Databases / Data management Books
Taylor & Francis Ltd Making with Data
Book SynopsisHow can we give data physical form?And how might those creations change the ways we experience data and the stories it can tell?Making with Data: Physical Design and Craft in a Data-Driven World provides a snapshot of the diverse practices contemporary creators are using to produce objects, spaces, and experiences imbued with data. Across 25+ beautifully-illustrated chapters, international artists, designers, and scientists each explain the process of creating a specific data-driven pieceâillustrating their practice with candid sketches, photos, and design artifacts from their own studios.The author website, featuring updates and more information about the projects behind the book, can be found here: https://makingwithdata.org/.Featuring influential voices in computer science, data science, graphic design, art, craft, and architecture, Making with Data is accessible and inspiring for entTrade Review"A mind-blowing collection! With the rich visual process descriptions, the creators invite us into their workshops and let us look over their shoulders. You will discover both an exhibition of wonderful data-inspired works as well as the backstories of each of these pieces. Whether hand-made, machine-controlled, or through natural processes, all the chapters show fascinating and bespoke creations of data objects. A much needed collection highlighting what is happening at the frontiers of art and sciences in this new field of data design."-- Giorgia Lupi, partner at Pentagram and author of Dear Data"What a much-needed book! Till, Sam, Lora, and Wes show us that data communication can be so much more than just visualization. There is a whole exciting world of data physicalization waiting to be explored, and the authors open the door for us and lead us through it with intelligent commentary. The book takes us to visit different artists, who explain their approaches and tools – from copper pipes to paper, from wood to electronics. It's a hugely inspiring tour. Reading this book will make you want to experiment with data in the realm of the physical."-- Lisa Charlotte Muth, data vis designer and writer at Datawrapper "This book has fresh inspirations from innovative artist-inventors who open up new possibilities for anyone who has data that tells a story. The screen is no longer the goal or the limit; freeing designers to explore more dimensions and shape deeper experiences to reach people with important messages about their health, communities, and climate. Data physicalizations break free into new dimensions where playful imaginations can use water, plastic, wood, or stone to fabricate data stories for public installations and private reflections. This book makes me want to turn on the laser cutter and restart the 3D printer to fabricate something startling, informative, and eye opening."-- Ben Shneiderman, Professor, Computer science, University of Maryland, USA"A collection of recent and diverse data-driven physical artifacts and sensorial experiences. Projects are beautifully illustrated and described in jargon-free language packed with practical information elucidating the design process, from the tools used to the context of their conception. Making with Data is an invaluable resource for educators and practitioners alike. It broadens our perspective of representing data by engaging all our senses."-- Isabel Meirelles, Professor, Faculty of Design, OCAD University, Toronto, Canada"“Designing with Data” is one of today’s key mantras. What next? Perhaps “Making with Data”, as argued by professors Huron, Nagel, Oehlberg and Willett. This timely book explores new ways data is penetrating our living environment and is crossing the boundary between the physical and the digital. Innovative fabrication methods lend materiality to data, as designers experiment with the use of laser cutters and 3D printers to transform maps and charts into tactile models and artworks. A compelling read for any data enthusiast!"-- Carlo Ratti, Director, MIT Senseable City Lab, USATable of Contents1. Handcraft - Introduction by Sheelagh Carpendale and Lora Oehlberg. 1.1 Snow Water Equivalent by Adrien Segal. 1.2 Life in Clay by Alice Thudt. 1.3 V-Pleat Data Origami by Sarah Hayes. 1.4 Anthropocene Footprints by Mieka West. 1.5 Endings by Loren Madsen. 2. Participation - Introduction by Georgia Panagiotidou and Andrew Vande Moere. 2.1 Cairn by Pauline Gourlet and Thierry Dassé. 2.2 SeeBoat by Laura Perovich. 2.3 Let’s Play with Data by Jose Duarte and EasyDataViz. 2.4 100% [City] by Rimini Protokoll (Helgard Haug, Stefan Kaegi, and Daniel Wetzel). 2.5 Data Strings by Daniel Pearson, Pau Garcia, and Alexandra de Requesens. 3. Digital Production - Introduction by Yvonne Jansen. 3.1 Chemo Singing Bowl by Stephen Barrass. 3.2 Wage Islands by Ekene Ijeoma. 3.3 Data That Feels Gravity by Volker Schweisfurth. 3.4 Orbacles by MINN_LAB Design Collective (Daniel F. Keefe, Ross Altheimer, Andrea J. Johnson, Mahdieh Mahmoudi, Patrick Moe, Maura Rockcastle, Marc Swackhamer, and Aaron Wittkamper). 3.5 Dataseeds by Nick Dulake and Ian Gwilt. 4 Actuation - Introduction by Pierre Dragicevic. 4.1 Tenison Road Charts by David Sweeney, Alex Taylor, and Siân Lindley. 4.2 LOOP by Kim Sauvé and Steven Houben. 4.3 AirFIELD by Nik Hafermaas, Dan Goods, and Jamie Barlow. 4.4 EMERGE by Jason Alexander, Faisal Taher, John Hardy, and John Vidler. 4.5 Zooids by Mathieu Le Goc, Charles Perin, Sean Follmer, Jean-Daniel Fekete, and Pierre Dragicevic. 5. Environment - Introduction by Dietmar Offenhuber. 5.1 Perpetual Plastic by Liina Klauss, Moritz Stefaner and Skye Morét. 5.2 Dataponics: Human-Vegetal Play by Robert Cercós. 5.3 Solar Totems by Charles Sowers. 5.4 Staubmarke (Dustmark) by Dietmar Offenhuber.
£37.99
Taylor & Francis Ltd Evolutionary Intelligence for Healthcare Applications
Book SynopsisThis book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.Table of Contents1. Evolutionary Intelligence. 2. Heart Disease Diagnosis. 3. Diabetes Prediction and Classification. 4. Degenerative Diseases. 5. Tuberculosis. 6. Muscular Dystrophy. 7. Tumor Prediction and Classification.
£43.69
CRC Press The Data Preparation Journey
Book SynopsisThe Data Preparation Journey: Finding Your Way With R introduces the principles of data preparation within in a systematic approach that follows a typical data science or statistical workflow. With that context, readers will work through practical solutions to resolving problems in data using the statistical and data science programming language R. These solutions include examples of complex real-world data, adding greater context and exposing the reader to greater technical challenges. This book focuses on the Import to Tidy to Transform steps. It demonstrates how âœVisualiseâ is an important part of Exploratory Data Analysis, a strategy for identifying potential problems with the data prior to cleaning.This book is designed for readers with a working knowledge of data manipulation functions in R or other programming languages. It is suitable for academics for whom analyzing data is crucial, businesses who make decisions based on the insights gleaned from collecting d
£52.24
Taylor & Francis Ltd Machine Learning for Decision Sciences with Case
Book SynopsisThis book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learningTable of Contents1. Introduction 2. Overview of Python for Machine Learning 3. Data Analytics Life Cycle for Machine Learning 4. Unsupervised Learning 5. Supervised Learning: Regression 6. Supervised Learning: Classification 7. Feature Engineering 8. Reinforcement Learning 9. Case Studies for Decision Sciences Using Python
£156.75
Taylor & Francis Ltd Urban Freight Analytics
Book SynopsisUrban Freight Analytics examines the key concepts associated with the development and application of decision support tools for evaluating and implementing city logistics solutions. New analytical methods are required for effectively planning and operating emerging technologies including the Internet of Things (IoT), Information and Communication Technologies (ICT), and Intelligent Transport Systems (ITS).The book provides a comprehensive study of modelling and evaluation approaches to urban freight transport. It includes case studies from Japan, the US, Europe, and Australia that illustrate the experiences of cities that have already implemented city logistics, including analytical methods that address the complex issues associated with adopting advanced technologies such as autonomous vehicles and drones in urban freight transport.Also considered are future directions in urban freight analytics, including hyperconnected city logistics based on the Physical ITable of ContentsPart I. Methods. 1. Introduction. 2. Data collection and analyses. 3. Geographic information systems and spatial analysis. 4. Optimisation. 5. Multi-agent simulation with machine learning. 6. Reliability and resilience. 7. Evaluation. Part II. Applications. 8. Autonomous Vehicles and Robots. 9. Access management and pricing. 10. Environmental sustainability. 11. Disruption of Networks. 12. Future directions.
£84.99
Taylor & Francis Ltd Data Journalism
Book SynopsisTaking a hands-on and holistic approach to data, Data + Journalism provides a complete guide to reporting data-driven stories. This book offers insights into data journalism from a global perspective, including datasets and interviews with data journalists from countries around the world. Emphasized by examples drawn from frequently updated sets of open data posted by authoritative sources like the FBI, Eurostat and the US Census Bureau, the authors take a deep dive into data journalism's heavy lifting searching for, scraping and cleaning data. Combined with exercises, video training supplements and lists of tools and resources at the end of each chapter, readers will learn not just how to crunch numbers but also how to put a human face to data, resulting in compelling, story-driven news stories based on solid analysis. Written by two experienced journalists and data journalism teachers, Data + Journalism is essential reading for students, instructorTable of ContentsIntroductionChapter 1: Acquiring DataChapter 2: Searching the Deep WebChapter 3: Scraping DataChapter 4: Cleaning DataChapter 5: Basic SpreadsheetsChapter 6: Advanced Spreadsheets and RChapter 7: Writing a Data StoryChapter 8: SQLChapter 9: Scraping Social MediaChapter 10: Data VisualizationChapter 11: Ethics, Trust, Transparency and Posting Data OnlineChapter 12: Math for Journalists: Writing with Numbers
£32.29
Taylor & Francis Ltd Knowledge Integration Methods for Probabilistic
Book SynopsisKnowledge-based systems and solving knowledge integrating problems have seen a great surge of research activity in recent years. Knowledge Integration Methods provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. The book also provides the mathematical background to solving problems of restoring consistency and integrating probabilistic knowledge bases in the integrating process. The research results presented in the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more. This text will be useful for computer science graduates and PhD students, in addition to researchers and readers working on knowledge management and ontology interpretation.Table of Contents1. Introduction 2. Probabilistic Knowledge-based Systems 3. Consistency Measures for Probabilistic Knowledge Bases 4. Methods for Restoring Consistency in Probabilistic Knowledge Bases 5. Distance-Based Methods for Integrating Probabilistic Knowledge Bases 6. Value-based Method for Integrating Probabilistic Knowledge Bases 7. Experiments and Applications 8. Conclusions and Open Problems
£94.99
Taylor & Francis Ltd Urban Informatics
Book SynopsisUrban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management, analysis, and manipulation using R statistical software. Designed for undergraduate and above level courses, this book is an ideal onramp for the study of urban informatics and how to translate novel data sets into new insights and practical tools.The book follows a unique pedagogical approach developed by the author to enable students to build skills by pursuing projects that inspire and motivate them. Each chapter has an Exploratory Data Assignment that prompts readers to practice their new skills on a data set of their choice. These assignments guide readers through the process of becoming familiar with the contents of a novel data set and communicating meaningful insights from the data to others.Key Features: The technical curriculum consists of both data management and analytics, including both as needed to become acquainted with and reveal the content of a new data set. Content that is contextualized in real-world applications relevant to community concerns. Unit-level assignments that educators might use as midterms or otherwise. These include Community Experience assignments that prompt students to evaluate the assumptions they have made about their data against real world information. All data sets are publicly available through the Boston Data Portal. Table of Contents1 Introduction 2 Welcome to R 3 Telling a Data Story: Examining Individual Records 4 The Pulse of the City: Observing Variable Patterns 5 Uncovering Information: Making and Creating Variables 6 Measuring with Big Data 7 Making Measures from Records: Aggregating and Merging Data 8 Mapping Communities 9 Advanced Visual Techniques 10 Beyond Measurement: Inferential Statistics (and Correlations) 11 Identifying Inequities across Groups: ANOVA and t-Test 12 Unpacking Mechanisms Driving Inequities: Multivariate Regression 13 Advanced Analytic Techniques 14 Emergent Technologies
£123.50
CRC Press Real World AI Ethics for Data Scientists
Book SynopsisIn the midst of the fourth industrial revolution, big data is weighed in gold, placing enormous power in the hands of data scientists the modern AI alchemists. But great power comes with greater responsibility. This book seeks to shape, in a practical, diverse, and inclusive way, the ethical compass of those entrusted with big data.Being practical, this book provides seven real-world case studies dealing with big data abuse. These cases span a range of topics from the statistical manipulation of research in the Cornell food lab through the Facebook user data abuse done by Cambridge Analytica to the abuse of farm animals by AI in a chapter co-authored by renowned philosophers Peter Singer and Yip Fai Tse. Diverse and inclusive, given the global nature of this revolution, this book provides case-by-case commentary on the cases by scholars representing non-Western ethical approaches (Buddhist, Jewish, Indigenous, and African) as well as Western approaches (consequentialism, deoTable of Contents1. Introduction: Moral Machines, 2. Introduction to Ethical Approaches in Data Science, 3. Research Ethics and the Scientific Method, 4. Machine Models in Court, 5. Synthetic Media and Political Violence, 6. Biometrics and Facial Recognition, 7. Content Moderation: Hate Speech and Genocide in Myanmar, 8. Mental Malware: Algorithms and Choice Architecture, 9. AI and Nonhuman Animals
£999.99
Taylor & Francis Ltd The Discipline of Data
Book SynopsisPulling aside the curtain of Big Data' buzz, this book introduces C-suite and other non-technical senior leaders to the essentials of obtaining and maintaining accurate, reliable data, especially for decision-making purposes. Bad data begets bad decisions, and an understanding of data fundamentals how data is generated, organized, stored, evaluated, and maintained has never been more important when solving problems such as the pandemic-related supply chain crisis. This book addresses the data-related challenges that businesses face, answering questions such as: What are the characteristics of high-quality data? How do you get from bad data to good data? What procedures and practices ensure high-quality data? How do you know whether your data supports the decisions you need to make? This clear and valuable resource will appeal to C-suite executives and top-line managers across industries, as wTable of Contents1 Preface. 2 Data – Introduction. 3 The Many Facets of Data. 3.1 Basic Concepts. 3.2 Basic Terms and Terminology. 4 Domain Specific Topics. 4.1 Data Governance. 4.2 Data Architecture. 4.3 Databases. 4.4 Master Data and Master Data Management. 4.5 Metadata and Metadata Management. 4.6 Data Quality. 4.7 Null Values. 4.8 Data Modeling and Design. 4.9 Data Integration and Interoperability. 4.10 Data Security. 4.11 Data at Rest and Data in Motion. 4.12 Data Wrangling and Data Storage. 5 Data: Past, Present and Future. 5.1 Data – The Past. 5.2 Data – The Present. 5.3 Data – The Future. 6 The New Reality. 7 Data – Use Cases.8 To Sum Up. 9 Data – Optimization. 10 Epilog
£28.49
CRC Press Recommender Systems
Book SynopsisRecommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical uses Describes how to design, train, and evaluate a recommendation algorithm Explains migration from a recommendation model to a live system with users Describes utilization of the data collected from a recommender system to understand the user preferences Addresses the security aspects and ways to deal with possible attacks to build a robust system This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.
£44.64
CRC Press SQL Server Database Programming with C
Book SynopsisDatabases have become an integral part of modern-day life. We live in an information-driven society and database technology has a direct impact on our daily lives. Decisions are routinely made by organizations based on the information collected and stored in the databases. Because databases play such an important role in business and society, database programming is a key skill.SQL Server Database Programming with C#: Desktop and Web Applications is for college students and software programmers who want to develop practical and commercial skills in database programming with C# or Visual C#.NET 2022 as well as the relational database Microsoft SQL Server 2019. The book explains the practical considerations and applications in database programming with Visual C# 2022 and provides realistic examples and detailed explanations. A direct writing style is combined with real-world examples to provide readers with a clear picture of how to handle database programming issues in the VisTable of ContentsCopyrights and Trademarks. Preface. Acknowledgements. About the Author. Chapter 1 Introduction. Chapter 2 Introduction to Databases. Chapter 3 Introduction to ADO.NET. Chapter 4 Introduction to Language Integrated Query (LINQ). Chapter 5 Data Selection Query with Visual C#.NET. Chapter 6 Data Inserting with Visual C#.NET. Chapter 7 Data Updating and Deleting with Visual C#.NET. Chapter 8 Accessing Data in ASP.NET. Chapter 9 ASP.NET Web Services. Index.
