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Discover more about data (view source)
Revision as of 16:58, 6 February 2022
, 16:58, 6 February 2022no edit summary
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Intro-to-data-science.jpg|150px|Introduction to data science]]
<h3 style="text-decoration:none;">Introduction to data science: data analysis and prediction algorithms with R</h3>
<p class="author">by Rafael Irizarry</p>
<p>Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist's experience.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Intro-to-functional-data-analysis.jpg|150px|Introduction to functional data analysis]]
<h3 style="text-decoration:none;">Introduction to functional data analysis</h3>
<p class="author">by Piotr Kokoszka and Matthew Reimherr</p>
<p>Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Doing-Bayesian-data-analysis.jpg|150px|Doing Bayesian data analysis]]
<h3 style="text-decoration:none;">Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan</h3>
<p class="author">by John K Kruschke</p>
<p>Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan. Amsterdam, Academic Press.
Provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Data-assimilation.jpg|150px|Data Assimilation]]
<h3 style="text-decoration:none;">Data Assimilation: A Mathematical Introduction (Texts in Applied Mathematics Book 62)</h3>
<p class="author">by Kody Law, Andrew Stuart, and Konstantinos Zygalakis</p>
<p>This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, together with the algorithms which are introduced and discussed, are all illustrated by the MATLAB software detailed in the book and made freely available online. The book is organized into nine chapters: the first contains a brief introduction to the mathematical tools around which the material is organized; the next four are concerned with discrete time dynamical systems and discrete time data; the last four are concerned with continuous time dynamical systems and continuous time data and are organized analogously to the corresponding discrete time chapters. This book is aimed at mathematical researchers interested in a systematic development of this interdisciplinary field, and at researchers from the geosciences, and a variety of other scientific fields, who use tools from data assimilation to combine data with time-dependent models. </p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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