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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 W. Hsieh
language : en
Publisher: Cambridge University Press
Release Date : 2023-03-23

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 2023-03-23 with Computers categories.


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



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 : 2023-03-23

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 2023-03-23 with Science 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 is 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.



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 : 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"--



Data Science For Sensory And Consumer Scientists


Data Science For Sensory And Consumer Scientists
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Author : Thierry Worch
language : en
Publisher: CRC Press
Release Date : 2023-09-29

Data Science For Sensory And Consumer Scientists written by Thierry Worch and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-29 with Business & Economics categories.


Data Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences. Key Features: • Elucidation of data scientific workflow. • Introduction to reproducible research. • In-depth coverage of data-scientific topics germane to sensory and consumer science. • Examples based in industrial practice used throughout the book



Practitioner S Guide To Data Science


Practitioner S Guide To Data Science
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Author : Hui Lin
language : en
Publisher: CRC Press
Release Date : 2023-05-24

Practitioner S Guide To Data Science written by Hui Lin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-24 with Business & Economics categories.


This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!



Environmental Software Systems Data Science In Action


Environmental Software Systems Data Science In Action
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Author : Ioannis N. Athanasiadis
language : en
Publisher: Springer Nature
Release Date : 2020-01-29

Environmental Software Systems Data Science In Action written by Ioannis N. Athanasiadis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-29 with Computers categories.


This book constitutes the refereed proceedings of the 13th IFIP WG 5.11 International Symposium on Environmental Software Systems, ISESS 2020, held in Wageningen, The Netherlands, in February 2020. The 22 full papers and 3 short papers were carefully reviewed and selected from 29 submissions. The papers cover a wide range of topics on environmental informatics, including data mining, artificial intelligence, high performance and cloud computing, visualization and smart sensing for environmental, earth, agricultural and food applications.



Data Science For Water Utilities


Data Science For Water Utilities
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Author : Peter Prevos
language : en
Publisher: CRC Press
Release Date : 2023-05-10

Data Science For Water Utilities written by Peter Prevos and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-10 with Computers categories.


This addition to the Data Science Series introduces the principles of data science and the R language to the singular needs of water professionals. The book provides unique data and examples relevant to managing water utility and is sourced from the author’s extensive experience. Data Science for Water Utilities: Data as a Source of Value is an applied, practical guide that shows water professionals how to use data science to solve urban water management problems. Content develops through four case studies. The first looks at analysing water quality to ensure public health. The second considers customer feedback. The third case study introduces smart meter data. The guide flows easily from basic principles through code that, with each case study, increases in complexity. The last case study analyses data using basic machine learning. Readers will be familiar with analysing data but do not need coding experience to use this book. The title will be essential reading for anyone seeking a practical introduction to data science and creating value with R.



Introduction To Biomedical Data Science


Introduction To Biomedical Data Science
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Author : Robert Hoyt
language : en
Publisher: Lulu.com
Release Date : 2019-11-24

Introduction To Biomedical Data Science written by Robert Hoyt and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-24 with Science categories.


Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises.