Introduction to environmental data science / Jerry D. Davis.
By: Davis, Jerry D [author.].
Publisher: Florida : CRC, 2023Description: xx, 382p.ISBN: 9781032330341.Subject(s): Environmental sciences -- Data processing | R (Computer program language)DDC classification: 363.700285 Summary: "Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; statistics & modelling ranging from exploratory to modelling, considering confirmatory statistics and extending to machine learning models; time series analysis, focusing especially on carbon and micrometeorological flux; and communication. Introduction to Environmental Data Science. It is an ideal textbook to teach undergraduate to graduate level students in environmental science, environmental studies, geography, earth science, and biology, but can also serve as a reference for environmental professionals working in consulting, NGOs, and government agencies at the local, state, federal, and international levels"--| Item type | Current location | Call number | Status | Date due | Barcode |
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NASSDOC Library | 363.700285 DAV-I (Browse shelf) | Available | 52816 |
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| 363.7 SEB-G Global environmental challenges and solutions | 363.7 SRI-V Vaishvik prakratik paryavaran | 363.70015193 GAM; Game practice and the environment | 363.700285 DAV-I Introduction to environmental data science / | 363.70071 SCI- Science and environmental education : | 363.700721 KAN-R Research Methods for Environmental Studies: A Social Science Approach | 363.7009 HIS- A history of environmentalism : |
Includes bibliographical references and index.
"Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; statistics & modelling ranging from exploratory to modelling, considering confirmatory statistics and extending to machine learning models; time series analysis, focusing especially on carbon and micrometeorological flux; and communication. Introduction to Environmental Data Science. It is an ideal textbook to teach undergraduate to graduate level students in environmental science, environmental studies, geography, earth science, and biology, but can also serve as a reference for environmental professionals working in consulting, NGOs, and government agencies at the local, state, federal, and international levels"--
English.
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