Multidimensional Data Visualization

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

Chunyan Li is a course instructor with many years of experience in teaching about time series analysis. His book is essential for students and researchers in oceanography and other subjects in the Earth sciences, looking for a complete coverage of the theory and practice of time series data analysis using MATLAB. This textbook covers the topic's core theory in depth, and provides numerous instructional examples, many drawn directly from the author's own teaching experience, using data files, examples, and exercises. The book explores many concepts, including time; distance on Earth; wind, current, and wave data formats; finding a subset of ship-based data along planned or random transects; error propagation; Taylor series expansion for error estimates; the least squares method; base functions and linear independence of base functions; tidal harmonic analysis; Fourier series and the generalized Fourier transform; filtering techniques: sampling theorems: finite sampling effects; wavelet

This book brings together papers that offer conceptual analyses, highlight issues, propose solutions, and discuss practices regarding privacy, data protection and enforcing rights in a changing world. It is one of the results of the 14th annual International Conference on Computers, Privacy and Data Protection (CPDP), which took place online in January 2021. The pandemic has produced deep and ongoing changes in how, when, why, and the media through which, we interact.Many of these changes correspond to new approaches in the collection and use of our data - new in terms of scale, form, and purpose. This raises difficult questions as to which rights we have, and should have, in relation to such novel forms of data processing, the degree to which these rights should be balanced against other poignant social interests, and how these rights should be enforced in light of the fluidity and uncertainty of circumstances. The book covers a range of topics, such as: digital sovereignty; art and a

Statistical Methods for Reliability Data, Second Edition (SMRD2) is an essential guide to the most widely used and recently developed statistical methods for reliability data analysis and reliability test planning. Written by three experts in the area, SMRD2 updates and extends the long- established statistical techniques and shows how to apply powerful graphical, numerical, and simulation-based methods to a range of applications in reliability. SMRD2 is a comprehensive resource that describes maximum likelihood and Bayesian methods for solving practical problems that arise in product reliability and similar areas of application. SMRD2 illustrates methods with numerous applications and all the data sets are available on the book’s website. Also, SMRD2 contains an extensive collection of exercises that will enhance its use as a course textbook.The SMRD2's website contains valuable resources, including R packages, Stan model codes, presentation slides, technical notes, information about

Interpreting Subsurface Seismic Data presents recent advances in methodologies for seismic imaging and interpretation across multiple applications in geophysics, including exploration, marine geology

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar

The most fascinating and profitable subject of predictive algorithms is the human actor. Analysing big data through learning algorithms to predict and pre-empt individual decisions gives a powerful tool to corporations, political parties and the state. Algorithmic analysis of digital footprints, as an omnipresent form of surveillance, has already been used in diverse contexts: behavioural advertising, personalised pricing, political micro-targeting, precision medicine, and predictive policing and prison sentencing. This volume brings together experts to offer philosophical, sociological, and legal perspectives on these personalised data practices. It explores common themes such as choice, personal autonomy, equality, privacy, and corporate and governmental efficiency against the normative frameworks of the market, democracy and the rule of law. By offering these insights, this collection on data-driven personalisation seeks to stimulate an interdisciplinary debate on one of the most pe

Jer Thorp's analysis of the word "data" in 10,325 New York Times stories written between 1984 and 2018 shows a distinct trend: among the words most closely associated with "data," we find not only its

Considering the overall situation of the current pandemic and pertinent recommendations, this book focuses on the use of Augmented Reality (AR) applications for COVID-19 outbreak along with techniques, tools and platforms to achieve social distancing and sanitization. It contains theoretical and practical knowledge of AR and remedies on how to cope with the technology including multiple use cases along with a set of recommendations. It illustrates application building using open source softwares with an interactive interface to aid the impaired users. The initial part of the book emphasizes on the basic knowledge of AR, technology, devices and rest of the relevant theories. Aimed at Researchers, students in augmented reality, technical healthcare professionals, practitioners, this book: Focusses on realization and visualization of Covid-19 pandemic using Augmented Reality (AR).Discusses how different AR Visualization techniques is implemented in different scenarios.Provides in-depth kn

Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To

Direct from Microsoft, this Exam Ref is the official study guide for the new Microsoft DP-200 Implementing an Azure Data Solution certification exam. Exam Ref DP-200 Implementing an Azure Data Soluti

《極端金融風險》內容全面系統，既有很高的實用價值，又有很強的資料收藏價值，涵蓋了The Multidimensional Nature of Risk and Dependence、Emergence of Dependence Structures in the Stock Markets、Discussion and Conclusions等內容，各章結構合理，層次清楚、敘述詳細、文字流暢，非

Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method w

Work with data like a pro using this guide that breaks down how to organize, apply, and most importantly, understand what you are analyzing in order to become a true data ninja.From the stock market t

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure.Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.

