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 and 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 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.
Features
Gives thorough consideration of the needs for environmental research in both spatial and temporal domains.
Features examples of applications involving field-collected data ranging from individual observations to data logging.
Includes examples also of applications involving government and NGO sources, ranging from satellite imagery to environmental data collected by regulators such as EPA.
Contains class-tested exercises in all chapters other than case studies. Solutions manual available for instructors.
All examples and exercises make use of a GitHub package for functions and especially data.
| ISBN: | 9781032322186 |
| Publication date: | 13th March 2023 |
| Author: | Jerry D Davis |
| Publisher: | Chapman & Hall/CRC an imprint of CRC Press |
| Format: | Hardback |
| Pagination: | 399 pages |
| Series: | Chapman & Hall/CRC Data Science Series |
| Genres: |
Probability and statistics |
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 and 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 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.
Features
Gives thorough consideration of the needs for environmental research in both spatial and temporal domains.
Features examples of applications involving field-collected data ranging from individual observations to data logging.
Includes examples also of applications involving government and NGO sources, ranging from satellite imagery to environmental data collected by regulators such as EPA.
Contains class-tested exercises in all chapters other than case studies. Solutions manual available for instructors.
All examples and exercises make use of a GitHub package for functions and especially data.
Introduction to Environmental Data Science features in the following genres: Probability and statistics
Introduction to Environmental Data Science is available in Hardback
Introduction to Environmental Data Science was written by Jerry D Davis and published by Chapman & Hall/CRC an imprint of CRC Press
Introduction to Environmental Data Science has 399 pages
Yes it is part of Chapman & Hall/CRC Data Science Series series
£75.59