Data Science for the Geosciences

An accessible text providing data science foundations to address earth science questions using real-world case studies.

Lijing Wang (Author), David Zhen Yin (Author), Jef Caers (Author)

9781009201407, Cambridge University Press

Paperback / softback, published 17 August 2023

250 pages
25.3 x 20.3 x 1.4 cm, 0.65 kg

'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.' Grégoire Mariethoz, University of Lausanne

Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide.

1. Extreme value statistics
2. Multi-variate analysis
3. Spatial data aggregation
4. Geostatistics
5. Review of mathematical and statistical concepts.

Subject Areas: Earth sciences [RB]