Data Structures

The basic concepts of traditional mechanics of stressed structures are suitable for classical uniform structures made of homogeneous materials but not for complex structures such as a network plate or

Earthquake Resistant Engineering Structures IV contains the proceedings of the Fourth International Conference on Earthquake Resistant Engineering Structures, held in Ancona, Italy, September 22-24, 2

A supplementary textbook for introductory programming and data structures courses using a LINUX or UNIX platform; or for advanced courses in programming, introductory software engineering, and operati

The strengthening of reinforced concrete (RC) structures using advanced fibre-reinforced polymer (FRP) composites, and in particular the behaviour of FRP-strengthened RC structures is a topic which ha

Shakespeare's Dramatic Structures

There are many books on data structures and algorithms, including some with useful libraries of C functions. Mastering Algorithms with C offers you a unique combination of theoretical background and w

Elastic structures, conceived as slender bodies able to transmit loads, have been studied by scientists and engineers for centuries. By the seventeenth century several useful theories of elastic structures had emerged, with applications to civil and mechanical engineering problems. In recent years improved mathematical tools have extended applications into new areas such as geomechanics and biomechanics. This book, first published in 1998, offers a critically filtered collection of the most significant theories dealing with elastic slender bodies. It includes mathematical models involving elastic structures, which are used to solve practical problems with particular emphasis on nonlinear problems. This collection of interesting and important problems in elastic structures will appeal to a broad range of scientists, engineers and graduate students working in the area of structural mechanics.

The support you need to build high-quality temporary structures. All the technical, business, and legal know-how you need to build and maintain 17 different temporary support and access structures has

Organisational Structures and Processes provides a framework for students to examine the varied and dynamic nature of organization's structures and internal relations. The workbook looks at:The identi

A knowledge of structural geology is fundamental to understanding the processes by which the earth's crust has evolved. It is a subject of fundamental importance to students of geology, experienced field geologists and academic researchers as well as to petroleum and mining engineers. In contrast to many structural textbooks which dwell upon geometrical descriptions of geological structures, this book emphasises mechanical principles and the way in which they can be used to understand how and why a wide range of geological structures develop. Structures on all scales are considered but the emphasis of the book is on those that can be seen on the scale of hand specimen or outcrop. Drawing on their considerable teaching experience the authors present a coherent and lucid analysis of geological structures which will be welcomed by a wide variety of earth scientists.

Dynamic stability analysis enables the determination of unstable regions, which adversely affect a structure's durability. Unlike conventional cable-strut frameworks, tensegrity structures are characterized by a system of internal forces that hold the elements in stable equilibrium, so the initial prestress crucially affects the distribution of unstable regions. This book presents both theoretical background and examples to make a full qualitative and quantitative assessment of these structures possible. First, a geometrically non-linear model and the methods used to evaluate the behavior of tensegrity structures are explained, before a broad spectrum of different planar and spatial design solutions is considered. The book moves step-by-step from the simplest two-dimensional structure, for which solutions can be presented in explicit form, to more complex tensegrity structures such as domes, towers, and plates.

Much of the statistical information currently produced by federal statistical agencies - information about economic, social, and physical well-being that is essential for the functioning of modern society - comes from sample surveys. In recent years, there has been a proliferation of data from other sources, including data collected by government agencies while administering programs, satellite and sensor data, private-sector data such as electronic health records and credit card transaction data, and massive amounts of data available on the internet. How can these data sources be used to enhance the information currently collected on surveys, and to provide new frontiers for producing information and statistics to benefit American society? Toward a 21st Century National Data Infrastructure: Enhancing Survey Programs by Using Multiple Data Sources, the second report in a series funded by the National Science Foundation, discusses how use of multiple data sources can improve the quality

With easy-to-read language and illustrations on almost every page, the DATA Set chapter books are perfect for emerging readers.March of the Mini Beasts (The DATA Set #1)Don't Disturb the Dinosaurs (The DATA Set #2)The Sky Is Falling (The DATA Set #3)Robots Rule the School (The DATA Set #4)A Case of the Clones (The DATA Set #5)Invasion of the Insects (The DATA Set #6)Out of Remote Control (The DATA Set #7)Down the Brain Drain (The DATA Set #8)S.O.S. From Outer Space (The DATA Set #9)Revenge of Dr. von Naysayer (The DATA Set #10)A Little Snow Must Fall (The DATA Set #11)

This commentary covers all topics and critical aspects elicited by the new European General Data Protection Regulation and its interpretation. The commentary focuses on the regulation itself, including cross-references to further provisions (eg the Police and Criminal Justice Data Protection Directive, the E-Privacy-Directive or the former Data Protection Directive 95/46/EC).Article by article the purpose of a provision is classified, its background, function and structure analysed and its content interpreted. The commentary provides an independent view of all topics, presenting both an overview and specific interpretation that provide far-reaching arguments.The editors and authors are outstanding experts in the field of data protection law well known for their practical as well as structured and thorough approach to data protection issues. They offer suitable solutions and sound arguments especially for international companies, legal councils and corporate lawyers as well as data prot