£71.24
CRC Press Big Data Analytics
Book SynopsisWith this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market.Comparing and contrasting the different types of analysis commonly conducted with big data, this accessible reference presents clear-cut explanations of the general workings of big data tools. Instead of spending time on HOW to install specific packages, it focuses on the reasons WHY readers would install a given package.The book provides authoritative guidance on a range of tools, including open source and proprietary systems. It details the strengths and weaknesses of incorporating big data analysis into decision-making and explains how to leverage the strengths while mitigating the weaknesses. Describes the benefits of distrTable of ContentsIntroduction. The Mother of Invention’s Triplets: Moore’s Law, the Proliferation of Data, and Data Storage Technology. Hadoop. HBase and Other Big Data Databases. Machine Learning. Statistics. Google. Geographic Information Systems. Discovery. Data Quality. Benefits. Concerns.
£42.74
Taylor & Francis Ltd Exploring Data Science with R and the Tidyverse
Book SynopsisThis book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader. Upon completion of the text, the reader will be able to: Gain proficiency in R programming Load and manipulate data frames, and tidy them using tidyverse tools Conduct statistical analyses and draw meaningful inferences from them Perform modeling from numerical and textual data Generate data visualizations (numerical and spatialTable of Contents1. Data Types 2. Data Transformation 3. Data Visualization 4. Building Simulations 5. Sampling 6. Hypothesis Testing 7. Quantifying Uncertainty 8. Towards Normality 9. Regression 10. Text Analysis
£73.14
Taylor & Francis Ltd Internet of Everything and Big Data
Book SynopsisThere currently is no in-depth book dedicated to the challenge of the Internet of Everything and Big Data technologies in smart cities. Humankind today is confronting a critical worldwide portability challenge and the framework that moves cities must keep pace with the innovation. Internet of Everything and Big Data: Major Challenges in Smart Cities reviews the applications, technologies, standards, and other issues related to smart cities.This book is dedicated to addressing the major challenges in realizing smart cities and sensing platforms in the era of Big Data cities and Internet of Everything. Challenges vary from cost and energy efficiency to availability and service quality. This book examines security issues and challenges, addresses the total information science challenges, covers exploring and creating IoT environment-related sales adaptive systems, and investigates basic and high-level concepts using the latest techniques implemented by researchers Table of Contents1. Wireless Sensor Networks in Smart Cities. 2. Big Data Analytics. 3. Security Issues in Smart Cities. 4. Artificial Intelligence in Smart-Cities. 5. Performability in IoT-enabled Sensors. 6. Data delivery in IoT-enabled Smart Cities. 7. Deployment Issues in IoT-enabled Sensors. 8. Traffic Modelling in Smart-Cities. 9. Resource Management and Enabling Technologies Localization in IoT-enabled Sensors. 10. Modeling and Simulation with Fuzzy Techniques in Smart Cities. 11. Energy Efficiency in Smart Cities Technologies. 12. Semantic Interoperability for IoT.
£142.50
Taylor & Francis Ltd Blockchainbased Cyber Security
Book SynopsisThe book focuses on a paradigm of blockchain technology that addresses cyber security. The challenges related to cyber security and the solutions based on Software Defined Networks are discussed. The book presents solutions to deal with cyber security attacks by considering real-time applications based on IoT, Wireless Sensor Networks, Cyber-Physical Systems, and Smart Grids. The book is useful for academicians and research scholars worldwide working in cyber security. It is also useful for industry experts working in cyber security.
£48.99
Taylor & Francis Ltd Risk Analytics
Book SynopsisThe 2022 World Economic Forum surveyed 1,000 experts and leaders who indicated their risk perception that the earth's conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in theminds of leaders, newknowledge brings new risk, and the adaptation and adoption of risk knowledge is required to better understand the causes and effects can have on technological risks. These opportunities require not only adopting new ways of managing and controlling emerging processes for society and business, but also adapting organizations to changes and managing new risks.Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. A risk analytics system is a hybrid system where human andTable of Contents1. Fundamental Concepts 2. Risk Management, Modelling, and Analytics Processes 3. Decision Making under Risk and Its Analytics Support 4. Risk Management and Analytics in Organizations 5. Tools for Risk Management 6. Data Analytics in Risk Management 7. Machine and Statistical Learning in Risk Analytics 8. Dealing with Monitoring the Risk Analytics Process 9. Creation of Actions and Value
£71.24
Taylor & Francis Ltd Big Data Concepts Technologies and Applications
Book SynopsisWith the advent of such advanced technologies as cloud computing, the Internet of Things, the Medical Internet of Things, the Industry Internet of Things and sensor networks as well as the exponential growth in the usage of Internet-based and social media platforms, there are enormous oceans of data. These huge volumes of data can be used for effective decision making and improved performance if analyzed properly. Due to its inherent characteristics, big data is very complex and cannot be handled and processed by traditional database management approaches. There is a need for sophisticated approaches, tools and technologies that can be used to store, manage and analyze these enormous amounts of data to make the best use of them.Big Data Concepts, Technologies, and Applications covers the concepts, technologies, and applications of big data analytics. Presenting the state-of-the-art technologies in use for big data analytics. it provides an in-depth discussiTable of ContentsSection A. Understanding Big Data Chapter 1. Overview of Big Data Chapter 2. Challenges of Big Data Chapter 3. Big Data Analytics Section B. Big Data Technologies Chapter 4. Hadoop Ecosystem Chapter 5. NoSQL Databases Chapter 6. Data Lakes Chapter 7. Deep Learning Chapter 8. Blockchain Section C. Big Data Applications Chapter 9. Big Data for Healthcare Chapter 10. Big Data Analytics for Fraud Detection and Prevention Chapter 11. Big Data Analytics in Social Media Chapter 12. Novel Applications and Research Directions in Big Data Analytics
£42.74
Taylor & Francis Ltd DataDriven Modelling and Predictive Analytics in
Book SynopsisData-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visuali
£71.24
Taylor & Francis Ltd Open Data for Everybody
Book SynopsisWhat if I told you something that could empower our third sector and activists to enhance their capacity? From gathering evidence for funding tenders to campaigning for crucial social issues and much more? It's called open data, yet many in social action remain unaware of it. Primarily shaped by corporate entities, open data seems tailored only for technologists, alienating the third sector. But in reality, it's a powerful tool for social change, bolstering civil society, and creating resilient communities.This book argues a simple point: if open data and the digital aspects that support it aren't accessible to all, then what is the point of it? In an age where technology should be seen as a fundamental human right, it's time to rethink outreach. Deeply rooted in grassroots social activism, this book explores a journey that led to collaborations with governments globally, based on real hands-on work, aiming to democratize open data. Through narrative storytelling, we share insights, best practices, procedures, and community-driven approaches. Regardless of your skill set or organization size, from grassroots workers to third-sector professionals and government officers, join us to reshape the perception of open data, fostering change in neighborhoods.Open Data for Everybody: Using Open Data for Social Good is a love letter to open data's transformative power. To create solutions, understanding the problem is crucial. This book seeks to return control to the real expertsâthose living and working within our communities.Discover more at: www.opendataforeverybody.com
£29.99
Taylor & Francis Customer Relationship Management in the Digital
Book SynopsisCustomer Relationship Management in the Digital Age charts the concepts, strategies, benefits, and technologies of CRM in an evolving and increasingly digital business landscape.It empowers readers with the skills to use CRM to forge enduring customer connections, optimize experiences, and drive loyalty across diverse industries and markets. Building upon existing literature, this guide offers a holistic approach that bridges theory and practice, making complex CRM concepts accessible to a wide audience. It integrates the latest technological advances, market trends, and customer centric initiatives, providing a comprehensive view of CRM's role in an increasingly customer-driven era. Pedagogical features include case studies, practical strategies and real-world examples, as well as chapter summaries and discussion questions to guide the reader through the key learning points of each chapter.This helpful book enables readers to navigate the complexities of CRM im
£55.09
CRC Press Introduction to Classifier Performance Analysis
Book Synopsis
£46.54
O'Reilly Media Advanced Analytics with PySpark
Book SynopsisUpdated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.
£39.74
Cambridge University Press Big Data Over Networks
Book SynopsisUtilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data overTable of ContentsPart I. Mathematical Foundations: 1. Tensor models – solution methods and applications Shiqian Ma, Bo Jiang, Xiuzhen Huang and Shuzhong Zhang; 2. Sparsity-aware distributed learning Symeon Chouvardas, Yannis Kopsinis and Sergios Theodoridis; 3. Optimization algorithms for big data with application in wireless networks Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun and Zhi-Quan Luo; 4. A unified distributed algorithm for non-cooperative games Jong-Shi Pang and Meisam Razaviyayn; Part II. Big Data over Cyber Networks: 5. Big data analytics systems Ganesh Ananthanarayanan and Ishai Menache; 6. Distributed big data storage in optical wireless networks Chen Gong, Zhengyuan Xu and Xiaodong Wang; 7. Big data aware wireless communication – challenges and opportunities Suzhi Bi, Rui Zhang, Zhi Ding and Shuguang Cui; 8. Big data processing for smart grid security Lanchao Liu, Zhu Han, H. Vincent Poor and Shuguang Cui; Part III. Big Data over Social Networks: 9. Big data: a new perspective on cities Riccardo Gallotti, Thomas Louail, Rémi Louf and Marc Barthelemy; 10. High dimensional network analytics: mapping topic networks in Twitter data during the Arab Spring Kathleen M. Carley, Wei Wei and Kenneth Joseph; 11. Social influence analysis in the big data era – a review Jianping Cao, Dongliang Duan, Liuqing Yang, Qingpeng Zhang, Senzhang Wang and Feiyue Wang; Part IV. Big Data over Biological Networks: 12. Inference of gene regulatory networks – validation and uncertainty Xiaoning Qian, Byung-Jun Yoon and Edward R Dougherty; 13. Inference of gene networks associated with the host response to infectious disease Zhe Gan, Xin Yuan, Ricardo Henao, Ephraim L. Tsalik and Lawrence Carin; 14. Gene-set-based inference of biological network topologies from big molecular profiling data Lipi Acharya and Dongxiao Zhu; 15. Large scale correlation mining for biomolecular network discovery Alfred Hero and Bala Rajaratnam.
£47.99
Cambridge University Press A HandsOn Introduction to Data Science
Book SynopsisThis book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.Trade Review'Chirag's extensive experience as a teacher shines through in this textbook, which lives up to its promise to be a 'hands on' introduction to data science. Students have a chance to apply their learning to real-life examples from diverse fields, with hands-on examples that build on basic techniques and utilize tools of data science practice throughout the book. I am particularly pleased to see him weave human issues into his approach, putting principles ahead of particular tools and pointing to ethical challenges at various stages of working with data to help his audience develop an appreciation of ways context and interpretation shape data practices. He exposes students to a more nuanced perspective in which human as well as machine input shapes data science outcomes. It is an awareness that we all will need if we are to use data appropriately to tackle the complex challenges we face today.' Theresa Dirndorfer Anderson'Dr. Shah has written a fabulous introduction to data science for a broad audience. His book offers many learning opportunities, including explanations of core principles, thought-provoking conceptual questions, and hands-on examples and exercises. It will help readers gain proficiency in this important area and quickly start deriving insights from data.' Ryen W. White, Microsoft Research AITable of ContentsPart I. Introduction: 1. Introduction; 2. Data; 3. Techniques; Part II. Tools: 4. UNIX; 5. Python; 6. R; 7. MySQL; Part III. Machine Learning: 8. Machine learning introduction and regression; 9. Supervised learning; 10. Unsupervised learning; Part IV. Applications and Evaluations: 11. Hands-on with solving data problems; 12. Data collection, experimentation and evaluation.
£41.79
Cambridge University Press Algorithmic Randomness
Book SynopsisThe last two decades have seen a wave of exciting new developments in the theory of algorithmic randomness and its applications to other areas of mathematics. This volume surveys much of the recent work that has not been included in published volumes until now. It contains a range of articles on algorithmic randomness and its interactions with closely related topics such as computability theory and computational complexity, as well as wider applications in areas of mathematics including analysis, probability, and ergodic theory. In addition to being an indispensable reference for researchers in algorithmic randomness, the unified view of the theory presented here makes this an excellent entry point for graduate students and other newcomers to the field.Table of Contents1. Key developments in algorithmic randomness Johanna N. Y. Franklin and Christopher P. Porter; 2. Algorithmic randomness in ergodic theory Henry Towsner; 3. Algorithmic randomness and constructive/computable measure theory Jason Rute; 4. Algorithmic randomness and layerwise computability Mathieu Hoyrup; 5. Relativization in randomness Johanna N. Y. Franklin; 6. Aspects of Chaitin's Omega George Barmpalias; 7. Biased algorithmic randomness Christopher P. Porter; 8. Higher randomness Benoit Monin; 9. Resource bounded randomness and its applications Donald M. Stull; Index.
£95.95
Cambridge University Press Computational Approaches to the Network Science
Book SynopsisBusiness operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team''s performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends.Trade Review'This is a timely book for team science, with a unique perspective that uses computational approaches to study the network effect on team performance. The book has a nice balance of theory, algorithms, and empirical studies. The authors possess years of experience in the field.' Charu Aggarwal, IBM Research AI'A comprehensive study that pushes forward our understanding of and ability to forecast and design team performance - a critical, yet complex human-subject phenomenon to which this book brings in-depth technical rigor.' Leman Akoglu, Carnegie Mellon University'This pioneering book is essential to technologists, data scientists, and researchers alike, offering a modern, computational approach to the science of teaming and how to manage the convergence of people, information, and technology in networked organizations.' Norbou Buchler, US Army Data and Analysis Center'Li and Tong have provided a thorough and insightful exploration of current research on teams in networks, linking computational techniques with results from the social sciences. A pleasure to read.' Sucheta Soundarajan, Syracuse University'This brief volume is a valuable resource for managers, but managers with a strong background in data science, and for other technologists involved in designing systems that support user interactions … The added value of this book is provided by the mathematical formalisms used, which encode characteristics of the computational challenges discussed … The topical focus results in a unique volume that might lead interested readers to discover new research avenues … Recommended' J. Brzezinski, ChoiceTable of Contents1. Introduction; 2. Team performance characterization; 3. Team performance prediction; 4. Team performance optimization; 5. Team performance explanation; 6. Human agent teaming; 7. Conclusion and future work.
