Database design and theory Books

Book SynopsisWorking scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models.

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Book SynopsisWhen we look at some of the most pressing issues in environmental politics today, it is hard to avoid data technologies. Big data, artificial intelligence, and data dashboards all promise “revolutionary” advances in the speed and scale at which governments, corporations, conservationists, and even individuals can respond to environmental challenges. By bringing together scholars from geography, anthropology, science and technology studies, and ecology, The Nature of Data explores how the digital realm is a significant site in which environmental politics are waged. This collection as a whole makes the argument that we cannot fully understand the current conjuncture in critical, global environmental politics without understanding the role of data platforms, devices, standards, and institutions. In particular, The Nature of Data addresses the contested practices of making and maintaining data infrastructure, the imaginaries produced by data infrastruTrade Review"This book is a necessary piece to lay the groundwork for a political ecology of data and urge more research in this direction. . . . A welcome integration of digital social sciences, political ecology, critical GIS, and science and technology studies, and as such which will be of interest to scholars across these fields, but also to conservation practitioners. This collection of essays might also be useful as a methodological text for advanced graduate students."—Anne-Lise Boyer, H-Environment"Thanks to insights from ecomedia studies, environmental humanists are increasingly studying how the environment becomes digital and the digital becomes environmental. The Nature of Data ably contributes to this research."—Heather Houser, ISLE“Data may not grow on trees, but it increasingly shapes how humans know, govern, and struggle over forests—and indeed, much of the nonhuman world. The Nature of Data captures this moment empirically while advancing political ecology conceptually. An altogether stellar volume.”—Susanne Freidberg, author of Fresh: A Perishable History“In accelerating ways, environmental politics are data politics. This powerful book shows what this looks like in different settings and at different scales, persuasively calling for a new subfield focused on the political ecology of data. Extending from prior work on the delimitations and politics of environmental science, the collection draws out what environmental data can help us see, what it cuts out, and how environmental data production itself is both polluting and weighted by commercial interests.”—Kim Fortun, author of Advocacy after Bhopal: Environmentalism, Disaster, New Global Orders“This is an original, diverse, and scintillating collection. Researchers working on political ecology of conservation and conservation social science have not taken challenges of data justice or the political economy of data production seriously enough. We must—and this book shows us how and why.”—Dan Brockington, author of Celebrity Advocacy and International Development“As environments are reverse engineered to match the spreadsheets and management platforms in which they are tallied, the environmental politics of data control, organization, and proliferation will hugely influence ecologies and politics going forward. By putting that insight front and center, Goldstein and Nost assemble a sweeping set of essays that gaze into the sometimes-disturbing future of the planet.”—Paul Robbins, author of Political Ecology: A Critical Introduction“This volume contributes to the growing discourses around political ecological work on data and the infrastructures that sustain, produce, and exchange them. The volume is startling in both its depth and breadth of engagement with timely and important topics; it marks a significant contribution to a growing field.”—Jim Thatcher, author of Thinking Big Data in Geography: New Regimes, New Research“Throughout, the reader is plunged into the complexities of digital systems, the environments they monitor and conserve, and the limits to their governance and oversight across a variety of places and scales and sovereignties. What emerges is resolutely not an endorsement of further digitalization of nature but a recognition that digitalization is perhaps yet another set of processes in which nature is actively produced.”—Matthew W. Wilson, author of New Lines: Critical GIS and the Trouble of the MapTable of ContentsList of Illustrations List of Tables Introduction: Infrastructuring Environmental Data Jenny Goldstein and Eric Nost Part 1. Sensors, Servers, and Structures 1. Data’s Metropolis: The Physical Footprints of Data Circulation and Modern Finance Graham Pickren 2. An Emerging Satellite Ecosystem and the Changing Political Economy of Remote Sensing Luis F. Alvarez León 3. Smart Earth: Environmental Governance in a Wired World Karen Bakker and Max Ritts 4. Data, Colonialism, and the Transformation of Nature in the Pacific Northwest Anthony Levenda and Zbigniew Grabowski Part 2. Civic Science and Community-Driven Data 5. Environmental Sensing Infrastructures and Just Good Enough Data Jennifer Gabrys and Helen Pritchard 6. Collaborative Modeling as Sociotechnical Data Infrastructure in Rural Zimbabwe M. V. Eitzel, Jon Solera, K. B. Wilson, Abraham Mawere Ndlovu, Emmanuel Mhike Hove, Daniel Ndlovu, Abraham Changarara, Alice Ndlovu, Kleber Neves, Adnomore Chirindira, Oluwasola E. Omoju, Aaron C. Fisher, and André Veski 7. Citizen Scientists and Conservation in the Anthropocene: From Monitoring to Making Coral Irus Braverman 8. Data Infrastructures, Indigenous Knowledge, and Environmental Observing in the Arctic Noor Johnson, Colleen Strawhacker, and Peter Pulsifer 9. Digital Infrastructure and the Affective Nature of Value in Belize Patrick Gallagher 10. Infrastructuring Environmental Data Justice Dawn Walker, Eric Nost, Aaron Lemelin, Rebecca Lave, Lindsey Dillon, and Environmental Data and Governance Initiative (EDGI) Part 3. Governing Data, Infrastructuring Land and Resources 11. “A Poverty of Data”? Exporting the Digital Revolution to Farmers in the Global South Madeleine Fairbairn and Zenia Kish 12. Illicit Digital Environments: Monitoring and Surveilling Environmental Crime in Southeast Asia Hilary O. Faxon and Jenny Goldstein 13. Data Gaps: Penguin Science and Petrostate Formation in the Falkland Islands (Malvinas) James J. A. Blair 14. Data Structures, Indigenous Ontologies, and Hydropower in the U.S. Northwest Corrine Armistead 15. How Forest Became Data: The Remaking of Ground-Truth in Indonesia Cindy Lin Conclusion: Toward a Political Ecology of Data Rebecca Lave, Eric Nost, and Jenny Goldstein Source Acknowledgments Contributors Index

