Data Science for the Geosciences

Lijing Wang, David Zhen Yin,, Jef Caers

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.

ScienceScience · high confidenceScience81 signalsLlm Classifier: Science -> ScienceGoogle Books: Science -> ScienceAward Category: PROSE Award for Earth and Environmental Sciences -> Arts & CriticismClimate, Weather & DisasterScience & DiscoveryArt, Music & PerformanceEducation & Universities
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies

This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.

  1. 01 Central figures

    None extracted with sufficient confidence.

  2. 02 Central places

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  3. 03 Suggested argument

    No argument inferred with sufficient confidence.

  4. 04 Reading orientation

    45 / 100 academic

    Trade / academic crossover

    An estimate of intended readership and scholarly apparatus—not quality or importance.

Confidence percentages are the model's own estimates. Profile confidence: 35%.