£41.79
John Wiley & Sons Inc Smart Grid using Big Data Analytics
Book SynopsisThis book is aimed at students in communications and signal processing who want to extend their skills in the energy area. It describes power systems and why these backgrounds are so useful to smart grid, wireless communications being very different to traditional wireline communications.Table of ContentsPreface xv Acknowledgments xix Some Notation xxi 1 Introduction 1 1.1 Big Data: Basic Concepts 1 1.2 Data Mining with Big Data 9 1.3 A Mathematical Introduction to Big Data 13 1.4 A Mathematical Theory of Big Data 28 1.5 Smart Grid 34 1.6 Big Data and Smart Grid 36 1.7 Reading Guide 37 Bibliographical Remarks 39 Part I Fundamentals of Big Data 41 2 The Mathematical Foundations of Big Data Systems 43 2.1 Big Data Analytics 44 2.2 Big Data: Sense, Collect, Store, and Analyze 45 2.3 Intelligent Algorithms 48 2.4 Signal Processing for Smart Grid 48 2.5 Monitoring and Optimization for Power Grids 48 2.6 Distributed Sensing and Measurement for Power Grids 49 2.7 Real-time Analysis of Streaming Data 50 2.8 Salient Features of Big Data 51 2.9 Big Data for Quantum Systems 54 2.10 Big Data for Financial Systems 55 2.11 Big Data for Atmospheric Systems 73 2.12 Big Data for Sensing Networks 74 2.13 Big Data forWireless Networks 75 2.14 Big Data for Transportation 78 Bibliographical Remarks 78 3 Large Random Matrices: An Introduction 79 3.1 Modeling of Large Dimensional Data as Random Matrices 79 3.2 A Brief of Random MatrixTheory 81 3.3 Change Point of Views: From Vectors to Measures 85 3.4 The Stieltjes Transform of Measures 86 3.5 A Fundamental Result: The Marchenko–Pastur Equation 88 3.6 Linear Eigenvalue Statistics and Limit Laws 89 3.7 Central LimitTheorem for Linear Eigenvalue Statistics 99 3.8 Central LimitTheorem for Random Matrix S−1T 101 3.9 Independence for Random Matrices 103 3.10 Matrix-Valued Gaussian Distribution 110 3.11 Matrix-ValuedWishart Distribution 112 3.12 Moment Method 112 3.13 Stieltjes Transform Method 113 3.14 Concentration of the Spectral Measure for Large Random Matrices 114 3.15 Future Directions 117 Bibliographical Remarks 117 4 Linear Spectral Statistics of the Sample Covariance Matrix 121 4.1 Linear Spectral Statistics 121 4.2 Generalized Marchenko–Pastur Distributions 122 4.3 Estimation of Spectral Density Functions 127 4.4 Limiting Spectral Distribution of Time Series 146 Bibliographical Remarks 154 5 Large Hermitian Random Matrices and Free Random Variables 155 5.1 Large Economic/Financial Systems 156 5.2 Matrix-Valued Probability 157 5.3 Wishart-Levy Free Stable Random Matrices 166 5.4 Basic Concepts for Free Random Variables 168 5.5 The Analytical Spectrum of theWishart–Levy Random Matrix 172 5.6 Basic Properties of the Stieltjes Transform 176 5.7 Basic Theorems for the Stieltjes Transform 179 5.8 Free Probability for Hermitian Random Matrices 185 5.9 Random Vandermonde Matrix 196 5.10 Non-Asymptotic Analysis of State Estimation 200 Bibliographical Remarks 201 6 Large Non-Hermitian Random Matrices and Quatartenionic Free Probability Theory 203 6.1 Quatartenionic Free ProbabilityTheory 204 6.2 R-diagonalMatrices 209 6.3 The Sum of Non-Hermitian Random Matrices 216 6.4 The Product of Non-Hermitian Random Matrices 220 6.5 Singular Value Equivalent Models 226 6.6 The Power of the Non-Hermitian Random Matrix 234 6.7 Power Series of Large Non-Hermitian Random Matrices 239 6.8 Products of Random Ginibre Matrices 246 6.9 Products of Rectangular Gaussian Random Matrices 249 6.10 Product of ComplexWishart Matrices 252 6.11 Spectral Relations between Products and Powers 254 6.12 Products of Finite-Size I.I.D. Gaussian Random Matrices 258 6.13 Lyapunov Exponents for Products of Complex Gaussian Random Matrices 260 6.14 Euclidean Random Matrices 264 6.15 Random Matrices with Independent Entries and the Circular Law 273 6.16 The Circular Law and Outliers 275 6.17 Random SVD, Single Ring Law, and Outliers 285 6.18 The Elliptic Law and Outliers 295 Bibliographical Remarks 305 7 The Mathematical Foundations of Data Collection 307 7.1 Architectures and Applications for Big Data 307 7.2 Covariance Matrix Estimation 308 7.3 Spectral Estimators for Large Random Matrices 312 7.4 Asymptotic Framework for Matrix Reconstruction 319 7.5 Optimum Shrinkage 329 7.6 A Shrinkage Approach to Large-Scale Covariance Matrix Estimation 331 7.7 Eigenvectors of Large Sample Covariance Matrix Ensembles 338 7.8 A General Class of Random Matrices 351 Bibliographical Remarks 359 8 Matrix Hypothesis Testing using Large RandomMatrices 361 8.1 Motivating Examples 362 8.2 Hypothesis Test of Two Alternative Random Matrices 363 8.3 Eigenvalue Bounds for Expectation and Variance 364 8.4 Concentration of Empirical Distribution Functions 369 8.5 Random Quadratic Forms 381 8.6 Log-Determinant of Random Matrices 382 8.7 General MANOVA Matrices 383 8.8 Finite Rank Perturbations of Large Random Matrices 386 8.9 Hypothesis Tests for High-Dimensional Datasets 391 8.9.1 Motivation for Likelihood Ratio Test (LRT) and Covariance Matrix Tests 392 8.10 Roy’s Largest Root Test 428 8.11 Optimal Tests of Hypotheses for Large Random Matrices 431 8.12 Matrix Elliptically Contoured Distributions 444 8.13 Hypothesis Testing for Matrix Elliptically Contoured Distributions 446 Bibliographical Remarks 452 Part II Smart Grid 455 9 Applications and Requirements of Smart Grid 457 9.1 History 457 9.2 Concepts and Vision 458 9.3 Today’s Electric Grid 459 9.4 Future Smart Electrical Energy System 464 10 Technical Challenges for Smart Grid 471 Bibliographical Remarks 483 11 Big Data for Smart Grid 485 11.1 Power in Numbers: Big Data and Grid Infrastructure 485 11.2 Energy’s Internet:The Convergence of Big Data and the Cloud 486 11.3 Edge Analytics: Consumers, Electric Vehicles, and Distributed Generation 486 11.4 CrosscuttingThemes: Big Data 486 11.5 Cloud Computing for Smart Grid 488 11.6 Data Storage, Data Access and Data Analysis 488 11.7 The State-of-the-Art Processing Techniques of Big Data 488 11.8 Big Data Meets the Smart Electrical Grid 488 11.9 4Vs of Big Data: Volume, Variety, Value and Velocity 489 11.10 Cloud Computing for Big Data 490 11.11 Big Data for Smart Grid 490 11.12 Information Platforms for Smart Grid 491 Bibliographical Remarks 491 12 Grid Monitoring and State Estimation 493 12.1 Phase Measurement Unit 493 12.2 Optimal PMU Placement 495 12.3 State Estimation 495 12.4 Basics of State Estimation 495 12.5 Evolution of State Estimation 496 12.6 Static State Estimation 497 12.7 Forecasting-Aided State Estimation 500 12.8 Phasor Measurement Units 501 12.9 Distributed System State Estimation 502 12.10 Event-Triggered Approaches to State Estimation 502 12.11 Bad Data Detection 502 12.12 Improved Bad Data Detection 504 12.13 Cyber-Attacks 504 12.14 Line Outage Detection 504 Bibliographical Remarks 504 13 False Data Injection Attacks against State Estimation 505 13.1 State Estimation 505 13.2 False Data Injection Attacks 507 13.3 MMSE State Estimation and Generalized Likelihood Ratio Test 508 13.4 Sparse Recovery from Nonlinear Measurements 512 13.5 Real-Time Intrusion Detection 515 Bibliographical Remarks 515 14 Demand Response 517 14.1 Why Engage Demand? 517 14.2 Optimal Real-time Pricing Algorithms 520 14.3 Transportation Electrification and Vehicle-to-Grid Applications 522 14.4 Grid Storage 522 Bibliographical Remarks 523 Part III Communications and Sensing 525 15 Big Data for Communications 527 15.1 5G and Big Data 527 15.2 5GWireless Communication Networks 527 15.3 Massive Multiple Input, Multiple Output 528 15.4 Free Probability for the Capacity of the Massive MIMO Channel 537 15.5 Spectral Sensing for Cognitive Radio 539 Bibliographical Remarks 539 16 Big Data for Sensing 541 16.1 Distributed Detection and Estimation 541 16.2 Euclidean Random Matrix 547 16.3 Decentralized Computing 548 Appendix A: Some Basic Results on Free Probability 551 Appendix B: Matrix-Valued Random Variables 557 References 567 Index 601
£99.86
John Wiley & Sons Inc Data Science and Big Data Analytics
Book SynopsisData Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use.Table of ContentsIntroduction xvii Chapter 1 Introduction to Big Data Analytics 1 1.1 Big Data Overview 2 1.1.1 Data Structures 5 1.1.2 Analyst Perspective on Data Repositories 9 1.2 State of the Practice in Analytics 11 1.2.1 BI Versus Data Science 12 1.2.2 Current Analytical Architecture 13 1.2.3 Drivers of Big Data 15 1.2.4 Emerging Big Data Ecosystem and a New Approach to Analytics 16 1.3 Key Roles for the New Big Data Ecosystem 19 1.4 Examples of Big Data Analytics 22 Summary 23 Exercises 23 Bibliography 24 Chapter 2 Data Analytics Lifecycle 25 2.1 Data Analytics Lifecycle Overview 26 2.1.1 Key Roles for a Successful Analytics Project 26 2.1.2 Background and Overview of Data Analytics Lifecycle 28 2.2 Phase 1: Discovery 30 2.2.1 Learning the Business Domain 30 2.2.2 Resources 31 2.2.3 Framing the Problem 32 2.2.4 Identifying Key Stakeholders 33 2.2.5 Interviewing the Analytics Sponsor 33 2.2.6 Developing Initial Hypotheses 35 2.2.7 Identifying Potential Data Sources 35 2.3 Phase 2: Data Preparation 36 2.3.1 Preparing the Analytic Sandbox 37 2.3.2 Performing ETLT 38 2.3.3 Learning About the Data 39 2.3.4 Data Conditioning 40 2.3.5 Survey and Visualize 41 2.3.6 Common Tools for the Data Preparation Phase 42 2.4 Phase 3: Model Planning 42 2.4.1 Data Exploration and Variable Selection 44 2.4.2 Model Selection 45 2.4.3 Common Tools for the Model Planning Phase 45 2.5 Phase 4: Model Building 46 2.5.1 Common Tools for the Model Building Phase 48 2.6 Phase 5: Communicate Results 49 2.7 Phase 6: Operationalize 50 2.8 Case Study: Global Innovation Network and Analysis (GINA) 53 2.8.1 Phase 1: Discovery 54 2.8.2 Phase 2: Data Preparation 55 2.8.3 Phase 3: Model Planning 56 2.8.4 Phase 4: Model Building 56 2.8.5 Phase 5: Communicate Results 58 2.8.6 Phase 6: Operationalize 59 Summary 60 Exercises 61 Bibliography 61 Chapter 3 Review of Basic Data Analytic Methods Using R 63 3.1 Introduction to R 64 3.1.1 R Graphical User Interfaces 67 3.1.2 Data Import and Export 69 3.1.3 Attribute and Data Types 71 3.1.4 Descriptive Statistics 79 3.2 Exploratory Data Analysis 80 3.2.1 Visualization Before Analysis 82 3.2.2 Dirty Data 85 3.2.3 Visualizing a Single Variable 88 3.2.4 Examining Multiple Variables 91 3.2.5 Data Exploration Versus Presentation 99 3.3 Statistical Methods for Evaluation 101 3.3.1 Hypothesis Testing 102 3.3.2 Difference of Means 104 3.3.3 Wilcoxon Rank-Sum Test 108 3.3.4 Type I and Type II Errors 109 3.3.5 Power and Sample Size 110 3.3.6 ANOVA 110 Summary 114 Exercises 114 Bibliography 115 Chapter 4 Advanced Analytical Theory and Methods: Clustering 117 4.1 Overview of Clustering 118 4.2 K-means 118 4.2.1 Use Cases 119 4.2.2 Overview of the Method 120 4.2.3 Determining the Number of Clusters 123 4.2.4 Diagnostics 128 4.2.5 Reasons to Choose and Cautions 130 4.3 Additional Algorithms 134 Summary 135 Exercises 135 Bibliography 136 Chapter 5 Advanced Analytical Theory and Methods: Association Rules 137 5.1 Overview 138 5.2 Apriori Algorithm 140 5.3 Evaluation of Candidate Rules 141 5.4 Applications of Association Rules 143 5.5 An Example: Transactions in a Grocery Store 143 5.5.1 The Groceries Dataset 144 5.5.2 Frequent Itemset Generation 146 5.5.3 Rule Generation and Visualization 152 5.6 Validation and Testing 157 5.7 Diagnostics 158 Summary 158 Exercises 159 Bibliography 160 Chapter 6 Advanced Analytical Theory and Methods: Regression 161 6.1 Linear Regression 162 6.1.1 Use Cases 162 6.1.2 Model Description 163 6.1.3 Diagnostics 173 6.2 Logistic Regression 178 6.2.1 Use Cases 179 6.2.2 Model Description 179 6.2.3 Diagnostics 181 6.3 Reasons to Choose and Cautions 188 6.4 Additional Regression Models 189 Summary 190 Exercises 190 Chapter 7 Advanced Analytical Theory and Methods: Classification 191 7.1 Decision Trees 192 7.1.1 Overview of a Decision Tree 193 7.1.2 The General Algorithm 197 7.1.3 Decision Tree Algorithms 203 7.1.4 Evaluating a Decision Tree 204 7.1.5 Decision Trees in R 206 7.2 Naïve Bayes 211 7.2.1 Bayes’ Theorem 212 7.2.2 Naïve Bayes Classifier 214 7.2.3 Smoothing 217 7.2.4 Diagnostics 217 7.2.5 Naïve Bayes in R 218 7.3 Diagnostics of Classifiers 224 7.4 Additional Classification Methods 228 Summary 229 Exercises 230 Bibliography 231 Chapter 8 Advanced Analytical Theory and Methods: Time Series Analysis 233 8.1 Overview of Time Series Analysis 234 8.1.1 Box-Jenkins Methodology 235 8.2 ARIMA Model 236 8.2.1 Autocorrelation Function (ACF) 236 8.2.2 Autoregressive Models 238 8.2.3 Moving Average Models 239 8.2.4 ARMA and ARIMA Models 241 8.2.5 Building and Evaluating an ARIMA Model 244 8.2.6 Reasons to Choose and Cautions 252 8.3 Additional Methods 253 Summary 254 Exercises 254 Chapter 9 Advanced Analytical Theory and Methods: Text Analysis 255 9.1 Text Analysis Steps 257 9.2 A Text Analysis Example 259 9.3 Collecting Raw Text 260 9.4 Representing Text 264 9.5 Term Frequency—Inverse Document Frequency (TFIDF) 269 9.6 Categorizing Documents by Topics 274 9.7 Determining Sentiments 277 9.8 Gaining Insights 283 Summary 290 Exercises 290 Bibliography 291 Chapter 10 Advanced Analytics—Technology and Tools: MapReduce and Hadoop 295 10.1 Analytics for Unstructured Data 296 10.1.1 Use Cases 296 10.1.2 MapReduce 298 10.1.3 Apache Hadoop 300 10.2 The Hadoop Ecosystem 306 10.2.1 Pig 306 10.2.2 Hive 308 10.2.3 HBase 311 10.2.4 Mahout 319 10.3 NoSQL 322 Summary 323 Exercises 324 Bibliography 324 Chapter 11 Advanced Analytics—Technology and Tools: In-Database Analytics 327 11.1 SQL Essentials 328 11.1.1 Joins 330 11.1.2 Set Operations 332 11.1.3 Grouping Extensions 334 11.2 In-Database Text Analysis 338 11.3 Advanced SQL 343 11.3.1 Window Functions 343 11.3.2 User-Defined Functions and Aggregates 347 11.3.3 Ordered Aggregates 351 11.3.4 MADlib 352 Summary 356 Exercises 356 Bibliography 357 Chapter 12 The Endgame, or Putting It All Together 359 12.1 Communicating and Operationalizing an Analytics Project 360 12.2 Creating the Final Deliverables 362 12.2.1 Developing Core Material for Multiple Audiences 364 12.2.2 Project Goals 365 12.2.3 Main Findings 367 12.2.4 Approach 369 12.2.5 Model Description 371 12.2.6 Key Points Supported with Data 372 12.2.7 Model Details 372 12.2.8 Recommendations 374 12.2.9 Additional Tips on Final Presentation 375 12.2.10 Providing Technical Specifications and Code 376 12.3 Data Visualization Basics 377 12.3.1 Key Points Supported with Data 378 12.3.2 Evolution of a Graph 380 12.3.3 Common Representation Methods 386 12.3.4 How to Clean Up a Graphic 387 12.3.5 Additional Considerations 392 Summary 393 Exercises 394 References and Further Reading 394 Bibliography 394 Index 397
£47.50
John Wiley & Sons Inc Big Data Revolution