本書之編譯延續原作簡明易懂的優點，幫助讀者奠定紮實的資料結構基礎。更利用 C 語言簡潔緊湊、靈活方便的強項，輕鬆建立良好的資料結構觀念。全書內容涵蓋資料結構的介紹和回顧，所有主題都是以清晰、直接的方式呈現，並搭配大量的圖表說明，強化概念。讀者可以運用本書所教授之技巧，在作者精心安排的各式例題中，練習撰寫一個簡短的程式，增進學習效果並提高學習興趣。

This new volume addresses the growing interest in and use of big data analytics in many industries and in many research fields around the globe; it is a comprehensive resource on the core concepts of big data analytics and the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches.The book's authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you're new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful dat

Social media has put mass communication in the hands of normal people on an unprecedented scale, and has also given social scientists the tools necessary to listen to the voices of everyday people around the world. This book gives social scientists the skills necessary to leverage that opportunity, and transform social media's vast stream of information into social science data. The book combines the big data techniques of computer science with social science methodology. Intended as a text for advanced undergraduates, graduate students, and researchers in the social sciences, this book provides a methodological pathway for scholars who want to make use of this new and evolving source of data. It provides a framework for building one's own data collection and analysis infrastructure, a toolkit of content analysis, geographic analysis, and network analysis, and meditations on the ethical implications of social media data.

Social media has put mass communication in the hands of normal people on an unprecedented scale, and has also given social scientists the tools necessary to listen to the voices of everyday people around the world. This book gives social scientists the skills necessary to leverage that opportunity, and transform social media's vast stream of information into social science data. The book combines the big data techniques of computer science with social science methodology. Intended as a text for advanced undergraduates, graduate students, and researchers in the social sciences, this book provides a methodological pathway for scholars who want to make use of this new and evolving source of data. It provides a framework for building one's own data collection and analysis infrastructure, a toolkit of content analysis, geographic analysis, and network analysis, and meditations on the ethical implications of social media data.

This book assesses data protection rules that are applicable to the processing of personal data in a law enforcement context. It offers the first extensive analysis of the LED and Regulation (EU) 2018/1725. It illustrates the challenges arising from the unclear delineation between the different data protection instruments at both national and EU level. Taking a practical approach, it exemplifies situations where the application of data protection instruments could give rise to a lowering of data protection standards where the data protection rules applicable in the law enforcement context are interpreted broadly. The scope of data protection instruments applied by law enforcement authorities impacts processing for purposes of border control, migration management and asylum because there is an unclear delineation between the different data protection instruments.

The volume traces the controversy that revolves around the bio-cultural relationships of Archaic (Neanderthal) and Modern humans at global and regional, Levantine scales. The focus of the book is on understanding the degree to which the behavioral organization of Archaic groups differed from Moderns. To this end, a case study is presented for a 44-70,000 year old, Middle Paleolithic occupation of a Jordanian rockshelter. The research, centering on the spatial analysis of artifacts, hearths and related data, reveals how the Archaic occupants of the shelter structured their activities and placed certain conceptual labels on different parts of the site. The structure of Tor Faraj is compared to site structures defined for modern foragers, in both ethnographic and archaeological contexts, to measure any differences in behavioral organization.The comparisons show very similar structures for Tor Faraj and its modern cohorts, and the implications of this finding challenge prevailing views tha

How to use data as a tool for empowerment rather than oppression.Big data can be used for good, from tracking disease to exposing human rights violations, and for bad, implementing surveillance and control. Data inevitably represents the ideologies of those who control its use; data analytics and algorithms too often exclude women, the poor, and ethnic groups. In Data Action, Sarah Williams provides a guide for working with data in more ethical and responsible ways. Williams outlines a method that emphasizes collaboration among data scientists, policy experts, data designers, and the public. The approach generates policy debates, influences civic decisions, and informs design to help ensure that the voices of people represented in the data are neither marginalized nor left unheard.

Health research around the world relies on access to data, and much of the most valuable, reliable, and comprehensive data collections are held by governments. These collections, which contain data on whole populations, are a powerful tool in the hands of researchers, especially when they are linked and analyzed, and can help to address “wicked problems” in health and emerging global threats such as COVID-19. At the same time, these data collections contain sensitive information that must only be used in ways that respect the values, interests, and rights of individuals and their communities. Sharing Linked Data for Health Research provides a template for allowing research access to government data collections in a regulatory environment designed to build social license while supporting the research enterprise.