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Book SynopsisIn this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately.

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Book SynopsisTrade Review"Among the metaverse of possible books on Text as Data that could have been published . . . I was pleased that my universe produced this one. I will assign this book as a critical part of my own course on content analysis for years to come, and it has already altered and improved the coherence of my own vocabulary and articulation for several critical choices underlying the process of turning text into data. . . . Highly recommend."---James Evans, Sociological Methods & Research

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Book SynopsisThis practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures.

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Book SynopsisData fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of each architecture to help data professionals understand its pros and cons.

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Book SynopsisWith this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn.

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Book SynopsisKafka in Action is a practical, hands-on guide to building Kafka-based data pipelines. Filled with real-world use cases and scenarios, this book probes Kafka's most common use cases, ranging from simple logging through managing streaming data systems for message routing, analytics, and more. In systems that handle big data, streaming data, or fast data, it's important to get your data pipelines right. Apache Kafka is a wicked-fast distributed streaming platform that operates as more than just a persistent log or a flexible message queue. Key Features · Understanding Kafka's concepts · Implementing Kafka as a message queue · Setting up and executing basic ETL tasks · Recording and consuming streaming data · Working with Kafka producers and consumers from Java applications · Using Kafka as part of a large data project team · Performing Kafka developer and admin tasks Written for intermediate Java developers or data engineers. No prior knowledge of Kafka is required. About the technology Apache Kafka is a distributed streaming platform for logging and streaming data between services or applications. With Kafka, it's easy to build applications that can act on or react to data streams as they flow through your system. Operational data monitoring, large scale message processing, website activity tracking, log aggregation, and more are all possible with Kafka. Dylan Scott is a software developer with over ten years of experience in Java and Perl. His experience includes implementing Kafka as a messaging system for a large data migration, and he uses Kafka in his work in the insurance industry.