Book SynopsisExploit the power and potential of Big Data to revolutionize business outcomes Big Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data.Table of ContentsPrologue 1 Berkeley, 1930s 1 Pattern Recognition 2 Nelson Peltz 3 Committing to One Percent 5 The Big Data Revolution 6 Introduction 7 Storytelling 7 Objective 7 Outline 8 Part I “The Revolution Starts Now: 9 Industries Transforming with Data” 8 Part II “Learning from Patterns in Big Data” 11 Part III “Leading the Revolution” 11 Storytelling (Continued) 13 Part I: the Revolution Starts Now: 9 Industries Transforming With Data 15 Chapter 1: Transforming Farms with Data 17 California, 2013 17 Brief History of Farming 18 The Data Era 19 Potato Farming 20 Precision Farming 21 Capturing Farm Data 22 Deere & Company Versus Monsanto 24 Integrated Farming Systems 25 Data Prevails 26 The Climate Corporation 26 Growsafe Systems 27 Farm of the Future 27 California, 2013 (Continued) 29 Chapter 2: Why Doctors Will Have Math Degrees 31 United States, 2014 31 The History of Medical Education 32 Scientific Method 32 Rise of Specialists 33 We Have a Problem 34 Ben Goldacre 35 Vinod Khosla 35 The Data Era 36 Collecting Data 36 Telemedicine 38 Innovating with Data 40 Implications of a Data-Driven Medical World 42 The Future of Medical School 42 A Typical Medical School 42 A Medical School for the Data Era 43 United States, 2030 44 Chapter 3: Revolutionizing Insurance: Why Actuaries Will Become Data Scientists 45 Middle of Somewhere, 2012 45 Short History of Property & Casualty Insurance and Underwriting 46 Actuarial Science In Insurance 47 Pensions, Insurance, Leases 49 Compound Interest 50 Probability 50 Mortality Data 50 Modern-Day Insurance 51 Eight Weeks to Eight Days 51 Online Policies 52 The Data Era 52 Dynamic Risk Management 52 Catastrophe Risk 54 Open Access Modeling 55 Opportunities 56 Middle of Somewhere, 2012 (Continued) 58 Chapter 4: Personalizing Retail and Fashion 59 Karolina 59 A Brief History of Retail 60 Retail Eras 60 Aristide Boucicaut 61 The Shift 62 The Data Era 63 Stitch Fix 63 Keaton Row 65 Zara 66 Karolina (Continued) 67 Chapter 5: Transforming Customer Relationships with Data 69 Buying a House 69 Brief History of Customer Service 70 Customer Service Over Time 70 Boeing 72 Financial Services 74 The Data Era 75 An Automobile Manufacturer 76 Zendesk 76 Buying a House (Continued) 77 Chapter 6: Intelligent Machines 79 Denmark 79 Intelligent Machines 80 Machine Data 81 The Data Era 82 General Electric 82 Drones 84 Tesla 86 Networks of Data 87 Denmark (Continued) 88 Chapter 7: Government and Society 89 Egypt, 2011 89 Social Media 90 Intelligence 90 Snowden Effect 91 Privacy Risk Versus Reward 91 Observation or Surveillance 93 Development Targets 93 Open Data 95 Hackathons 95 Open Access 95 Ensuring Personal Protection 96 Private Clouds 97 Sanitizing Data 97 Evidence-Based Policy 97 Public-Private Partnerships 98 Impact Bonds 101 Social Impact Bond 102 Development Impact Bonds 103 The Role of Big Data 104 Egypt, 2011 (Continued) 105 Chapter 8: Corporate Sustainability 107 City of London 107 Global Megaforces 109 Population 109 Carbon Footprint 110 Water Scarcity 110 Environmental Risk 111 BP and Exxon Mobile 111 Early Warning Systems 112 Social Media 113 Risk and Resilience 114 Measuring Sustainability 115 Long-Term Decision Making 116 Stranded Assets 117 City of London (Continued) 118 Chapter 9: Weather and Energy 119 India, 2012 119 The Weather 120 Forecasting the Weather 120 When are Weather Forecasts Wrong? 121 Chaos 122 Ensemble Forecasts 122 Communication 123 Renewable Energy 124 Solar, Hydro, and Wind Power 124 Volatile or Intermittent Supply 125 Energy Consumption 126 Smart Meters 127 Intelligent Demand-Side Management 128 India, 2012 (Continued) 129 Part II: Learning From Patterns in Big Data 131 Chapter 10: Pattern Recognition 133 Elements of Success Rhyme 133 Pattern Recognition: A Gift or Trap? 134 What Fish Teach Us About Pattern Recognition 135 Bayes’ Theorem 135 Tsukiji Market 135 Pattern Recognition 137 Rochester Institute of Technology 137 A Method for Recognizing Patterns 137 Elements of Success Rhyme (Continued) 140 Chapter 11: Why Patterns in Big Data Have Emerged 141 Meatpacking District 141 Business Models in the Data Era 142 Data as a Competitive Advantage 143 Data Improves Existing Products or Services 145 Data as the Product 145 Dun & Bradstreet 146 CoStar 148 Ihs 149 Meatpacking District (Continued) 151 Chapter 12: Patterns in Big Data 153 The Data Factor 154 Summary of Big Data Patterns 155 Redefining a Skilled Worker 155 Creating and Utilizing New Sources of Data 156 Building New Data Applications 157 Transforming and Creating New Business Processes 157 Data Collection for Competitive Advantage 158 Exposing Opinion-Based Biases 159 Real-Time Monitoring and Decision Making 159 Social Networks Leveraging and Creating Data 160 Deconstructing the Value Chain 161 New Product Offerings 161 Building for Customers Instead of Markets 162 Tradeoff Between Privacy and Insight 163 Changing the Definition of a Product 163 Inverting the Search Paradigm for Data Discovery 164 Data Security 165 New Partnerships Founded on Data 165 Shortening the Innovation Lifecycle 166 Defining New Channels to Market 166 New Economic Models 167 Forecasting and Predicting Future Events 168 Changing Incentives 168 New Partnerships (Public/Private) 169 Real-Time Monitoring and Decision Making (Early Warning Systems) 169 A Framework for Big Data Patterns 170 Part III: Leading the Revolution 171 Chapter 13: The Data Opportunity 173 What Oil Teaches Us About Data 173 Bain Study 175 Seizing the Opportunity 176 Chapter 14: Porsche 177 Rome 177 Ferdinand Porsche 178 The Birth of Porsche 178 The Porsche Sports Car 179 Porsche Today 180 Rome (Continued) 180 Chapter 15: Puma 181 Herzogenaurach 181 Advertising Wars 182 Jochen Zeitz 182 Environmental Profit and Loss 183 Herzogenaurach (Continued) 184 Chapter 16: A Methodology for Applying Big Data Patterns 185 Introduction 185 The Method 186 Step 1: Understand Data Assets 187 The Patterns 188 Step 2: Explore Data 191 Challenges 192 Questions 192 Hypotheses 193 Data 193 Models 193 Statistical Significance 194 Step 3: Design the Future 194 The Patterns 195 Step 4: Design a Data-Driven Business Model 197 The Patterns 197 Step 5: Transform Business Processes for the Data Era 199 The Patterns 199 Step 6: Design for Governance and Security 201 The Patterns 201 Step 7: Share Metrics and Incentives 202 Chapter 17: Big Data Architecture 205 Introduction 205 Architect for the Future 206 Lessons from Stuttgart 207 Big Data Reference Architectures 207 Leveraging Investments in Architecture 208 Big Data Reference Architectures 211 Business View 212 Logical View 213 Chapter 18: Business View Reference Architecture 215 Introduction 215 Men’s Trunk: A Retailer in the Data Era 216 The Business View Reference Architecture 217 Answer Fabric 218 Data Virtualization 219 Data Engines 220 Management 221 Data Governance 221 User Interface, Applications, and Business Processes 222 Summary 222 Chapter 19: Logical View Reference Architecture 223 Introduction 223 Men’s Trunk: A Retailer in the Data Era (Continued) 224 The Logical View Reference Architecture 226 Data Ingest 227 Analytics 227 Discovery 228 Landing 228 Operational Warehouse 229 Information Insight 230 Operational Data 231 Governance 231 Men’s Trunk: A Retailer in the Data Era (Continued) 232 Chapter 20: The Architecture of the Future 233 Men’s Trunk: A Retailer in the Data Era (Continued) 233 Men’s Trunk: Applying the Methodology 235 Step 1: Understand Data Assets 235 Step 2: Explore the Data 236 Step 3: Design the Future 237 Step 4: Design a Data-Driven Business Model 237 Step 5: Transform Business Processes for the Data Era 237 Step 6: Design for Governance and Security 237 Step 7: Share Metrics and Incentives 238 Men’s Trunk: The Business View Reference Architecture 239 Answer Fabric 240 Data Virtualization 241 Data Engines 241 Management 242 Data Governance 242 User Interface, Applications, and Business Processes 243 Men’s Trunk: The Logical View Reference Architecture 244 Approach 244 Men’s Trunk: A Retailer in the Data Era (Continued) 248 Epilogue 249 The Time is Now 249 Taking Action 250 Fear not Usual Competitors 251 The Future 252 Index 255
£16.15
John Wiley & Sons Inc Strategies in Biomedical Data Science
Book SynopsisAn essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data.Table of ContentsForeword xi Acknowledgments xv Introduction 1 Who Should Read This Book? 3 What’s in This Book? 4 How to Contact Us 6 Chapter 1 Healthcare, History, and Heartbreak 7 Top Issues in Healthcare 9 Data Management 16 Biosimilars, Drug Pricing, and Pharmaceutical Compounding 18 Promising Areas of Innovation 19 Conclusion 25 Notes 25 Chapter 2 Genome Sequencing: Know Thyself, One Base Pair at a Time 27 Content contributed by Sheetal Shetty and Jacob Brill Challenges of Genomic Analysis 29 The Language of Life 30 A Brief History of DNA Sequencing 31 DNA Sequencing and the Human Genome Project 35 Select Tools for Genomic Analysis 38 Conclusion 47 Notes 48 Chapter 3 Data Management 53 Content contributed by Joe Arnold Bits about Data 54 Data Types 56 Data Security and Compliance 59 Data Storage 66 SwiftStack 70 OpenStack Swift Architecture 78 Conclusion 94 Notes 94 Chapter 4 Designing a Data-Ready Network Infrastructure 105 Research Networks: A Primer 108 ESnet at 30: Evolving toward Exascale and Raising Expectations 109 Internet2 Innovation Platform 111 Advances in Networking 113 InfiniBand and Microsecond Latency 114 The Future of High-Performance Fabrics 117 Network Function Virtualization 119 Software-Defined Networking 121 OpenDaylight 122 Conclusion 157 Notes 157 Chapter 5 Data-Intensive Compute Infrastructures 163 Content contributed by Dijiang Huang, Yuli Deng, Jay Etchings, Zhiyuan Ma, and Guangchun Luo Big Data Applications in Health Informatics 166 Sources of Big Data in Health Informatics 168 Infrastructure for Big Data Analytics 171 Fundamental System Properties 186 GPU-Accelerated Computing and Biomedical Informatics 187 Conclusion 190 Notes 191 Chapter 6 Cloud Computing and Emerging Architectures 211 Cloud Basics 213 Challenges Facing Cloud Computing Applications in Biomedicine 215 Hybrid Campus Clouds 216 Research as a Service 217 Federated Access Web Portals 219 Cluster Homogeneity 220 Emerging Architectures (Zeta Architecture) 221 Conclusion 229 Notes 229 Chapter 7 Data Science 235 NoSQL Approaches to Biomedical Data Science 237 Using Splunk for Data Analytics 244 Statistical Analysis of Genomic Data with Hadoop 250 Extracting and Transforming Genomic Data 253 Processing eQTL Data 256 Generating Master SNP Files for Cases and Controls 259 Generating Gene Expression Files for Cases and Controls 260 Cleaning Raw Data Using MapReduce 261 Transpose Data Using Python 263 Statistical Analysis Using Spark 264 Hive Tables with Partitions 268 Conclusion 270 Notes 270 Appendix: A Brief Statistics Primer 290 Content Contributed by Daniel Peñaherrera Chapter 8 Next-Generation Cyberinfrastructures 307 Next-Generation Cyber Capability 308 NGCC Design and Infrastructure 310 Conclusion 327 Note 330 Conclusion 335 Appendix A The Research Data Management Survey: From Concepts to Practice 337 Brandon Mikkelsen and Jay Etchings Appendix B Central IT and Research Support 353 Gregory D. Palmer Appendix C HPC Working Example: Using Parallelization Programs Such as GNU Parallel and OpenMP with Serial Tools 377 Appendix D HPC and Hadoop: Bridging HPC to Hadoop 385 Appendix E Bioinformatics + Docker: Simplifying Bioinformatics Tools Delivery with Docker Containers 391 Glossary 399 About the Author 419 About the Contributors 421 Index 427
£45.00
John Wiley & Sons Inc Big Data and Machine Learning in Quantitative
Book SynopsisGet to know the why' and how' of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it's a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. Gain a solid reason to use machine learning Frame your question using financial markets laws Know your data Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effectivTable of ContentsCHAPTER 1 Do Algorithms Dream About Artificial Alphas? 1By Michael Kollo CHAPTER 2 Taming Big Data 13By Rado Lipuš and Daryl Smith CHAPTER 3 State of Machine Learning Applications in Investment Management 33By Ekaterina Sirotyuk CHAPTER 4 Implementing Alternative Data in an Investment Process 51By Vinesh Jha CHAPTER 5 Using Alternative and Big Data to Trade Macro Assets 75By Saeed Amen and Iain Clark CHAPTER 6 Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales 95By Giuliano De Rossi, Jakub Kolodziej and Gurvinder Brar CHAPTER 7 Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework 129By Tony Guida and Guillaume Coqueret CHAPTER 8 A Social Media Analysis of Corporate Culture 149By Andy Moniz CHAPTER 9 Machine Learning and Event Detection for Trading Energy Futures 169By Peter Hafez and Francesco Lautizi CHAPTER 10 Natural Language Processing of Financial News 185By M. Berkan Sesen, Yazann Romahi and Victor Li CHAPTER 11 Support Vector Machine-Based Global Tactical Asset Allocation 211By Joel Guglietta CHAPTER 12 Reinforcement Learning in Finance 225By Gordon Ritter CHAPTER 13 Deep Learning in Finance: Prediction of Stock Returns with Long Short-Term Memory Networks 251By Miquel N. Alonso, Gilberto Batres-Estrada and Aymeric Moulin Biography 279
£37.80
John Wiley & Sons Inc Understanding Databases
Book SynopsisUnderstanding Databases: Concepts and Practice is an accessible, highly visual introduction to database systems for undergraduate students across many majors. Designed for self-contained first courses in the subject, this interactive e-textbook covers fundamental database topics including conceptual design, the relational data model, relational algebra and calculus, Structured Query Language (SQL), database manipulation, transaction management, and database design theory. Visual components and self-assessment features provide a more engaging and immersive method of learning that enables students to develop a solid foundation in both database theory and practical application. Concise, easy-to-digest chapters offer ample opportunities for students to practice and master the material, and include a variety of solved real-world problems, self-check questions, and hands-on collaborative activities that task students to build a functioning database. This Enhanced eText also Table of ContentsPreface xi 1 Introduction to Databases and the Relational Data Model 1 1.1 Databases are a Tool 2 1.2 Overview of Data and Models 6 1.3 The Relational Data Model 14 1.3.1 Definition 14 1.3.2 Uniqueness 16 1.3.3 Referential Integrity 20 Special Topic: Primary Key and Referential Integrity 26 1.3.4 Additional Constraints 27 2 Conceptual Design 43 2.1 Gathering Requirements 44 2.2 Entity-Relationship Diagrams 46 How To: Design an Entity-Relationship Diagram 50 Special Topic: (Min, Max) Pairs 54 Special Topic: Recursive Relationships and Role Names 54 Special Topic: Ternary Relationships 56 Special Topic: EER for Modeling Inheritance 56 2.3 Mapping ER Diagrams to Tables 62 How To: Map an ER Diagram to Relations 67 2.4 Other Graphical Approaches 73 3 Relational Algebra 103 3.1 Query Design 104 How To: Query Design 104 3.2 Algebra Operators 109 Note: Overview of Relational Algebra 110 3.2.1 Filtering 111 3.2.2 Sets 114 3.2.3 Joins 116 3.2.4 Division 119 3.3 Relational Completeness 127 3.4 Query Optimization 134 How To: Heuristic Query Optimization 136 4 Relational Calculus 161 4.1 Logical Foundations 162 Note: Overview of Relational Calculus Languages 163 4.2 Tuple Relational Calculus 164 4.2.1 Fundamental Query Expressions 165 How To: Writing a Fundamental Query in TRC 165 vi 4.2.2 Quantification of Variables 167 4.2.3 Atoms and Formula 172 4.2.4 Relational Completeness 175 4.3 Domain Relational Calculus 183 4.3.1 Fundamental Query Expressions 183 How To: Writing a Fundamental Query in DRC 184 4.3.2 Quantification of Variables 187 4.3.3 Atoms and Formula 193 4.3.4 Relational Completeness 195 4.4 Safety 204 5 SQL: An Introduction to Querying 237 5.1 Foundations 237 Note: SQL Syntax 238 Syntax: Basic SQL Query 240 5.2 Fundamental Query Expressions 246 How To: Writing a Fundamental Query in SQL 246 5.2.1 Queries involving One Table 247 5.2.2 Queries involving Multiple Tables 250 How To: Writing a Reflection Query 255 5.3 Nested Queries 261 Special Topic: A glimpse at query optimization 264 Special Topic: Views and Inline Views 266 5.4 Set Operators 270 5.5 Aggregation and Grouping 276 Special Topic: Arithmetic Expressions 281 5.6 Querying with null Values 285 5.7 Relational Completeness 289 5.7.1 Fundamental Operators 290 5.7.2 Additional Operators 292 6 SQL: Beyond the Query Language 329 6.1 Data Definition 329 Syntax: Create Table Statement 331 Syntax: Drop Table Statement 336 Syntax: Alter Table Statement 337 Special Topic: Create Index 338 Syntax: Create View Statement 339 6.2 Data Manipulation 342 Syntax: Insert Into Statement 343 Special Topic: Database Population 346 Syntax: Update Statement 347 Syntax: Delete Statement 349 6.3 Database User Privileges 352 Syntax: Grant Statement 354 Syntax: Revoke Statement 355 7 Database Programming 371 7.1 Persistent Stored Modules 372 Syntax: Create Procedure Statement 374 Syntax: Create Function Statement 376 7.2 Overview of Call-Level Interface 382 7.3 Java and JDBC 385 7.4 Python and DB-API 393 8 XML and Databases 431 8.1 Overview of XML 432 8.2 DTD 439 Syntax: DTD Overview 440 8.3 XML Schema 448 Syntax: XSD Overview of Element and Attribute Declarations 450 Syntax: XSD Attribute Declarations: use, default, fixed 460 8.4 Structuring XML for Data Exchange 467 9 Transaction Management 491 9.1 ACID Properties of a Transaction 492 9.2 Recovery control 498 How To: Recovery Control: UNDO and REDO 501 9.3 Concurrency control 504 9.3.1 Serializability 507 How To: Create a Precedence Graph 508 9.3.2 Locking 512 9.3.3 Timestamps 520 Algorithm: Basic Timestamp Protocol 521 10 More on Database Design 543 10.1 Database Design Goals 544 10.2 Functional Dependencies 546 Algorithm: Attribute Closure 550 Special Topic: Minimal Set of Functional Dependencies 552 How To: Heuristic Determination of a Candidate Key 552 10.3 Decomposition 558 How To: Determine Breakdown of F for a Decomposition 559 How To: Determine Lossless Pairwise Decomposition 562 Algorithm: Lossless-Join Property for Database Schema 565 10.4 Normal Forms 571 How To: Determine the Normal Form of a Relation 574 Algorithm: BCNF Decomposition Algorithm 575 A WinRDBI 599 A.1 Overview 599 A.2 Query Languages 600 A.3 Implementation Overview 606 A.4 Summary 606 Index 607