This timely book examines crucial developments in the field of privacy law, efforts by legal systems to impose their data protection standards beyond their borders and claims by states to assert sover

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book introduces you to the Wolfram programming langua

數據可視化(Data Visualization)是借助於圖形化手段，清晰有效地傳達與溝通信息的重要技術，是數據加工和處理的基本方法之一，也是大數據開發與應用的最終環節和最重要的環節之一。它通過圖形、圖像、表格等技術更為直觀地表達數據，從而為發現數據的隱含規律提供技術手段，並輔助用戶分析數據，提供決策支撐服務。 本書共8章，第一章介紹數據可視化的基本概念、特徵、目標、作用，可視化的流程以及數據可

This book brings together papers that offer conceptual analyses, highlight issues, propose solutions, and discuss practices regarding privacy, data protection and Artificial Intelligence. It is one of

FloodZone is Anastasia Samoylova’s photographic account of life on the climatic knife-edge of the southern United States. Serious climate change is upon us, but this is not a visualization of disaster

The real world is perceived and broken down as data, models and algorithms in the eyes of physicists and engineers. Data is noisy by nature and classical statistical tools have so far been successful in dealing with relatively smaller levels of randomness. The recent emergence of Big Data and the required computing power to analyse them have rendered classical tools outdated and insufficient. Tools such as random matrix theory and the study of large sample covariance matrices can efficiently process these big data sets and help make sense of modern, deep learning algorithms. Presenting an introductory calculus course for random matrices, the book focusses on modern concepts in matrix theory, generalising the standard concept of probabilistic independence to non-commuting random variables. Concretely worked out examples and applications to financial engineering and portfolio construction make this unique book an essential tool for physicists, engineers, data analysts, and economists.

First published as What is Quantitative Longitudinal Data Analysis?, this title is now available as part of the Bloomsbury Research Methods series.Across the social sciences, there is widespread agree

First published as What is Quantitative Longitudinal Data Analysis?, this title is now available as part of the Bloomsbury Research Methods series.Across the social sciences, there is widespread agree

The integrity of democratic elections, both in the United States and abroad, is an important problem. In this Element, we present a data-driven approach that evaluates the performance of the administration of a democratic election, before, during, and after Election Day. We show that this data-driven method can help to improve confidence in the integrity of American elections.

Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

Categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. This title presents various ways of extracting real-life conclusions from

In situ hybridization is a technique that allows for the visualization of specific DNA and RNA sequences in individual cells, and is an especially important method for studying nucleic acids in hetero

This second edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial

This groundbreaking book explores the new legal and economic challenges triggered by big data, and analyses the interactions among and between intellectual property, competition law, free speech, priv

Data, Matter, Design presents a comprehensive overview of current design processes that rely on the input of data and use of computational design strategies, and their relationship to an array of

Data, Matter, Design presents a comprehensive overview of current design processes that rely on the input of data and use of computational design strategies, and their relationship to an array of

Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable

Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable

Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of

Two hot topics in recent years are data analytics and AI. Unfortunately, both communities have not done been communicating and collaborating with each other to build the necessary synergies. This book

Two hot topics in recent years are data analytics and AI. Unfortunately, both communities have not done been communicating and collaborating with each other to build the necessary synergies. This book

A wake-up call for America to create a new framework for democratizing data.Public data are foundational to our democratic system. People need consistently high-quality information from trustworthy so

Senza data (Undated), an exhibition at the Stefano Bardini Museum in Florence, presents a series of works by Italian artist Luca Pignatelli painted on railway tarpaulins, wood, paper, sheet metal, and

All too often, individuals engaged in the biomedical sciences assume that numeric data must be left to the proper authorities (e.g., statisticians and data analysts) who are trained to apply sophisticated mathematical algorithms to sets of data. This is a terrible mistake. Individuals with keen observational skills, regardless of their mathematical training, are in the best position to draw correct inferences from their own data and to guide the subsequent implementation of robust, mathematical analyses. Volume 2 of Logic and Critical Thinking in the Biomedical Sciences provides readers with a repertoire of deductive non-mathematical methods that will help them draw useful inferences from their own data.Volumes 1 and 2 of Logic and Critical Thinking in the Biomedical Sciences are written for biomedical scientists and college-level students engaged in any of the life sciences, including bioinformatics and related data sciences.