An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deplo

How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local. In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Loukissas argues in All Data Are Local, we should approach data sets with an awareness that data are created by humans and their dutiful machines, at a time, in a place, with the instruments at hand, for audiences that are conditioned to receive them. The term data set implies something discrete, complete, and portable, but it is none of those things. Examining a series of data sources important for understanding the state of public life in the United States--Harvard's Arnold Arboretum, the Digital Public Library of America, UCLA's Television News Archive, and the real estate marketplace Zillow--Loukissas shows us how to analyze data settings rather than data sets.Loukissas sets out six principles: all data are local; data have complex attachments to place; data are collected from heterogen

As the world becomes increasingly connected, it is also more exposed to a myriad of cyber threats. We need to use multiple types of tools and techniques to learn and understand the evolving threat landscape. Data is a common thread linking various types of devices and end users. Analyzing data across different segments of cybersecurity domains, particularly data generated during cyber-attacks, can help us understand threats better, prevent future cyber-attacks, and provide insights into the evolving cyber threat landscape. This book takes a data oriented approach to studying cyber threats, showing in depth how traditional methods such as anomaly detection can be extended using data analytics and also applies data analytics to non-traditional views of cybersecurity, such as multi domain analysis, time series and spatial data analysis, and human-centered cybersecurity.

Now in its fourth edition, this comprehensive introduction of fundamental panel data methodologies provides insights on what is most essential in panel literature. A capstone to the forty-year career of a pioneer of panel data analysis, this new edition's primary contribution will be the coverage of advancements in panel data analysis, a statistical method widely used to analyze two or higher-dimensional panel data. The topics discussed in early editions have been reorganized and streamlined to comprehensively introduce panel econometric methodologies useful for identifying causal relationships among variables, supported by interdisciplinary examples and case studies. This book, to be featured in Cambridge's Econometric Society Monographs series, has been the leader in the field since the first edition. It is essential reading for researchers, practitioners and graduate students interested in the analysis of microeconomic behavior.

An integrated introduction to data visualization, strategic communication, and delivery best practices.Persuading with Data provides an integrated instructional guide to data visualization, strategic communication, and delivery best practices. Most books on data visualization focus on creating good graphs. This is the first book that combines both explanatory visualization and communication strategy, showing how to use visuals to create effective communications that convince an audience to accept and act on the data. In four parts that proceed from macro to micro, the book explains how our brains make sense of graphs; how to design effective graphs and slides that support your ideas; how to organize those ideas into a compelling presentation; and how to deliver and defend data to an audience. Persuading with Data is for anyone who has to explain analytical results to others. It synthesizes a wide range of skills needed by modern data professionals, providing a complete toolkit for cr

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets.Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow―and show how a human-centered approach can enhance each one, by making the process more transparent, aski

Discover the world of engineering with fun, step-by-step projects. Take a closer look at the engineering behind the buildings and structures that surround us. Discover how engineers balance forces s

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found

FIRST EDITION BRONZE RUNNER UP: Axiom Awards 2018 - Business Technology Category 90% of today's data was generated in the last two years and the amount of data we have available is predicted to double every two years. Data is an integral strategic asset for all businesses. Learn how to leverage this data and generate valuable insights from bestselling author and Big Data guru Bernard Marr. Data has massive potential for all businesses when used correctly, from small organizations to tech giants and huge multinationals. But this resource is too often not being fully utilized and is far from being arguably the world's most valuable resource. Data Strategy is the must-read guide on how to create a robust data-driven approach that will harness the power of data to revolutionize your business. Explaining how to collect, use and manage data, this book prepares any organization with the tools and strategies needed to thrive in the digital economy. This new edition of this bestselling title is

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques to the detection, mitigation, treatment, and elimination of the COVID-19 virus around the world. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes. Data Science for COVID-19, Volume 2 extends the coverage of Volume 1 into research on statistical data and applications of Data Science to a variety of perspectives for analysis and understanding of the wide variety of impacts COVID-19 is having on societies and medical practice around the world.

This collection explores the relevance of global trade law for data, big data and cross-border data flows. Contributing authors from different disciplines including law, economics and political science analyze developments at the World Trade Organization and in preferential trade venues by asking what future-oriented models for data governance are available and viable in the area of trade law and policy. The collection paints the broad picture of the interaction between digital technologies and trade regulation as well as provides in-depth analyses of critical to the data-driven economy issues, such as privacy and AI, and different countries' perspectives. This title is also available as Open Access on Cambridge Core.

Supported by ten years of research, Wigmore has gathered extensive data covering the 2008 financial crisis and subsequent recovery to provide the first comprehensive history of the period. Financial crises cannot occur unless institutional investors finance the bubbles that created them. Wigmore follows the trail of data putting pressure on institutional investors to achieve higher levels of returns that led to over-leverage throughout the financial system and placed such a burden on recovery. Here is a 'very good picture - and painful reminder - of the crisis' evolution across multiple asset classes, structures, participants, and geographies.' This work serves as a critical analysis of modern portfolio management and an important reference work for financial professionals, academics, investors, and students.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

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

Three books by the bestselling authors on Data Warehousing! The most authoritative guides from the inventor of the technique all for a value price.The Data Warehouse Toolkit, 3rd Edition (978111853080

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