£95.29
John Wiley & Sons Inc The Big RBook
Book SynopsisIntroduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuseTable of ContentsForeword xxv About the Author xxvii Acknowledgements xxix Preface xxxi About the Companion Site xxxv I Introduction 1 1 The Big Picture with Kondratiev and Kardashev 3 2 The Scientific Method and Data 7 3 Conventions 11 II Starting with R and Elements of Statistics 19 4 The Basics of R 21 4.1 Getting Started with R 23 4.2 Variables 26 4.3 Data Types 28 4.3.1 The Elementary Types 28 4.3.2 Vectors 29 4.3.3 Accessing Data from a Vector 29 4.3.4 Matrices 32 4.3.5 Arrays 38 4.3.6 Lists 41 4.3.7 Factors 45 4.3.8 Data Frames 49 4.3.9 Strings or the Character-type 54 4.4 Operators 57 4.4.1 Arithmetic Operators 57 4.4.2 Relational Operators 57 4.4.3 Logical Operators 58 4.4.4 Assignment Operators 59 4.4.5 Other Operators 61 4.5 Flow Control Statements 63 4.5.1 Choices 63 4.5.2 Loops 65 4.6 Functions 69 4.6.1 Built-in Functions 69 4.6.2 Help with Functions 69 4.6.3 User-defined Functions 70 4.6.4 Changing Functions 70 4.6.5 Creating Function with Default Arguments 71 4.7 Packages 72 4.7.1 Discovering Packages in R 72 4.7.2 Managing Packages in R 73 4.8 Selected Data Interfaces 75 4.8.1 CSV Files 75 4.8.2 Excel Files 79 4.8.3 Databases 79 5 Lexical Scoping and Environments 81 5.1 Environments in R 81 5.2 Lexical Scoping in R 83 6 The Implementation of OO 87 6.1 Base Types 89 6.2 S3 Objects 91 6.2.1 Creating S3 Objects 94 6.2.2 Creating Generic Methods 96 6.2.3 Method Dispatch 97 6.2.4 Group Generic Functions 98 6.3 S4 Objects 100 6.3.1 Creating S4 Objects 100 6.3.2 Using S4 Objects 101 6.3.3 Validation of Input 105 6.3.4 Constructor functions 107 6.3.5 The Data slot 108 6.3.6 Recognising Objects, Generic Functions, and Methods 108 6.3.7 CreatingS4Generics 110 6.3.8 Method Dispatch 111 6.4 The Reference Class, refclass, RC or R5 Model 113 6.4.1 Creating RC Objects 113 6.4.2 Important Methods and Attributes 117 6.5 Conclusions about the OO Implementation 119 7 Tidy R with the Tidyverse 121 7.1 The Philosophy of the Tidyverse 121 7.2 Packages in the Tidyverse 124 7.2.1 The Core Tidyverse 124 7.2.2 The Non-core Tidyverse 125 7.3 Working with the Tidyverse 127 7.3.1 Tibbles 127 7.3.2 Piping with R 132 7.3.3 Attention Points When Using the Pipe 133 7.3.4 Advanced Piping 134 7.3.5 Conclusion 137 8 Elements of Descriptive Statistics 139 8.1 Measures of Central Tendency 139 8.1.1 Mean 139 8.1.2 The Median 142 8.1.3 The Mode 143 8.2 Measures of Variation or Spread 145 8.3 Measures of Covariation 147 8.3.1 The Pearson Correlation 147 8.3.2 The Spearman Correlation 148 8.3.3 Chi-square Tests 149 8.4 Distributions 150 8.4.1 Normal Distribution 150 8.4.2 Binomial Distribution 153 8.5 Creating an Overview of Data Characteristics 155 9 Visualisation Methods 159 9.1 Scatterplots 161 9.2 Line Graphs 163 9.3 Pie Charts 165 9.4 Bar Charts 167 9.5 Boxplots 171 9.6 Violin Plots 173 9.7 Histograms 176 9.8 Plotting Functions 179 9.9 Maps and Contour Plots 180 9.10 Heat-maps 181 9.11 Text Mining 184 9.11.1 Word Clouds 184 9.11.2 Word Associations 188 9.12 Colours in R 191 10 Time Series Analysis 197 10.1 Time Series in R 197 10.1.1 The Basics of Time Series in R 197 10.2 Forecasting 200 10.2.1 Moving Average 200 10.2.2 Seasonal Decomposition 206 11 Further Reading 211 III Data Import 213 12 A Short History of Modern Database Systems 215 13 RDBMS 219 14 SQL 223 14.1 Designing the Database 223 14.2 Building the Database Structure 226 14.2.1 Installing a RDBMS 226 14.2.2 Creating the Database 228 14.2.3 Creating the Tables and Relations 229 14.3 Adding Data to the Database 235 14.4 Querying the Database 239 14.4.1 The Basic Select Query 239 14.4.2 More Complex Queries 240 14.5 Modifying the Database Structure 244 14.6 Selected Features of SQL 249 14.6.1 Changing Data 249 14.6.2 Functions in SQL 249 15 Connecting R to an SQL Database 253 IV Data Wrangling 257 16 Anonymous Data 261 17 Data Wrangling in the tidyverse 265 17.1 Importing the Data 266 17.1.1 Importing from an SQLRDBMS 266 17.1.2 Importing Flat Files in the Tidyverse 267 17.2 Tidy Data 275 17.3 Tidying Up Data with tidyr 277 17.3.1 Splitting Tables 278 17.3.2 Convert Headers to Data 281 17.3.3 Spreading One Column Over Many 284 17.3.4 Split One Columns into Many 285 17.3.5 Merge Multiple Columns Into One 286 17.3.6 Wrong Data 287 17.4 SQL-like Functionality via dplyr 288 17.4.1 Selecting Columns 288 17.4.2 Filtering Rows 289 17.4.3 Joining 290 17.4.4 Mutating Data 293 17.4.5 Set Operations 296 17.5 String Manipulation in the tidyverse 299 17.5.1 Basic String Manipulation 300 17.5.2 Pattern Matching with Regular Expressions 302 17.6 Dates with lubridate 314 17.6.1 ISO 8601 Format 315 17.6.2 Time-zones 317 17.6.3 Extract Date and Time Components 318 17.6.4 Calculating with Date-times 319 17.7 Factors with Forcats 325 18 Dealing with Missing Data 333 18.1 Reasons for Data to be Missing 334 18.2 Methods to Handle Missing Data 336 18.2.1 Alternative Solutions to Missing Data 336 18.2.2 Predictive Mean Matching(PMM) 338 18.3 R Packages to Deal with Missing Data 339 18.3.1 mice 339 18.3.2 missForest 340 18.3.3 Hmisc 341 19 Data Binning 343 19.1 What is Binning and Why Use It 343 19.2 Tuning the Binning Procedure 347 19.3 More Complex Cases: Matrix Binning 352 19.4 Weight of Evidence and Information Value 359 19.4.1 Weight of Evidence(WOE) 359 19.4.2 Information Value(IV) 359 19.4.3 WOE and IV in R 359 20 Factoring Analysis and Principle Components 363 20.1 Principle Components Analysis (PCA) 364 20.2 Factor Analysis 368 V Modelling 373 21 Regression Models 375 21.1 Linear Regression 375 21.2 Multiple Linear Regression 379 21.2.1 Poisson Regression 379 21.2.2 Non-linear Regression 381 21.3 Performance of Regression Models 384 21.3.1 Mean Square Error (MSE) 384 21.3.2 R-Squared 384 21.3.3 Mean Average Deviation(MAD) 386 22 Classification Models 387 22.1 Logistic Regression 388 22.2 Performance of Binary Classification Models 390 22.2.1 The Confusion Matrix and Related Measures 391 22.2.2 ROC 393 22.2.3 The AUC 396 22.2.4 The Gini Coefficient 397 22.2.5 Kolmogorov-Smirnov (KS) for Logistic Regression 398 22.2.6 Finding an Optimal Cut-off 399 23 Learning Machines 405 23.1 Decision Tree 407 23.1.1 Essential Background 407 23.1.2 Important Considerations 412 23.1.3 Growing Trees with the Package rpart 414 23.1.4 Evaluating the Performance of a Decision Tree 424 23.2 Random Forest 428 23.3 Artificial Neural Networks (ANNs) 434 23.3.1 The Basics of ANNs in R 434 23.3.2 Neural Networks in R 436 23.3.3 The Work-flow to for Fitting a NN 438 23.3.4 Cross Validate the NN 444 23.4 Support Vector Machine 447 23.4.1 Fitting a SVM in R 447 23.4.2 Optimizing the SVM 449 23.5 Unsupervised Learning and Clustering 450 23.5.1 k-Means Clustering 450 23.5.2 Visualizing Clusters in Three Dimensions 462 23.5.3 Fuzzy Clustering 464 23.5.4 Hierarchical Clustering 466 23.5.5 Other Clustering Methods 468 24 Towards a Tidy Modelling Cycle with modelr 469 24.1 Adding Predictions 470 24.2 Adding Residuals 471 24.3 Bootstrapping Data 472 24.4 Other Functions of modelr 474 25 Model Validation 475 25.1 Model Quality Measures 476 25.2 Predictions and Residuals 477 25.3 Bootstrapping 479 25.3.1 Bootstrapping in Base R 479 25.3.2 Bootstrapping in the tidyverse with modelr 481 25.4 Cross-Validation 483 25.4.1 Elementary Cross Validation 483 25.4.2 Monte Carlo Cross Validation 486 25.4.3 k-Fold Cross Validation 488 25.4.4 Comparing Cross Validation Methods 489 25.5 Validation in a Broader Perspective 492 26 Labs 495 26.1 Financial Analysis with quantmod 495 26.1.1 The Basics of quantmod 495 26.1.2 Types of Data Available in quantmod 496 26.1.3 Plotting with quantmod 497 26.1.4 The quantmod Data Structure 500 26.1.5 Support Functions Supplied by quantmod 502 26.1.6 Financial Modelling in quantmod 504 27 Multi Criteria Decision Analysis (MCDA) 511 27.1 What and Why 511 27.2 General Work-flow 513 27.3 Identify the Issue at Hand: Steps 1 and 2 516 27.4 Step3: the Decision Matrix 518 27.4.1 Construct a Decision Matrix 518 27.4.2 Normalize the Decision Matrix 520 27.5 Step 4: Delete Inefficient and Unacceptable Alternatives 521 27.5.1 Unacceptable Alternatives 521 27.5.2 Dominance – Inefficient Alternatives 521 27.6 Plotting Preference Relationships 524 27.7 Step5: MCDA Methods 526 27.7.1 Examples of Non-compensatory Methods 526 27.7.2 The Weighted Sum Method(WSM) 527 27.7.3 Weighted Product Method(WPM) 530 27.7.4 ELECTRE 530 27.7.5 PROMethEE 540 27.7.6 PCA(Gaia) 553 27.7.7 Outranking Methods 557 27.7.8 Goal Programming 558 27.8 Summary MCDA 561 VI Introduction to Companies 563 28 Financial Accounting (FA) 567 28.1 The Statements of Accounts 568 28.1.1 Income Statement 568 28.1.2 Net Income: The P&L statement 568 28.1.3 Balance Sheet 569 28.2 The Value Chain 571 28.3 Further, Terminology 573 28.4 Selected Financial Ratios 575 29 Management Accounting 583 29.1 Introduction 583 29.1.1 Definition of Management Accounting (MA) 583 29.1.2 Management Information Systems (MIS) 584 29.2 Selected Methods in MA 585 29.2.1 Cost Accounting 585 29.2.2 Selected Cost Types 587 29.3 Selected Use Cases of MA 590 29.3.1 Balanced Scorecard 590 29.3.2 Key Performance Indicators (KPIs) 591 30 Asset Valuation Basics 597 30.1 Time Value of Money 598 30.1.1 Interest Basics 598 30.1.2 Specific Interest Rate Concepts 598 30.1.3 Discounting 600 30.2 Cash 601 30.3 Bonds 602 30.3.1 Features of a Bond 602 30.3.2 Valuation of Bonds 604 30.3.3 Duration 606 30.4 The Capital Asset Pricing Model (CAPM) 610 30.4.1 The CAPM Framework 610 30.4.2 The CAPM and Risk 612 30.4.3 Limitations and Shortcomings of the CAPM 612 30.5 Equities 614 30.5.1 Definition 614 30.5.2 Short History 614 30.5.3 Valuation of Equities 615 30.5.4 Absolute Value Models 616 30.5.5 Relative Value Models 625 30.5.6 Selection of Valuation Methods 630 30.5.7 Pitfalls in Company Valuation 631 30.6 Forwards and Futures 638 30.7 Options 640 30.7.1 Definitions 640 30.7.2 Commercial Aspects 642 30.7.3 Short History 643 30.7.4 Valuation of Options at Maturity 644 30.7.5 The Black and Scholes Model 649 30.7.6 The Binomial Model 654 30.7.7 Dependencies of the Option Price 660 30.7.8 The Greeks 664 30.7.9 Delta Hedging 665 30.7.10 Linear Option Strategies 667 30.7.11 Integrated Option Strategies 674 30.7.12 Exotic Options 678 30.7.13 Capital Protected Structures 680 VII Reporting 683 31 A Grammar of Graphics with ggplot2 687 31.1 TheBasicsofggplot2 688 31.2 Over-plotting 692 31.3 CaseStudyforggplot2 696 32 R Markdown 699 33 knitr and LATEX 703 34 An Automated Development Cycle 707 35 Writing and Communication Skills 709 36 Interactive Apps 713 36.1 Shiny 715 36.2 Browser Born Data Visualization 719 36.2.1 HTML-widgets 719 36.2.2 Interactive Maps with leaflet 720 36.2.3 Interactive Data Visualisation with ggvis 721 36.2.4 googleVis 723 36.3 Dashboards 725 36.3.1 The Business Case: a Diversity Dashboard 726 36.3.2 A Dashboard with flexdashboard 731 36.3.3 A Dashboard with shinydashboard 737 VIII Bigger and Faster R 741 37 Parallel Computing 743 37.1 Combine foreach and doParallel 745 37.2 Distribute Calculations over LAN with Snow 748 37.3 Using the GPU 752 37.3.1 Getting Started with gpuR 754 37.3.2 On the Importance of Memory use 757 37.3.3 Conclusions for GPU Programming 759 38 R and Big Data 761 38.1 Use a Powerful Server 763 38.1.1 Use R on a Server 763 38.1.2 Let the Database Server do the Heavy Lifting 763 38.2 Using more Memory than we have RAM 765 39 Parallelism for Big Data 767 39.1 Apache Hadoop 769 39.2 Apache Spark 771 39.2.1 Installing Spark 771 39.2.2 Running Spark 773 39.2.3 SparkR 776 39.2.4 sparklyr 788 39.2.5 SparkR or sparklyr 791 40 The Need for Speed 793 40.1 Benchmarking 794 40.2 Optimize Code 797 40.2.1 Avoid Repeating the Same 797 40.2.2 Use Vectorisation where Appropriate 797 40.2.3 Pre-allocating Memory 799 40.2.4 Use the Fastest Function 800 40.2.5 Use the Fastest Package 801 40.2.6 Be Mindful about Details 802 40.2.7 Compile Functions 804 40.2.8 Use C or C++ Code in R 806 40.2.9 Using a C++ Source File in R 809 40.2.10CallCompiledC++Functions in R 811 40.3 Profiling Code 812 40.3.1 The Package profr 813 40.3.2 The Package proftools 813 40.4 Optimize Your Computer 817 IX Appendices 819 A Create your own R Package 821 A.1 Creating the Package in the R Console 823 A.2 Update the Package Description 825 A.3 Documenting the Functionsxs 826 A.4 Loading the Package 827 A.5 Further Steps 828 B Levels of Measurement 829 B.1 Nominal Scale 829 B.2 Ordinal Scale 830 B.3 Interval Scale 831 B.4 Ratio Scale 832 C Trademark Notices 833 C.1 General Trademark Notices 834 C.2 R-Related Notices 835 C.2.1 Crediting Developers of R Packages 835 C.2.2 The R-packages used in this Book 835 D Code Not Shown in the Body of the Book 839 E Answers to Selected Questions 845 Bibliography 859 Nomenclature 869 Index 881
£93.56
John Wiley & Sons Inc Cognitive Intelligence and Big Data in Healthcare
