Java Data Object

This is an authoritative and practical guide to analysing business data, to enable managers and companies to develop successful business strategies. Data has become a dominant factor in today’s busine

Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis. Maxwell, Dela

Describes the work of a bid data analyst, detailing the education and skills needed for the position, advancement in the career path, and what the future holds for the industry.

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to beco

Emerging Spatial Big Data (SBD) has transformative potential in solving many grand societal challenges such as water resource management, food security, disaster response, and transportation. However,

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics.

This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in

Data protection aims to safeguard the rights, freedoms and interests of individuals. Since the enforcement of the Lisbon Treaty, data protection has been elevated to the status of a fundamental right

Direct object omission is a general occurrence, observed in varying degrees across the world's languages. The expression of verbal transitivity in small children begins with the regular use of verbs without their object, even where object omissions are illicit in the ambient language. Grounded in generative grammar and learnability theory, this book presents a comprehensive view of experimental approaches to object acquisition, and is the first to examine how children rely on the lexical, structural and pragmatic components to unravel the system. The results presented lead to the hypothesis that missing objects in child language should not be seen as a deficit but as a continuous process of knowledge integration. The book argues for a new model of how this aspect of grammar is innately represented from birth. Ideal reading for advanced students and researchers in language acquisition and syntactic theory, the book's opening and closing chapters are also suitable for non-specialist read

A comprehensive introduction to the theory and practice of contemporary data science analysis for railway track engineering Featuring a practical introduction to state-of-the-art data analysis for rai

This book is designed to provide a new framework for Data Science, based on a solid foundation in mathematics and computational science. It covers a diverse range of topics from the field, including d

The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologiesThe main goal of this book is to spur the development of effective big-dat

This volume addresses the complex relationships between human beings and their environment through the analysis of botanical, zoological, and geological data in archaeological contexts. At the heart o

As machine-readable data comes to play an increasingly important role in everyday life, researchers find themselves with rich resources for studying society. The novel methods and tools needed to work

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in var

This book presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how to use statistical tests as a key tool of analyzing research data. The author deta

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the

This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have b

"This book covers the fundamental concepts of big data management and analytics along with recent research development in big data. It also includes various real time/offline applications and case stu

This title provides analysts the tools, techniques, and methods to “monetize their organization’s data.” A base understanding of the evolution of analytics and how organizations need to add new positi

"This book aims at analyzing impact of such changes in developing countries on the information and data generated, owned and maintained by the government. The book focus on the challenges, weaknesses

Electronic health records (EHRs) have become commonplace in the medical profession. Health data are readily captured and permanently stored in a digital fashion, and consequently, are increasingly bei

This book features peer reviewed contributions from across the disciplines on themes relating to protection of data and to privacy protection. The authors explore fundamental and legal questions, inve

This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, whil

This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems

We are living in a cyber society. Mobile devices, social media, the Internet, crime cameras, and other diverse sources can be pulled together to form massive datasets, known as big data, which make it

Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications teaches everyday biological data analysis to the undergraduate and graduate levels. Through numerous examples and over 100

This book describes how the creation of new digital services -- through vertical and horizontal integration of data coming from sensors on top of existing legacy systems -- that has already had a majo

For readers who may not be statisticians or data analysts, Sarmento and Costa explain how to analyze data using the Python and R languages. They have particularly in mind, students and researchers

The overall goal of the book is to present the latest research on the new challenges of data technologies. It will offer an overview of the social, ethical and legal problems posed by group profiling,

Is there more to qualitative data collection than face-to-face interviews? Answering with a resounding 'yes', this book introduces the reader to a wide array of exciting and novel techniques for collecting qualitative data in the social and health sciences. Collecting Qualitative Data offers a practical and accessible guide to textual, media and virtual methods currently under-utilised within qualitative research. Contributors from a range of disciplines share their experiences of implementing a particular technique, provide step-by-step guidance to using that approach, and highlight both the potential and pitfalls. From gathering blog data to the story completion method to conducting focus groups online, the methods and data types featured in this book are ideally suited to student projects and other time- and resource-limited research. In presenting several innovative ways that data can be collected, new modes of scholarship and new research orientations are opened up to student rese

This book helps readers gain an in-depth understanding of electronic health record (EHR) systems, medical big data, and the regulations that govern them. It analyzes both the shortcomings and benefits of EHR systems, exploring the law's response to the creation of these systems, highlighting gaps in the current legal framework, and developing detailed recommendations for regulatory, policy, and technological improvements. Electronic Health Records and Medical Big Data addresses not only privacy and security concerns but also other important challenges, such as those related to data quality and data analysis. In addition, the author formulates a large body of recommendations to improve the technology's safety, security, and efficacy for both clinical and secondary (such as research) uses of medical data.

An essential guide to healthcare data problems, sources, and solutionsStrategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing delu

Fraud Data Analytics is the critical tool in locating and recognizing fraud scenario’s in today’s core business systems. This book will combine fraud risk assessment and the use of fraud data analytic

The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics

This book will examine gun violence and crime in America. The emphasis in this text will be on the inadequacy of the data on firearm ownership. In addition, there will be a discussion of murder and ot

Focusing on the practical clinical and statistical issues that arise in pharmaceutical industry trials, this book summarizes the author’s experience in serving on many data monitoring committees (DMCs

Compliance with federal equal employment opportunity regulations, including civil rights laws and affirmative action requirements, requires collection and analysis of data on disparities in employment