or for the estimation of extreme events.' Gregoire Mariethoz, University of Lausanne