Book SynopsisCOGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention. As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications. This book tackles all these issues and provides insight into these diversifieTable of ContentsPreface xv 1 Era of Computational Cognitive Techniques in Healthcare Systems 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta 1.1 Introduction 2 1.2 Cognitive Science 3 1.3 Gap Between Classical Theory of Cognition 4 1.4 Cognitive Computing’s Evolution 6 1.5 The Coming Era of Cognitive Computing 7 1.6 Cognitive Computing Architecture 9 1.6.1 The Internet-of-Things and Cognitive Computing 10 1.6.2 Big Data and Cognitive Computing 11 1.6.3 Cognitive Computing and Cloud Computing 13 1.7 Enabling Technologies in Cognitive Computing 13 1.7.1 Reinforcement Learning and Cognitive Computing 13 1.7.2 Cognitive Computing with Deep Learning 15 1.7.2.1 Relational Technique and Perceptual Technique 15 1.7.2.2 Cognitive Computing and Image Understanding 16 1.8 Intelligent Systems in Healthcare 17 1.8.1 Intelligent Cognitive System in Healthcare (Why and How) 20 1.9 The Cognitive Challenge 32 1.9.1 Case Study: Patient Evacuation 32 1.9.2 Case Study: Anesthesiology 32 1.10 Conclusion 34 References 35 2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano 2.1 Introduction 42 2.2 Literature Concept 44 2.2.1 Cognitive Computing Concept 44 2.2.2 Neural Networks Concepts 47 2.2.3 Convolutional Neural Network 49 2.2.4 Deep Learning 52 2.3 Materials and Methods (Metaheuristic Algorithm Proposal) 55 2.4 Case Study and Discussion 57 2.5 Conclusions with Future Research Scopes 60 References 61 3 Convergence of Big Data and Cognitive Computing in Healthcare 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran 3.1 Introduction 68 3.2 Literature Review 70 3.2.1 Role of Cognitive Computing in Healthcare Applications 70 3.2.2 Research Problem Study by IBM 73 3.2.3 Purpose of Big Data in Healthcare 74 3.2.4 Convergence of Big Data with Cognitive Computing 74 3.2.4.1 Smart Healthcare 74 3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 75 3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 76 3.3.1 EEG Pathology Diagnoses 76 3.3.2 Cognitive–Big Data-Based Smart Healthcare 77 3.3.3 System Architecture 79 3.3.4 Detection and Classification of Pathology 80 3.3.4.1 EEG Preprocessing and Illustration 80 3.3.4.2 CNN Model 80 3.3.5 Case Study 81 3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 83 3.4.1 Cloud Computing with Big Data in Healthcare 86 3.4.2 Heart Diseases 87 3.4.3 Healthcare Big Data Techniques 88 3.4.3.1 Rule Set Classifiers 88 3.4.3.2 Neuro Fuzzy Classifiers 89 3.4.3.3 Experimental Results 91 3.5 Conclusion 92 References 93 4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh 4.1 Introduction 98 4.2 The Role of Technology in an Aging Society 99 4.3 Literature Survey 100 4.4 Health Monitoring 101 4.5 Nutrition Monitoring 105 4.6 Stress-Log: An IoT-Based Smart Monitoring System 106 4.7 Active Aging 108 4.8 Localization 108 4.9 Navigation Care 111 4.10 Fall Monitoring 113 4.10.1 Fall Detection System Architecture 114 4.10.2 Wearable Device 114 4.10.3 Wireless Communication Network 114 4.10.4 Smart IoT Gateway 115 4.10.5 Interoperability 115 4.10.6 Transformation of Data 115 4.10.7 Analyzer for Big Data 115 4.11 Conclusion 115 References 116 5 Influence of Cognitive Computing in Healthcare Applications 121Lucia Agnes Beena T. and Vinolyn Vijaykumar 5.1 Introduction 122 5.2 Bond Between Big Data and Cognitive Computing 124 5.3 Need for Cognitive Computing in Healthcare 126 5.4 Conceptual Model Linking Big Data and Cognitive Computing 128 5.4.1 Significance of Big Data 128 5.4.2 The Need for Cognitive Computing 129 5.4.3 The Association Between the Big Data and Cognitive Computing 130 5.4.4 The Advent of Cognition in Healthcare 132 5.5 IBM’s Watson and Cognitive Computing 133 5.5.1 Industrial Revolution with Watson 134 5.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 135 5.6 Future Directions 137 5.6.1 Retail 138 5.6.2 Research 139 5.6.3 Travel 139 5.6.4 Security and Threat Detection 139 5.6.5 Cognitive Training Tools 140 5.7 Conclusion 141 References 141 6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano 6.1 Introduction 146 6.2 Literature Concept 148 6.2.1 Cognitive Computing Concept 148 6.2.1.1 Application Potential 151 6.2.2 Cognitive Computing in Healthcare 153 6.2.3 Deep Learning in Healthcare 157 6.2.4 Natural Language Processing in Healthcare 160 6.3 Discussion 162 6.4 Trends 163 6.5 Conclusions 164 References 165 7 Protecting Patient Data with 2F- Authentication 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa 7.1 Introduction 170 7.2 Literature Survey 175 7.3 Two-Factor Authentication 177 7.3.1 Novel Features of Two-Factor Authentication 178 7.3.2 Two-Factor Authentication Sorgen 178 7.3.3 Two-Factor Security Libraries 179 7.3.4 Challenges for Fitness Concern 180 7.4 Proposed Methodology 181 7.5 Medical Treatment and the Preservation of Records 186 7.5.1 Remote Method of Control 187 7.5.2 Enabling Healthcare System Technology 187 7.6 Conclusion 189 References 190 8 Data Analytics for Healthcare Monitoring and Inferencing 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi 8.1 An Overview of Healthcare Systems 198 8.2 Need of Healthcare Systems 198 8.3 Basic Principle of Healthcare Systems 199 8.4 Design and Recommended Structure of Healthcare Systems 199 8.4.1 Healthcare System Designs on the Basis of these Parameters 200 8.4.2 Details of Healthcare Organizational Structure 201 8.5 Various Challenges in Conventional Existing Healthcare System 202 8.6 Health Informatics 202 8.7 Information Technology Use in Healthcare Systems 203 8.8 Details of Various Information Technology Application Use in Healthcare Systems 203 8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 204 8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 205 8.11 Healthcare Data Analytics 206 8.12 Healthcare as a Concept 206 8.13 Healthcare’s Key Technologies 207 8.14 The Present State of Smart Healthcare Application 207 8.15 Data Analytics with Machine Learning Use in Healthcare Systems 208 8.16 Benefit of Data Analytics in Healthcare System 210 8.17 Data Analysis and Visualization: COVID-19 Case Study in India 210 8.18 Bioinformatics Data Analytics 222 8.18.1 Notion of Bioinformatics 222 8.18.2 Bioinformatics Data Challenges 222 8.18.3 Sequence Analysis 222 8.18.4 Applications 223 8.18.5 COVID-19: A Bioinformatics Approach 224 8.19 Conclusion 224 References 225 9 Features Optimistic Approach for the Detection of Parkinson’s Disease 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha 9.1 Introduction 230 9.1.1 Parkinson’s Disease 230 9.1.2 Spect Scan 231 9.2 Literature Survey 232 9.3 Methods and Materials 233 9.3.1 Database Details 233 9.3.2 Procedure 234 9.3.3 Pre-Processing Done by PPMI 235 9.3.4 Image Analysis and Features Extraction 235 9.3.4.1 Image Slicing 235 9.3.4.2 Intensity Normalization 237 9.3.4.3 Image Segmentation 239 9.3.4.4 Shape Features Extraction 240 9.3.4.5 SBR Features 241 9.3.4.6 Feature Set Analysis 242 9.3.4.7 Surface Fitting 242 9.3.5 Classification Modeling 243 9.3.6 Feature Importance Estimation 246 9.3.6.1 Need for Analysis of Important Features 246 9.3.6.2 Random Forest 247 9.4 Results and Discussion 248 9.4.1 Segmentation 248 9.4.2 Shape Analysis 249 9.4.3 Classification 249 9.5 Conclusion 252 References 253 10 Big Data Analytics in Healthcare 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta 10.1 Introduction 258 10.2 Need for Big Data Analytics 260 10.3 Characteristics of Big Data 264 10.3.1 Volume 264 10.3.2 Velocity 265 10.3.3 Variety 265 10.3.4 Veracity 265 10.3.5 Value 265 10.3.6 Validity 265 10.3.7 Variability 266 10.3.8 Viscosity 266 10.3.9 Virality 266 10.3.10 Visualization 266 10.4 Big Data Analysis in Disease Treatment and Management 267 10.4.1 For Diabetes 267 10.4.2 For Heart Disease 268 10.4.3 For Chronic Disease 270 10.4.4 For Neurological Disease 271 10.4.5 For Personalized Medicine 271 10.5 Big Data: Databases and Platforms in Healthcare 279 10.6 Importance of Big Data in Healthcare 285 10.6.1 Evidence-Based Care 285 10.6.2 Reduced Cost of Healthcare 285 10.6.3 Increases the Participation of Patients in the Care Process 285 10.6.4 The Implication in Health Surveillance 285 10.6.5 Reduces Mortality Rate 285 10.6.6 Increase of Communication Between Patients and Healthcare Providers 286 10.6.7 Early Detection of Fraud and Security Threats in Health Management 286 10.6.8 Improvement in the Care Quality 286 10.7 Application of Big Data Analytics 286 10.7.1 Image Processing 286 10.7.2 Signal Processing 287 10.7.3 Genomics 288 10.7.4 Bioinformatics Applications 289 10.7.5 Clinical Informatics Application 291 10.8 Conclusion 293 References 294 11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303V. Sathananthavathi and G. Indumathi 11.1 Introduction 304 11.1.1 Glaucoma 304 11.2 Literature Survey 306 11.3 Methodology 309 11.3.1 Sclera Segmentation 310 11.3.1.1 Fully Convolutional Network 311 11.3.2 Pupil/Iris Ratio 313 11.3.2.1 Canny Edge Detection 314 11.3.2.2 Mean Redness Level (MRL) 315 11.3.2.3 Red Area Percentage (RAP) 316 11.4 Results and Discussion 317 11.4.1 Feature Extraction from Frontal Eye Images 318 11.4.1.1 Level of Mean Redness (MRL) 318 11.4.1.2 Percentage of Red Area (RAP) 318 11.4.2 Images of the Frontal Eye Pupil/Iris Ratio 318 11.4.2.1 Histogram Equalization 319 11.4.2.2 Morphological Reconstruction 319 11.4.2.3 Canny Edge Detection 319 11.4.2.4 Adaptive Thresholding 320 11.4.2.5 Circular Hough Transform 321 11.4.2.6 Classification 322 11.5 Conclusion and Future Work 324 References 325 12 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh 12.1 Introduction 328 12.2 Introduction to Big Data and Data Mining 328 12.3 Role of Sentimental Analysis in the Healthcare Sector 330 12.4 Case Study: Analyzing Mental Health 332 12.4.1 Problem Statement 332 12.4.2 Research Objectives 333 12.4.3 Methodology and Framework 333 12.4.3.1 Big 5 Personality Model 333 12.4.3.2 Openness to Explore 334 12.4.3.3 Methodology 335 12.4.3.4 Detailed Design Methodologies 340 12.4.3.5 Work Done Details as Required 341 12.5 Results and Discussion 343 12.6 Conclusion and Future 345 References 346 13 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav 13.1 Introduction 350 13.2 Artificial Intelligence and Management of Chronic Diseases 351 13.3 Blockchain and Healthcare 354 13.3.1 Blockchain and Healthcare Management of Chronic Disease 355 13.4 Internet-of-Things and Healthcare Management of Chronic Disease 358 13.5 Conclusions 360 References 360 14 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367BKSP Kumar Raju Alluri 14.1 Introduction 367 14.2 Cognitive Computing Framework in Healthcare 371 14.3 Benefits of Using Cognitive Computing for Healthcare 372 14.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 374 14.4.1 Using Cognitive Services for a Patient’s Healthcare Management 375 14.4.2 Using Cognitive Services for Healthcare Providers 376 14.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 377 14.6 Future Directions for Extending Heathcare Services Using CATs 380 14.7 Addressing CAT Challenges in Healthcare as a General Framework 384 14.8 Conclusion 384 References 385 Index 391
£133.20
John Wiley & Sons Inc Data Science For Dummies
Book SynopsisMonetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you'll ever need to lead profitable data science projectsSecret, reverse-engineered data monetization tactics that no one's talking aboutThe shocking truth about how simple natural language processing can beHow to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you're new to the data science field or already a decade in, you're sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company's data by picking up your copy today.Table of ContentsIntroduction 1 Part 1: Getting Started with Data Science 5 Chapter 1: Wrapping Your Head Around Data Science 7 Chapter 2: Tapping into Critical Aspects of Data Engineering 19 Part 2: Using Data Science to Extract Meaning from Your Data 37 Chapter 3: Machine Learning Means Using a Machine to Learn from Data 39 Chapter 4: Math, Probability, and Statistical Modeling 51 Chapter 5: Grouping Your Way into Accurate Predictions 77 Chapter 6: Coding Up Data Insights and Decision Engines 103 Chapter 7: Generating Insights with Software Applications 137 Chapter 8: Telling Powerful Stories with Data 161 Part 3: Taking Stock of Your Data Science Capabilities 187 Chapter 9: Developing Your Business Acumen 189 Chapter 10: Improving Operations 205 Chapter 11: Making Marketing Improvements 229 Chapter 12: Enabling Improved Decision-Making 245 Chapter 13: Decreasing Lending Risk and Fighting Financial Crimes 265 Chapter 14: Monetizing Data and Data Science Expertise 275 Part 4: Assessing Your Data Science Options 289 Chapter 15: Gathering Important Information about Your Company 291 Chapter 16: Narrowing In on the Optimal Data Science Use Case 311 Chapter 17: Planning for Future Data Science Project Success 327 Chapter 18: Blazing a Path to Data Science Career Success 341 Part 5: The Part of Tens 367 Chapter 19: Ten Phenomenal Resources for Open Data 369 Chapter 20: Ten Free or Low-Cost Data Science Tools and Applications 381 Index 397
£24.64
John Wiley & Sons Inc CompTIA Data Study Guide
Book SynopsisTable of ContentsIntroduction xv Assessment Test xxii Chapter 1 Today’s Data Analyst 1 Welcome to the World of Analytics 2 Data 2 Storage 3 Computing Power 4 Careers in Analytics 5 The Analytics Process 6 Data Acquisition 7 Cleaning and Manipulation 7 Analysis 8 Visualization 8 Reporting and Communication 8 Analytics Techniques 10 Descriptive Analytics 10 Predictive Analytics 11 Prescriptive Analytics 11 Machine Learning, Artificial Intelligence, and Deep Learning 11 Data Governance 13 Analytics Tools 13 Summary 15 Chapter 2 Understanding Data 17 Exploring Data Types 18 Structured Data Types 20 Unstructured Data Types 31 Categories of Data 36 Common Data Structures 39 Structured Data 39 Unstructured Data 41 Semi-structured Data 42 Common File Formats 42 Text Files 42 JavaScript Object Notation 44 Extensible Markup Language (XML) 45 HyperText Markup Language (HTML) 47 Summary 48 Exam Essentials 49 Review Questions 51 Chapter 3 Databases and Data Acquisition 57 Exploring Databases 58 The Relational Model 59 Relational Databases 62 Nonrelational Databases 68 Database Use Cases 71 Online Transactional Processing 71 Online Analytical Processing 74 Schema Concepts 75 Data Acquisition Concepts 81 Integration 81 Data Collection Methods 83 Working with Data 88 Data Manipulation 89 Query Optimization 96 Summary 99 Exam Essentials 100 Review Questions 101 Chapter 4 Data Quality 105 Data Quality Challenges 106 Duplicate Data 106 Redundant Data 107 Missing Values 110 Invalid Data 111 Nonparametric data 112 Data Outliers 113 Specification Mismatch 114 Data Type Validation 114 Data Manipulation Techniques 116 Recoding Data 116 Derived Variables 117 Data Merge 118 Data Blending 119 Concatenation 121 Data Append 121 Imputation 122 Reduction 124 Aggregation 126 Transposition 127 Normalization 128 Parsing/String Manipulation 130 Managing Data Quality 132 Circumstances to Check for Quality 132 Automated Validation 136 Data Quality Dimensions 136 Data Quality Rules and Metrics 140 Methods to Validate Quality 142 Summary 144 Exam Essentials 145 Review Questions 146 Chapter 5 Data Analysis and Statistics 151 Fundamentals of Statistics 152 Descriptive Statistics 155 Measures of Frequency 155 Measures of Central Tendency 160 Measures of Dispersion 164 Measures of Position 173 Inferential Statistics 175 Confidence Intervals 175 Hypothesis Testing 179 Simple Linear Regression 186 Analysis Techniques 190 Determine Type of Analysis 190 Types of Analysis 191 Exploratory Data Analysis 192 Summary 192 Exam Essentials 194 Review Questions 196 Chapter 6 Data Analytics Tools 201 Spreadsheets 202 Microsoft Excel 203 Programming Languages 205 R 205 Python 206 Structured Query Language (SQL) 208 Statistics Packages 209 IBM SPSS 210 SAS 211 Stata 211 Minitab 212 Machine Learning 212 IBM SPSS Modeler 213 RapidMiner 214 Analytics Suites 217 IBM Cognos 217 Power BI 218 MicroStrategy 219 Domo 220 Datorama 221 AWS QuickSight 222 Tableau 222 Qlik 224 BusinessObjects 225 Summary 225 Exam Essentials 225 Review Questions 227 Chapter 7 Data Visualization with Reports and Dashboards 231 Understanding Business Requirements 232 Understanding Report Design Elements 235 Report Cover Page 236 Executive Summary 237 Design Elements 239 Documentation Elements 244 Understanding Dashboard Development Methods 247 Consumer Types 247 Data Source Considerations 248 Data Type Considerations 249 Development Process 250 Delivery Considerations 250 Operational Considerations 252 Exploring Visualization Types 252 Charts 252 Maps 258 Waterfall 264 Infographic 266 Word Cloud 267 Comparing Report Types 268 Static and Dynamic 268 Ad Hoc 269 Self-Service (On-Demand) 269 Recurring Reports 269 Tactical and Research 270 Summary 271 Exam Essentials 272 Review Questions 274 Chapter 8 Data Governance 279 Data Governance Concepts 280 Data Governance Roles 281 Access Requirements 281 Security Requirements 286 Storage Environment Requirements 289 Use Requirements 291 Entity Relationship Requirements 292 Data Classification Requirements 292 Jurisdiction Requirements 297 Breach Reporting Requirements 298 Understanding Master Data Management 299 Processes 300 Circumstances 301 Summary 303 Exam Essentials 304 Review Questions 306 Appendix Answers to the Review Questions 311 Chapter 2: Understanding Data 312 Chapter 3: Databases and Data Acquisition 314 Chapter 4: Data Quality 315 Chapter 5: Data Analysis and Statistics 317 Chapter 6: Data Analytics Tools 319 Chapter 7: Data Visualization with Reports and Dashboards 322 Chapter 8: Data Governance 323 Index 327
£42.75
Taylor & Francis Ltd Mining Multimedia Documents