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Book SynopsisTrade Review"If you work in the field of data management, chance are you know of David Plotkin. He has worked in the field for decades and has written extensively on data, metadata, and related issues. With the second edition of his book, Plotkin has written the quintessential book on data stewardship. He defines data stewardship and how it fits together with data governance, delivering best practices, policies, and procedures that make sense. The book offers up practical advice on implementing data stewardship and defines the roles and responsibilities of the data steward. But he also devotes a chapter offering guidance on training for data stewards, and another chapter on the metrics that can be used to measure the performance of data stewards. And if your organization is new or just starting to implementing data governance and stewardship, then you absolutely must read Chapter 4, “Implementing Data Stewardship.” It will step you through the process of championing, communicating, and gaining support for data stewardship." --Data and Technology TodayTable of Contents1. Data Stewardship and Data Governance: How They Fit Together2. Understanding the Types of Data Stewardship3. Data Stewardship Roles and Responsibilities4. Implementing Data Stewardship5. Training Business Data Stewards6. Practical Data Stewardship7. Important Roles of Data Stewards8. Measuring Data Stewardship Progress: The Metrics9. Rating Your Data Stewardship Maturity10. Big Data Stewardship and Data Lakes11. Governing and Stewarding your Data using Data Domains12. Summing It All UpAppendixA: Example Definition and DerivationB: Sample Training Plan Outlines

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Book SynopsisOver the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry.

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Book SynopsisThe immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos. This edited book gathers together an international set of researchers to investigate the possibilities offered by data-fabric solutions; the volume focuses in particular on data architectures and on semantic changes in future data landscapes.

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Book SynopsisAuthor Mark Edmondson, Google Developer Expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations.

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Book SynopsisWith Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real time.

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Book SynopsisThis book focuses on how to use various data mining techniques to develop real-world applications. It offers practical applications with a clear understanding of data mining concepts. The book also has a special chapter on case studies of data mining in practice.

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Book SynopsisGraph Databases in Action teaches readers everything they need to know to begin building and running applications powered by graph databases. Right off the bat, seasoned graph database experts introduce readers to just enough graph theory, the graph database ecosystem, and a variety of datastores. They also explore modelling basics in action with real-world examples, then go hands-on with querying, coding traversals, parsing results, and other essential tasks as readers build their own graph-backed social network app complete with a recommendation engine! Key Features · Graph database fundamentals · An overview of the graph database ecosystem · Relational vs. graph database modelling · Querying graphs using Gremlin · Real-world common graph use cases For readers with basic Java and application development skills building in RDBMS systems such as Oracle, SQL Server, MySQL, and Postgres. No experience with graph databases is required. About the technology Graph databases store interconnected data in a more natural form, making them superior tools for representing data with rich relationships. Unlike in relational database management systems (RDBMS), where a more rigid view of data connections results in the loss of valuable insights, in graph databases, data connections are first priority. Dave Bechberger has extensive experience using graph databases as a product architect and a consultant. He’s spent his career leveraging cutting-edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and supply chain management. He’s an active member of the graph community and has presented on a wide variety of graph-related topics at national and international conferences. Josh Perryman is technologist with over two decades of diverse experience building and maintaining complex systems, including high performance computing (HPC) environments. Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.

