Data Structures Using C

From a top healthcare futurist, frontline innovator, and Deloitte consultant comes a road map to Humanizing Healthcare--hardwiring humanity at every point of care--a cultural transformation that is good for people and good for business. Without meaning to do so, the healthcare industry continues to injure people--patients and clinicians--physically, emotionally, mentally, financially, spiritually, and socially. There have been numerous efforts to overhaul the system, but nothing has yet cured healthcare of its illnesses. Paramedic-turned-physician executive and Deloitte consultant Summer Knight helps top healthcare and life sciences organizations create patient-centric, consumer-oriented healthcare delivery systems. She not only provides a guide for how the industry can transform to embody a more human perspective, using data and technology, she also illustrates how using Humanizing Healthcare as a collective North Star will positively impact all stakeholders: consumers, providers, car

Learn how today's managers, investors and other business stakeholders effectively use accounting information and reports to guide business success with Warren/Jones/Farmer�s USING FINANCIAL ACC

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

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

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 The recent emergence of Big Data and the required computing power to analyse them have rendered classical tools outdated and insufficient.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

This is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-splines combine regression on B-splines with simple, discrete, roughness penalties. They were introduced by the authors in 1996 and have been used in many diverse applications. The regression basis makes it straightforward to handle non-normal data, like in generalized linear models. The authors demonstrate optimal smoothing, using mixed model technology and Bayesian estimation, in addition to classical tools like cross-validation and AIC, covering theory and applications with code in R. Going far beyond simple smoothing, they also show how to use P-splines for regression on signals, varying-coefficient models, quantile and expectile smoothing, and composite links for grouped data. Penalties are the crucial elements of P-splines; with proper modifications they can handle periodic and circular data as well as shape constraints. Combining penalties with tensor products of B-splines extends these

Downscaling is a widely used technique for translating information from large-scale climate models to the spatial and temporal scales needed to assess local and regional climate impacts, vulnerability, risk and resilience. This book is a comprehensive guide to the downscaling techniques used for climate data. A general introduction of the science of climate modeling is followed by a discussion of techniques, models and methodologies used for producing downscaled projections, and the advantages, disadvantages and uncertainties of each. The book provides detailed information on dynamic and statistical downscaling techniques in non-technical language, as well as recommendations for selecting suitable downscaled datasets for different applications. The use of downscaled climate data in national and international assessments is also discussed using global examples. This is a practical guide for graduate students and researchers working on climate impacts and adaptation, as well as for polic

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.

There is an increasing trend in teachers using graphic novels to get their students excited about reading and writing, using both original stories and adaptations of classic works by authors such as H

S-PLUS is the statistical software of choice for many applied researchers in disciplines ranging from finance to medicine. This book focuses on statistical techniques, applies them to one or more data

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

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

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

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

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.

For five decades, rising US income and wealth inequality has been driven by wage repression and production realignments benefitting the top one percent of households. In this inaugural book for Cambridge Studies in New Economic Thinking, Professor Lance Taylor takes an innovative approach to measuring inequality, providing the first and only full integration of distributional and macro level data for the US. While work by Thomas Piketty and colleagues pursues integration from the income side, Professor Taylor uses data of distributions by size of income and wealth combined with the cost and demand sides, flows of funds, and full balance sheet accounting of real capital and financial claims. This blends measures of inequality with national income and product accounts to show the relationship between productivity and wages at the industry sector level. Taylor assesses the scope and nature of various interventions to reduce income and wealth inequalities using his simulation model, disent

Construction grammar (CxG) is a framework for syntactic analysis that takes constructions - pairings of form and meaning that range from the highly idiomatic to the very general - to be the building blocks of sentence meaning. Offering the first comprehensive introduction to CxG to focus on both English words and the constructions that combine them, this textbook shows students not only what the analyses of particular structures are, but also how and why those analyses are constructed, with each chapter taking the student step-by-step through the reasoning processes that yield the best description of a data set. It offers a wealth of illustrative examples and exercises, largely based on real language data, making it ideal for both self-study and classroom use. Written in an accessible and engaging way, this textbook will open up this increasingly popular linguistic framework to anyone interested in the grammatical patterns of English.