Book SynopsisThe information age has led to an explosion in the amount of information available to the individual and the means by which it is accessed, stored, viewed, and transferred. In particular, the growth of the internet has led to the creation of huge repositories of multimedia documents in a diverse range of scientific and professional fields, as well as the tools to extract useful knowledge from them.Mining Multimedia Documents is a must-read for researchers, practitioners, and students working at the intersection of data mining and multimedia applications. It investigates various techniques related to mining multimedia documents based on text, image, and video features. It provides an insight into the open research problems benefitting advanced undergraduates, graduate students, researchers, scientists and practitioners in the fields of medicine, biology, production, education, government, national security and economics.Table of ContentsMining Multimedia Documents: An Overview. Fuzzy Decision Trees for Text Document Clustering. Towards Modeling Semi-Automatic Data Warehouses: Guided by Social Interactions. Multi-Agent System for Text Mining. The transformation of User Requirements in UML Diagrams: An Overview. An Overview of Information Extraction using Textual Case-Based Reasoning. Opinions Classification. Documents Classification Based on Text and Image Features. Content-Based Image Retrieval (CBIR). Mining Knowledge in Medical Image Databases. Segmentation for Medical Image Mining. Biological Data Mining: Techniques and Applications. Video Text Extraction and Mining. Recent Advancement in Multimedia Content using Deep Learning.
£133.00
CRC Press Big Data Analytics
Book SynopsisThe proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.Table of ContentsChallenges in Big Data. Challenges in Big Data Analytics. Bigdata Reference Model. A Survey of Big Data Analytics Tools. Understanding Data Science Behind Business Analytics. Big Data Predictive Modelling and Analytics. Deep Learning for Engineering Big Data analytics. A Framework for Minimising Data Leakage from Non-Production Systems. Big Data acquisition, preparation and analysis using Apache Software Foundation Projects. Storing and Analysing Streaming Data; A Big Data Challenge. Bigdata Cluster Analysis: A Study of Existing Techniques and Future Directions. Nonlinear feature extraction for Big Data Analytics. Enhanced Feature Mining and Classifier Models to predict Customer Churn for an E-retailer. Large-Scale Entity Clustering on Knowledge Graphs for Topic Discovery and Exploration. Big Data Analytics for Connected Intelligence with the Internet of Things. Bigdata, Internet traffic, and Website value co-creation. From hype to reflective practice-The possibilities and challenges of big data analysis in humanities research
£137.75
CRC Press Risk Assessment and Decision Analysis with
Book SynopsisSince the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions.Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more IntrodTrade ReviewPraise for the first edition: "By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. … the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book."—Peter J.F. Lucas, Journal of Statistical Theory and Practice, Vol. 8, March 2014 "… very useful to practitioners, professors, students, and anyone interested in understanding the application of Bayesian networks to risk assessment and decision analysis. Having many years of experience in the area, I highly recommend the book."—William E. Vesely, International Journal of Performability Engineering, July 2013 "Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a non-mathematician, I’ve found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook."—Carl Smith, School of Agriculture and Food Sciences, The University of Queensland "… although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication … . As such they are not accessible to readers who are not already proficient in those subjects. This book is an exciting development because it addresses this problem. … it should be understandable by any numerate reader interested in risk assessment and decision making. The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks. However, the focus is on ensuring that readers can build practical Bayesian network models … readers are provided with a tool that performs the propagation, so they will be able to build their own models to solve real-world risk assessment problems."—From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award winner "Let's be honest, most risk assessment methodologies are guesses, and not very good ones at that. People collect statistics about what they can see and then assume it tells them something about what they can't. The problem is that people assume the world follows nice distributions embedded in the world's fabric and that we simply need a little data to get the parameters right. Fenton and Neil take readers on an excellent journey through a more modern and appropriate way to make sense of uncertainty by leveraging prior beliefs and emerging evidence. Along the way they provide a wakeup call for the classic statistical views of risk and eloquently show the biases, fallacies and misconceptions that exist in such a view, and how dangerous they are for those making decisions.The book is not condescending to those without a mathematical background and is not too simple for those who do. It sets a nice tone which focuses more on how readers should think about risk and uncertainty and then uses a wealth of practical examples to show them how Bayesian methods can deliver powerful insights.After reading this book, you should be in no doubt that not only is it possible to model risk from the perspective of understanding how it behaves, but also that is necessarily the only sensible way to do so if you want to do something useful with your model and make correct decisions from it.Anyone aspiring to work, or already working, in the field of risk is well advised to read this book and put it into practice."—Neil Cantle, Milliman "The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes. There are loads of vivid examples (for instance, one explaining the Monty Hall problem), and it doesn’t skim over any of the technical details …"—Angela Saini (MIT Knight Science Journalism Fellow 2012-2013) on her blog, December 2012 "As computational chip size and product development cycle time approach zero, survival in the software industry becomes predicated on three related capabilities: prediction, diagnosis, and causality. These are the competitive advantages in 21st century software design testing. Fenton and Neil not only make a compelling case for Bayesian inference, but they also meticulously and patiently guide software engineers previously untrained in probability theory toward competence in mathematics. We have been waiting for decades for the last critical component that will make Bayesian a household word in industry: the incredible combination of an accessible software tool and an accompanying and brilliantly written textbook. Now software testers have the math, the algorithms, the tool, and the book. We no longer have any excuses for not dramatically raising our technology game to meet that challenge of continuous testing. Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys."—Michael Corning, Microsoft Corporation "This is an awesome book on using Bayesian networks for risk assessment and decision analysis. What makes this book so great is both its content and style. Fenton and Neil explain how the Bayesian networks work and how they can be built and applied to solve various decision-making problems in different areas. Even more importantly, the authors very clearly demonstrate motivations and advantages for using Bayesian networks over other modelling techniques. The core ideas are illustrated by lots of examples—from toy models to real-world applications. In contrast with many other books, this one is very easy to follow and does not require a strong mathematical or statistical background. I highly recommend this book to all researchers, students and practitioners who would like to go beyond traditional statistics or automated data mining techniques and incorporate expert knowledge in their models."—Dr. Lukasz Radlinski, Szczecin University "It is the first book that takes the art and science of developing Bayesian network models for actual problems as seriously as the underlying mathematics. The reader will obtain a good understanding of the methods as they are introduced through well-motivated and intuitive examples and attractive case studies. The authors do this in such a way that readers with little previous exposure to probability theory and statistics will be able to grasp and appreciate the power of Bayesian networks. While this in itself is already a major achievement, the authors go far beyond this by providing very close and pragmatic links between model building and the required techniques. It, thus, shares insights that are mostly missing from other textbooks, making this book also of interest to advanced readers, lecturers and researchers in the area."—Prof.dr. Peter Lucas, Institute for Computing and Information Sciences, Radboud University Nijmegen, and Leiden Institute of Advanced Computer Science, Leiden University "This book gives a thorough account of Bayesian networks, one of the most widely used frameworks for reasoning with uncertainty, and their application in domains as diverse as system reliability modelling and legal reasoning. The book's central premise is that ‘essentially, all models are wrong, but some are useful’ (G.E.P. Box), and the book distinguishes itself by focusing on the art of building useful models for risk assessment and decision analysis rather than on delving into mathematical detail of the models that are built. The authors are renowned for their ability to put Bayesian network technology into practical use, and it is therefore no surprise that the book is filled to the brim with motivating and relevant examples. With the accompanying evaluation copy of the excellent AgenaRisk software, readers can easily play around with the examples and gain valuable insights of how the models behave ‘at work.’ I believe this book should be of interest to practitioners working with risk assessment and decision making and also as a valuable textbook in undergraduate courses on probability and risk."—Helge Langseth, Norwegian University of Science and Technology "Bayesian networks are revolutionizing the way experts assess and manage uncertainty. This is the first book to explain this powerful new tool to a non-specialist audience. It takes us on a compelling journey from the basics of probability to sophisticated networks of system design, finance and crime. This trip is greatly supported by free software, allowing readers to explore and develop Bayesian networks for themselves. The style is accessible and entertaining, without sacrificing conceptual or mathematical rigor. This book is a must-read for anyone wanting to learn about Bayesian networks; it provides the know-how and software so that we can all share this adventure into risk and uncertainty."—David Lagnado, Senior Lecturer in Cognitive and Decision Sciences, University College London "This is the book I have wanted to see for many years. Whilst we are entitled to see appropriate duty of care in any risk management scenario, ill-informed practice is in fact prevalent in industry and society. There is little real excuse for this as classical decision theory has a long established history, and it can now be operationalized in complex scenarios using the Bayesian network technology that is a core theme of this book. The problem has been that most books on Bayesian networks and decision theory focus in depth on the technical foundations, and provide little in the way of practical guidance on how to use the technology to support real-world risk assessment and decision making.In contrast, Norman Fenton and Martin Neil have provided a clearly written and highly readable book that is packed with informative and insightful examples. I had fun reading it, but there is also sufficient technical detail so that one can obtain a deep understanding of the subject from studying the book. It is a joy, and one that I keep dipping back into."—Paul Krause, Professor of Software Engineering, University of Surrey "Given the massive uncertainties managers now need to operate within, this book is both vital and timely. Fenton and Neil’s explanation of how to create practical models that simulate real-life strategic scenarios gives hard-pressed managers a new tool that they can use to understand potential impacts and opportunities. This book should be required reading for anyone involved in strategy, business planning, or significant decision-making."—Rob Wirszycz, Celaton Limited Praise for the first edition: "By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. … the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book."—Peter J.F. Lucas, Journal of Statistical Theory and Practice, Vol. 8, March 2014 "… very useful to practitioners, professors, students, and anyone interested in understanding the application of Bayesian networks to risk assessment and decision analysis. Having many years of experience in the area, I highly recommend the book."—William E. Vesely, International Journal of Performability Engineering, July 2013 "Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a non-mathematician, I’ve found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook."—Carl Smith, School of Agriculture and Food Sciences, The University of Queensland "… although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication … . As such they are not accessible to readers who are not already proficient in those subjects. This book is an exciting development because it addresses this problem. … it should be understandable by any numerate reader interested in risk assessment and decision making. The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks. However, the focus is on ensuring that readers can build practical Bayesian network models … readers are provided with a tool that performs the propagation, so they will be able to build their own models to solve real-world risk assessment problems."—From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award winner "Let's be honest, most risk assessment methodologies are guesses, and not very good ones at that. People collect statistics about what they can see and then assume it tells them something about what they can't. The problem is that people assume the world follows nice distributions embedded in the world's fabric and that we simply need a little data to get the parameters right. Fenton and Neil take readers on an excellent journey through a more modern and appropriate way to make sense of uncertainty by leveraging prior beliefs and emerging evidence. Along the way they provide a wakeup call for the classic statistical views of risk and eloquently show the biases, fallacies and misconceptions that exist in such a view, and how dangerous they are for those making decisions.The book is not condescending to those without a mathematical background and is not too simple for those who do. It sets a nice tone which focuses more on how readers should think about risk and uncertainty and then uses a wealth of practical examples to show them how Bayesian methods can deliver powerful insights.After reading this book, you should be in no doubt that not only is it possible to model risk from the perspective of understanding how it behaves, but also that is necessarily the only sensible way to do so if you want to do something useful with your model and make correct decisions from it.Anyone aspiring to work, or already working, in the field of risk is well advised to read this book and put it into practice."