Search results for "Introduction to Statistical Data Science"
Items we need to order for you
Introduction to Environmental Data Science
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.
R 3,541.58
Data Science and Human-Environment Systems
Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.
R 5,554.92
Introduction to Environmental Modeling and R
R is fast becoming ubiquitous in the environmental sciences to analyse data. This book introduces environmental modeling and R. It assumes no background in either coding or calculus. It offers real-world examples, fully described programs, and detailed exercises. Readers learn how to analyse large datasets, create beautiful images, thoughtfully utilize the benefits of AI, and use techniques like optimization and sensitivity analysis in their modelling of complex environmental systems. Using examples from a range of environmental topics – including ecology, conservation, and climate science - the book will interest readers from a broad range of environmental and conservation sciences. Most graduate programs in environmental science and sustainability use R because it is both open source and powerful. R is common in government and consulting work, so students that go on to more advanced environmental modelling courses and potentially careers in the environmental field will find a grounding in R very useful.
R 2,429.43
Think, Do, and Communicate Environmental Science
Many students find it daunting to move from studying environmental science, to designing and implementing their own research proposals. This book provides a practical introduction to help develop scientific thinking, aimed at undergraduate and new graduate students in the earth and environmental sciences. Students are guided through the steps of scientific thinking using published scientific literature and real environmental data. The book starts with advice on how to effectively read scientific papers, before outlining how to articulate testable questions and answer them using basic data analysis. The Mauna Loa CO2 dataset is used to demonstrate how to read metadata, prepare data, generate effective graphs and identify dominant cycles on various timescales. Practical, question-driven examples are explored to explain running averages, anomalies, correlations and simple linear models. The final chapter provides a framework for writing persuasive research proposals, making this an essential guide for students embarking on their first research project.
R 3,047.34
Think, Do, and Communicate Environmental Science
Many students find it daunting to move from studying environmental science, to designing and implementing their own research proposals. This book provides a practical introduction to help develop scientific thinking, aimed at undergraduate and new graduate students in the earth and environmental sciences. Students are guided through the steps of scientific thinking using published scientific literature and real environmental data. The book starts with advice on how to effectively read scientific papers, before outlining how to articulate testable questions and answer them using basic data analysis. The Mauna Loa CO2 dataset is used to demonstrate how to read metadata, prepare data, generate effective graphs and identify dominant cycles on various timescales. Practical, question-driven examples are explored to explain running averages, anomalies, correlations and simple linear models. The final chapter provides a framework for writing persuasive research proposals, making this an essential guide for students embarking on their first research project.
R 1,755.13
Introduction to Catastrophe Risk Modelling
Focusing on the physics of the catastrophe process and addressed directly to advanced students, this innovative textbook quantifies dozens of perils, both natural and man-made, and covers the latest developments in catastrophe modelling. Combining basic statistics, applied physics, natural and environmental sciences, civil engineering, and psychology, the text remains at an introductory level, focusing on fundamental concepts for a comprehensive understanding of catastrophe phenomenology and risk quantification. A broad spectrum of perils are covered, including geophysical, hydrological, meteorological, climatological, biological, extraterrestrial, technological and socio-economic, as well as events caused by domino effects and global warming. Following industry standards, the text provides the necessary tools to develop a CAT model from hazard to loss assessment. Online resources include a CAT risk model starter-kit and a CAT risk modelling 'sandbox' with Python Jupyter tutorial. Every process, described by equations, (pseudo)codes and illustrations, is fully reproducible, allowing students to solidify knowledge through practice.
R 2,513.00
Introduction to Catastrophe Risk Modelling
Focusing on the physics of the catastrophe process and addressed directly to advanced students, this innovative textbook quantifies dozens of perils, both natural and man-made, and covers the latest developments in catastrophe modelling. Combining basic statistics, applied physics, natural and environmental sciences, civil engineering, and psychology, the text remains at an introductory level, focusing on fundamental concepts for a comprehensive understanding of catastrophe phenomenology and risk quantification. A broad spectrum of perils are covered, including geophysical, hydrological, meteorological, climatological, biological, extraterrestrial, technological and socio-economic, as well as events caused by domino effects and global warming. Following industry standards, the text provides the necessary tools to develop a CAT model from hazard to loss assessment. Online resources include a CAT risk model starter-kit and a CAT risk modelling 'sandbox' with Python Jupyter tutorial. Every process, described by equations, (pseudo)codes and illustrations, is fully reproducible, allowing students to solidify knowledge through practice.
R 6,262.32
Numerical Simulations in the Environmental and Earth Sciences
Increases in computer power and technology have rapidly advanced the applications of numerical modelling in environmental and earth sciences. The progress of numerical modelling in atmospheric, oceanic and geophysical sciences was the topic of an international conference held by the National Autonomous University of Mexico. The review articles and research papers in this volume constitute a wide-ranging account of modelling environmental and earth processes through a variety of numerical simulations. Sections are devoted to such topics as general circulation models and global change, methods of geophysical data assimilation, and mathematical and computational methods with geophysical applications. This book forms an excellent introduction and overview for graduate students as well as a critical update for researchers.
R 5,641.65