In Digital Data Collection and Information Privacy Law, Mark Burdon argues for the reformulation of information privacy law to regulate new power consequences of ubiquitous data collection. Examining developing business models, based on collections of sensor data - with a focus on the 'smart home' - Burdon demonstrates the challenges that are arising for information privacy's control-model and its application of principled protections of personal information exchange. By reformulating information privacy's primary role of individual control as an interrupter of modulated power, Burdon provides a foundation for future law reform and calls for stronger information privacy law protections. This book should be read by anyone interested in the role of privacy in a world of ubiquitous and pervasive data collection.

Fundamental tenets of colonial historiography are challenged by showing that US capital investment into this colony did not lead to the disappearance of the small farmer. Contrary to well-established narratives, quantitative data show that the increasing integration of rural producers within the US market led to differential outcomes, depending on pre-existing land tenure structures, capital requirements to initiate production, and demographics. These new data suggest that the colonial economy was not polarized into landless Puerto Rican rural workers on one side and corporate US capitalists on the other. The persistence of Puerto Rican small farmers in some regions and the expansion of local property ownership and production disprove this socioeconomic model. Other aspects of extant Puerto Rican historiography are confronted in order to make room for thorough analyses and new conclusions on the economy of colonial Puerto Rico during the early twentieth century.

Specifically designed for linguists, this book provides an introduction to programming using Python for those with little to no experience of coding. Python is one of the most popular and widely-used programming languages as it's also available for free and runs on any operating system. All examples in the text involve language data and can be adapted or used directly for language research. The text focuses on key language-related issues: searching, text manipulation, text encoding and internet data, providing an excellent resource for language research. More experienced users of Python will also benefit from the advanced chapters on graphical user interfaces and functional programming.

This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospa

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix no

The Vintage Recordings and Data A5 Journal from Galison features a series of analog images from the past such as a vinyl album, floppy disc, VHS tape, cassette tape, and many more. The journal has sle

Biology has entered the age of Big Data. A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolu

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic

ICCGS 2019 is intended for naval architects, marine engineers and academics, and focusses on: collision and grounding of ships, collisions between ships and icebergs, offshore structures, bridges

Topics covered in the book include: Methods and tools for loads and load effects, Methods and tools for strength assessment, Experimental analysis of structures, Materials and fabrication of structure

This book provides a self-contained introduction to the simulation of flow and transport in porous media, written by a developer of numerical methods. The reader will learn how to implement reservoir simulation models and computational algorithms in a robust and efficient manner. The book contains a large number of numerical examples, all fully equipped with online code and data, allowing the reader to reproduce results, and use them as a starting point for their own work. All of the examples in the book are based on the MATLAB Reservoir Simulation Toolbox (MRST), an open-source toolbox popular popularity in both academic institutions and the petroleum industry. The book can also be seen as a user guide to the MRST software. It will prove invaluable for researchers, professionals and advanced students using reservoir simulation methods. This title is also available as Open Access on Cambridge Core.

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-cl

A guide to all aspects of experimental design and data analysis for fMRI experiments, completely revised and updated for the second edition. Functional magnetic resonance imaging (fMRI), which allows

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of ver

This tutorial reference serves as a coherent overview of various statistical and mathematical approaches used in brain network analysis, where modeling the complex structures and functions of the human brain often poses many unique computational and statistical challenges. This book fills a gap as a textbook for graduate students while simultaneously articulating important and technically challenging topics. Whereas most available books are graph theory-centric, this text introduces techniques arising from graph theory and expands to include other different models in its discussion on network science, regression, and algebraic topology. Links are included to the sample data and codes used in generating the book's results and figures, helping to empower methodological understanding in a manner immediately usable to both researchers and students.

One of the "six best books for data geeks" - Financial TimesWith over 200 images and extensive how-to and how-not-to examples, this new edition has everything students and scholars need to u

In this volume Chris Callow provides a critical reading of the evidence for changes in Iceland’s socio-political structures from its colonisation to the 1260s when leading Icelanders swore oaths of lo

The methods and techniques presented in this book are focused on spatial data and its use in monitoring agricultural resources, farm management, and regional markets. Spatial econometrics models are i