Introduction To Environmental Data Science


Introduction To Environmental Data Science
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Introduction To Environmental Data Science


Introduction To Environmental Data Science
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Author : Jerry Davis
language : en
Publisher: CRC Press
Release Date : 2023-03-13

Introduction To Environmental Data Science written by Jerry Davis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-13 with Business & Economics categories.


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


Introduction To Environmental Data Science
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Author : William Wei Hsieh
language : en
Publisher:
Release Date : 2023

Introduction To Environmental Data Science written by William Wei Hsieh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Environmental management categories.


"Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography; pattern recognition for satellite images from remote sensing; management of agriculture and forests; assessment of climate change; and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data. William W. Hsieh is a professor emeritus in the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia. Known as a pioneer in introducing machine learning to environmental science, he has written over 100 peer-reviewed journal papers on climate variability, machine learning, atmospheric science, oceanography, hydrology and agricultural science. He is the author of the book Machine Learning Methods in the Environmental Sciences (2009, Cambridge University Press), the first single-authored textbook on machine learning for environmental scientists. Currently retired in Victoria, British Columbia, he enjoys growing organic vegetables"--



Introduction To Environmental Data Science


Introduction To Environmental Data Science
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Author : Jerry D. Davis
language : en
Publisher:
Release Date : 2023

Introduction To Environmental Data Science written by Jerry D. Davis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Environmental sciences categories.


"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"--



Introduction To Environmental Data Science


Introduction To Environmental Data Science
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Author : William W. Hsieh
language : en
Publisher: Cambridge University Press
Release Date : 2023-03-31

Introduction To Environmental Data Science written by William W. Hsieh and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-31 with Computers categories.


A comprehensive guide to machine learning and statistics for students and researchers of environmental data science.



Introduction To Environmental Data Analysis And Modeling


Introduction To Environmental Data Analysis And Modeling
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Author : Moses Eterigho Emetere
language : en
Publisher: Springer Nature
Release Date : 2020-01-03

Introduction To Environmental Data Analysis And Modeling written by Moses Eterigho Emetere and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-03 with Technology & Engineering categories.


This book introduces numerical methods for processing datasets which may be of any form, illustrating adequately computational resolution of environmental alongside the use of open source libraries. This book solves the challenges of misrepresentation of datasets that are relevant directly or indirectly to the research. It illustrates new ways of screening datasets or images for maximum utilization. The adoption of various numerical methods in dataset treatment would certainly create a new scientific approach. The book enlightens researchers on how to analyse measurements to ensure 100% utilization. It introduces new ways of data treatment that are based on a sound mathematical and computational approach.



Environmental Data Analysis


Environmental Data Analysis
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Author : Carsten Dormann
language : en
Publisher: Springer Nature
Release Date : 2020-12-20

Environmental Data Analysis written by Carsten Dormann and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-20 with Medical categories.


Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems. The reader will be introduced to statistical distributions early on, and will learn to deploy models suitable for the data at hand, which in environmental science are often not normally distributed. To make the initially steep learning curve more manageable, each statistical chapter is followed by a walk-through in a corresponding R-based how-to chapter, which reviews the theory and applies it to environmental data. In this way, a coherent and expandable foundation in parametric statistics is laid, which can be expanded in advanced courses.The content has been “field-tested” in several years of courses on statistics for Environmental Science, Geography and Forestry taught at the University of Freiburg.



Analyzing Environmental Data


Analyzing Environmental Data
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Author : Walter W. Piegorsch
language : en
Publisher: John Wiley & Sons
Release Date : 2005-03-04

Analyzing Environmental Data written by Walter W. Piegorsch and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-03-04 with Mathematics categories.


Environmental statistics is a rapidly growing field, supported by advances in digital computing power, automated data collection systems, and interactive, linkable Internet software. Concerns over public and ecological health and the continuing need to support environmental policy-making and regulation have driven a concurrent explosion in environmental data analysis. This textbook is designed to address the need for trained professionals in this area. The book is based on a course which the authors have taught for many years, and prepares students for careers in environmental analysis centered on statistics and allied quantitative methods of data evaluation. The text extends beyond the introductory level, allowing students and environmental science practitioners to develop the expertise to design and perform sophisticated environmental data analyses. In particular, it: Provides a coherent introduction to intermediate and advanced methods for modeling and analyzing environmental data. Takes a data-oriented approach to describing the various methods. Illustrates the methods with real-world examples Features extensive exercises, enabling use as a course text. Includes examples of SAS computer code for implementation of the statistical methods. Connects to a Web site featuring solutions to exercises, extra computer code, and additional material. Serves as an overview of methods for analyzing environmental data, enabling use as a reference text for environmental science professionals. Graduate students of statistics studying environmental data analysis will find this invaluable as will practicing data analysts and environmental scientists including specialists in atmospheric science, biology and biomedicine, chemistry, ecology, environmental health, geography, and geology.



Environmental Data Analysis With Matlab


Environmental Data Analysis With Matlab
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Author : William Menke
language : en
Publisher: Elsevier
Release Date : 2011-09-02

Environmental Data Analysis With Matlab written by William Menke and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-02 with Computers categories.


"Environmental Data Analysis with MatLab" is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. It is well written and outlines a clear learning path for researchers and students. It uses real world environmental examples and case studies. It has MatLab software for application in a readily-available software environment. Homework problems help user follow up upon case studies with homework that expands them.



Introduction To Environmental Data Science


Introduction To Environmental Data Science
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Author : Jerry Davis
language : en
Publisher: CRC Press
Release Date : 2023-03-13

Introduction To Environmental Data Science written by Jerry Davis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-13 with Business & Economics categories.


• 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.



An Introduction To Data Science


An Introduction To Data Science
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Author : Jeffrey S. Saltz
language : en
Publisher: SAGE Publications
Release Date : 2017-08-25

An Introduction To Data Science written by Jeffrey S. Saltz and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-25 with Social Science categories.


An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language and RStudio® platform to make data science welcoming and accessible for all learners. After introducing the basics of data science, the book builds on each previous concept to explain R programming from the ground up. Readers will learn essential skills in data science through demonstrations of how to use data to construct models, predict outcomes, and visualize data.