—Neil Cantle, Milliman "The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes. There are loads of vivid examples (for instance, one explaining the Monty Hall problem), and it doesn’t skim over any of the technical details …"—Angela Saini (MIT Knight Science Journalism Fellow 2012-2013) on her blog, December 2012 "As computational chip size and product development cycle time approach zero, survival in the software industry becomes predicated on three related capabilities: prediction, diagnosis, and causality. These are the competitive advantages in 21st century software design testing. Fenton and Neil not only make a compelling case for Bayesian inference, but they also meticulously and patiently guide software engineers previously untrained in probability theory toward competence in mathematics. We have been waiting for decades for the last critical component that will make Bayesian a household word in industry: the incredible combination of an accessible software tool and an accompanying and brilliantly written textbook. Now software testers have the math, the algorithms, the tool, and the book. We no longer have any excuses for not dramatically raising our technology game to meet that challenge of continuous testing. Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys."—Michael Corning, Microsoft Corporation "This is an awesome book on using Bayesian networks for risk assessment and decision analysis. What makes this book so great is both its content and style. Fenton and Neil explain how the Bayesian networks work and how they can be built and applied to solve various decision-making problems in different areas. Even more importantly, the authors very clearly demonstrate motivations and advantages for using Bayesian networks over other modelling techniques. The core ideas are illustrated by lots of examples—from toy models to real-world applications. In contrast with many other books, this one is very easy to follow and does not require a strong mathematical or statistical background. I highly recommend this book to all researchers, students and practitioners who would like to go beyond traditional statistics or automated data mining techniques and incorporate expert knowledge in their models."—Dr. Lukasz Radlinski, Szczecin University "It is the first book that takes the art and science of developing Bayesian network models for actual problems as seriously as the underlying mathematics. The reader will obtain a good understanding of the methods as they are introduced through well-motivated and intuitive examples and attractive case studies. The authors do this in such a way that readers with little previous exposure to probability theory and statistics will be able to grasp and appreciate the power of Bayesian networks. While this in itself is already a major achievement, the authors go far beyond this by providing very close and pragmatic links between model building and the required techniques. It, thus, shares insights that are mostly missing from other textbooks, making this book also of interest to advanced readers, lecturers and researchers in the area."—Prof.dr. Peter Lucas, Institute for Computing and Information Sciences, Radboud University Nijmegen, and Leiden Institute of Advanced Computer Science, Leiden University "This book gives a thorough account of Bayesian networks, one of the most widely used frameworks for reasoning with uncertainty, and their application in domains as diverse as system reliability modelling and legal reasoning. The book's central premise is that ‘essentially, all models are wrong, but some are useful’ (G.E.P. Box), and the book distinguishes itself by focusing on the art of building useful models for risk assessment and decision analysis rather than on delving into mathematical detail of the models that are built. The authors are renowned for their ability to put Bayesian network technology into practical use, and it is therefore no surprise that the book is filled to the brim with motivating and relevant examples. With the accompanying evaluation copy of the excellent AgenaRisk software, readers can easily play around with the examples and gain valuable insights of how the models behave ‘at work.’ I believe this book should be of interest to practitioners working with risk assessment and decision making and also as a valuable textbook in undergraduate courses on probability and risk."—Helge Langseth, Norwegian University of Science and Technology "Bayesian networks are revolutionizing the way experts assess and manage uncertainty. This is the first book to explain this powerful new tool to a non-specialist audience. It takes us on a compelling journey from the basics of probability to sophisticated networks of system design, finance and crime. This trip is greatly supported by free software, allowing readers to explore and develop Bayesian networks for themselves. The style is accessible and entertaining, without sacrificing conceptual or mathematical rigor. This book is a must-read for anyone wanting to learn about Bayesian networks; it provides the know-how and software so that we can all share this adventure into risk and uncertainty."—David Lagnado, Senior Lecturer in Cognitive and Decision Sciences, University College London "This is the book I have wanted to see for many years. Whilst we are entitled to see appropriate duty of care in any risk management scenario, ill-informed practice is in fact prevalent in industry and society. There is little real excuse for this as classical decision theory has a long established history, and it can now be operationalized in complex scenarios using the Bayesian network technology that is a core theme of this book. The problem has been that most books on Bayesian networks and decision theory focus in depth on the technical foundations, and provide little in the way of practical guidance on how to use the technology to support real-world risk assessment and decision making.In contrast, Norman Fenton and Martin Neil have provided a clearly written and highly readable book that is packed with informative and insightful examples. I had fun reading it, but there is also sufficient technical detail so that one can obtain a deep understanding of the subject from studying the book. It is a joy, and one that I keep dipping back into."—Paul Krause, Professor of Software Engineering, University of Surrey "Given the massive uncertainties managers now need to operate within, this book is both vital and timely. Fenton and Neil’s explanation of how to create practical models that simulate real-life strategic scenarios gives hard-pressed managers a new tool that they can use to understand potential impacts and opportunities. This book should be required reading for anyone involved in strategy, business planning, or significant decision-making."—Rob Wirszycz, Celaton Limited Table of ContentsThere Is More to Assessing Risk Than Statistics. The Need for Causal, Explanatory Models in Risk Assessment. Measuring Uncertainty: The Inevitability of Subjectivity. The Basics of Probability. Bayes’ Theorem and Conditional Probability. From Bayes’ Theorem to Bayesian Networks. Defining the Structure of Bayesian Networks. Building and Eliciting Node Probability Tables. Numeric Variables and Continuous Distribution Functions. Hypothesis Testing and Confidence Intervals. Modeling Operational Risk. Systems Reliability Modeling. Bayes and the Law. Learning Bayesian Networks. Decision making, Influence Diagrams and Value of information. Bayesian networks in forensics. Using Bayesian networks to debunk bad statistics. Bayesian networks for football prediction. Appendix A: The Basics of Counting. Appendix B: The Algebra of Node Probability Tables. Appendix C: Junction Tree Algorithm. Appendix D: Dynamic Discretization. Appendix E: Statistical Distributions.
£58.89
Chapman and Hall/CRC The Essentials of Data Science Knowledge
Book SynopsisThe Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years' experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R's capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
£52.24
Taylor & Francis Ltd Data Analytics Applications in Gaming and
Book SynopsisThe last decade has witnessed the rise of big data in game development as the increasing proliferation of Internet-enabled gaming devices has made it easier than ever before to collect large amounts of player-related data. At the same time, the emergence of new business models and the diversification of the player base have exposed a broader potential audience, which attaches great importance to being able to tailor game experiences to a wide range of preferences and skill levels. This, in turn, has led to a growing interest in data mining techniques, as they offer new opportunities for deriving actionable insights to inform game design, to ensure customer satisfaction, to maximize revenues, and to drive technical innovation. By now, data mining and analytics have become vital components of game development. The amount of work being done in this area nowadays makes this an ideal time to put together a book on this subject.Data Analytics Applications in Gaming and EntertainTable of ContentsPart 1 – Introduction to game data mining. Part 2 – Data mining for games user research. Part 3 – Data mining for game technology.Part 4 – Visualization of large-scale game data.
£94.99
Taylor & Francis Ltd DiskBased Algorithms for Big Data
Book SynopsisDisk-Based Algorithms for Big Data is a product of recent advances in the areas of big data, data analytics, and the underlying file systems and data management algorithms used to support the storage and analysis of massive data collections. The book discusses hard disks and their impact on data management, since Hard Disk Drives continue to be common in large data clusters. It also explores ways to store and retrieve data though primary and secondary indices. This includes a review of different in-memory sorting and searching algorithms that build a foundation for more sophisticated on-disk approaches like mergesort, B-trees, and extendible hashing. Following this introduction, the book transitions to more recent topics, including advanced storage technologies like solid-state drives and holographic storage; peer-to-peer (P2P) communication; large file systems and query languages like Hadoop/HDFS, Hive, Cassandra, and Presto; and NoSQL databases like Neo4j for graph structurTable of ContentsForeword. Physical Disk Storage. File Management. Sorting. Searching. Disk-Based Sorting. Disk-Based Searching. Storage Technology. Large File Systems. NoSQL Storage. Appendix
£56.99
Taylor & Francis Ltd Large Databases in Economic History
Book SynopsisBig data' is now readily available to economic historians, thanks to the digitisation of primary sources, collaborative research linking different data sets, and the publication of databases on the internet. Key economic indicators, such as the consumer price index, can be tracked over long periods, and qualitative information, such as land use, can be converted to a quantitative form. In order to fully exploit these innovations it is necessary to use sophisticated statistical techniques to reveal the patterns hidden in datasets, and this book shows how this can be done.A distinguished group of economic historians have teamed up with younger researchers to pilot the application of new techniques to big data'. Topics addressed in this volume include prices and the standard of living, money supply, credit markets, land values and land use, transport, technological innovation, and business networks. The research spans the medieval, early modern and modern periods. Research methoTrade Review'This book makes applied econometric methods accessible to anyone interested in quantitative economic history' — Helen Paul, University of Southampton, UK.Table of Contents1. Introduction: Research methods for large databases Mark Casson and Nigar Hashimzade 2. Long-run Price Dynamics: The measurement of substitutability between commodities Mark Casson, Nigar Hashimzade and Catherine Casson 3. The Quantity Theory of Money in Historical Perspective Nick Mayhew 4. Medieval Foreign Exchange: A time series analysis Adrian Bell, Chris Brooks and Tony K. Moore 5. Local Property Values in Fourteenth and Fifteenth-century England Margaret Yates, Anna Campbell and Mark Casson 6. Visual Analytics for Large-scale Actor Networks, with an Application to Liverpool Business Networks John Haggerty and Sheryllynne Haggerty 7. Railways and Local Population Growth: Northamptonshire and Rutland, 1801-91 Mark Casson, Leigh Shaw-Taylor, A.E.M. Satchell and E.A. Wrigley 8. Women’s Land Ownership in Nineteenth-century England Janet Casson 9. The Diffusion of Steam Technology in England: Ploughing engines, 1860-1930 Jane McCutchan 10. Industrious Burglars: Funding consumption from property crime Jane Humphries, Sara Horrell and Ken Sneath
£47.49
Taylor & Francis Ltd Applied Cloud Deep Semantic Recognition
Book SynopsisThis book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issueTable of Contents1 Large-Scale Video Event Detection Using Deep Neural Networks 2 Leveraging Selectional Preferences for Anomaly Detection in Newswire Events 3 Abnormal Event Recognition in Crowd Environments 4 Cognitive Sensing: Adaptive Anomalies Detection with Deep Networks 5 Language-Guided Visual Recognition 6 Deep Learning for Font Recognition and Retrieval 7 A Distributed Secure Machine-Learning Cloud Architecture for Semantic Analysis 8 A Practical Look at Anomaly Detection Using Autoencoders with H2O and the R Programming Language
£114.00
Taylor & Francis Ltd Data Analytics for Smart Cities
Book SynopsisThe development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradiTable of ContentsPrefaceEditorsContributors1 Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition AssessmentAmir H. Alavi and William G. Buttlar2 Global Satellite Observations for Smart CitiesZhong Liu, Menglin S. Jin, Jacqueline Liu, Angela Li, William Teng, Bruce Vollmer, and David Meyer3 Advancing Smart and Resilient Cities with Big Spatial Disaster Data: Challenges, Progress, and OpportunitiesXuan Hu and Jie Gong4 Smart City Portrayal: Dynamic Visualization Applied to the Analysis of Underground MetroEvgheni Polisciuc and Penousal Machado5 Smart Bike-Sharing Systems for Smart CitiesHesham A. Rakha, Mohammed Elhenawy, Huthaifa I. Ashqar, Mohammed H. Almannaa, and Ahmed Ghanem6 Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal ProcessingAbdollah Malekjafarian, Eugene J. OBrien, and Fatemeh Golpayegani7 Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart CitiesArash Jahangiri, Sahar Ghanipoor Machiani, and Vahid Balali8 Exploratory Analysis of Run-Off-Road Crash PatternsMohammad Jalayer, Huaguo Zhou, and Subasish Das9 Predicting Traffic Safety Risk Factors Using an Ensemble ClassifierNasim Arbabzadeh, Mohammad Jalayer, and Mohsen Jafari10 Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public HousesClyde Zhengdao Li, Bo Yu, Cheng Fan, and Jingke HongIndex.
£104.50
CRC Press Data Analytics Applied to the Mining Industry
Book SynopsisData Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes cTable of Contents1. Digital Transformation of Mining. 2. Data Analytics and the Mining Value Chain. 3. Data Collection, Storage and Retrieval. 4. Making Sense of Data. 5. Analytics Toolset. 6. Making Decisions based on Analytics. 7. Process Performance Analytics. 8. Process Maintenance Analytics. 9. Data Analytics for Energy Efficiency and Gas Emission Reduction. 10. Future Skills Requirements.
£157.50
Taylor & Francis Ltd Data Visualization Made Simple
Book SynopsisData Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today's information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more.In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, anTrade Review"In the tradition of Edward Tufte’s design strategies for visual and narrative representations that frame our shared reality, Sosulski has artfully crafted a functional guide to design patterns and processes for shaping expressions of data and, most importantly, its usage in real-world organizational contexts. This will be a go-to reference in my library for years to come."—Jason Severs, Chief Design Officer, Droga5Table of Contents1. Becoming Visual 2. The Tools 3. The Graphics 4. The Data 5. The Design 6. The Audience 7. The Presentation 8. The Cases 9. The End
£35.14