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Book SynopsisData is a valuable corporate asset and its effective management is vital to an organisation’s success and survival. With this book you will learn to master the key principles of data management and use them to implement best practices in your organization. This professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function. This fully updated new edition provides new chapters on the most important data topics such as big data, artificial intelligence, linked data and concept systems. Principles of Data Management is fully aligned with syllabus for the BCS Professional Certificate in Data Management Essentials, making this the go-to text to unlocking the value of your data. Ideal for business managers and all involved in the development of information systems as well as data management professionals Comprehensive and descriptive view of data management Suitable for all levels, from beginners to advanced learners Must-read for anyone involved in the development of systems to manage data Trade ReviewThis book is an excellent guide to understanding data management theory and techniques. It works at all levels: from beginner to advanced, and from reference source to the practicalities of implementation. I would highly recommend to anyone wanting to get to grips with data management, regardless of experience in the field. -- Ian Wallis, Managing Director, Data Strategists LtdKeith has developed a broad and thorough understanding of all aspects of data management over many years, so is without doubt one of the authorities on data management. This updated book includes reference to a number of new techniques as well as refining existing guidance on data modelling and database structures. Keith clearly explains both the importance of planning and analysis of databases and repositories and an explanation of key techniques to achieve this. A ‘must buy’ for the bookshelf of any data management practitioner. -- Julian Schwarzenbach, Chair of the BCS Data Management Specialist GroupThis book provides a comprehensive and descriptive view of data management within a database setting. This is a must read for anyone involved in the development of systems to manage data. This book is as useful as it is interesting. It covers everything you need to know about getting the most out of your data management processes and architecture. -- Ian Rush, Data & Process Advantage LtdTable of ContentsPart 1: Preliminaries Chapter 1 Data and the enterprise Chapter 2 Databases and their development Chapter 3 What is data management? Part 2: Data Administration Chapter 4 Corporate data modelling Chapter 5 Data definition and naming Chapter 6 Metadata Chapter 7 Data quality Chapter 8 Data accessibility Chapter 9 Master data management Part 3: Database and Repository Administration Chapter 10 Database administration Chapter 11 Repository administration Part 4: The Data Management Environment Chapter 12 The use of packaged application software Chapter 13 Distributed data and databases Chapter 14 Business intelligence Chapter 15 Object orientation Chapter 16 Multimedia Chapter 17 Integrating data and web technology Chapter 18 Linked data Chapter 19 Concept systems Chapter 20 Big data and artificial intelligence Appendices Appendix A Comparison of data modelling notations Appendix B Generic data models Appendix C HTML and XML Appendix D Techniques and skills for data management Appendix E Data strategy Appendix F International standards for data management Appendix G The BCS Data Management Essentials syllabus

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Book SynopsisWhy does one smoker die of lung cancer but another live to 100? The answer is 'The Hidden Half' - those random, unknowable variables that mess up our attempts to comprehend the world.We humans are very clever creatures - but we're idiots about how clever we really are. In this entertaining and ingenious book, Blastland reveals how in our quest to make the world more understandable, we lose sight of how unexplainable it often is. The result - from GDP figures to medicine - is that experts know a lot less than they think. Filled with compelling stories from economics, genetics, business, and science, The Hidden Half is a warning that an explanation which works in one arena may not work in another. Entertaining and provocative, it will change how you view the world.Trade ReviewHighly original and challenging... Once you have read this book, you can't unread it. * Daniel Finkelstein, The Times *Fascinating and provocative. Blastland is one of the most original thinkers around. * Tim Harford - Financial Times columnist and author of The Undercover Economist *Elegantly written and mind-expanding, The Hidden Half will enthrall you with its storytelling while educating you with its scientific depth. * Daniel H. Pink - bestselling author of Drive *Brilliant. Blastland provides an explanation of the need for humility in the face of the inevitable limits to knowledge and our all-too-human temptation to tell stories about the world that go far beyond what the evidence will support. * Diane Coyle - Bennett Professor of Public Policy, Cambridge University *Fascinating... As John Wooden said, it's what you learn after you know it all that counts. * Andrew Gelman - author of Rich State Poor State Red State Blue State *Excellent. Blastland makes a compelling case that God is fond of playing dice with the cosmos-and the list of unpredictable things keeps growing, not shrinking. * Phillip Tetlock - bestselling author of Superforecasting *Beautifully written and often very funny. Anyone making decision that matter should enjoy this book and profit from its lessons. * Dame Frances Cairncross - Chair, Executive Committee of the Institute for Fiscal Studies *Thought-provoking. * UnHerd *Table of Contents0: Prologue 1: Bill is not Ben 2: I am not constant 3: Here is not there, now is not then 4: One path is not enough 5: The principle isn't practical 6: Big is not small 7: Big is not clear 8: The ignorant chicken 9: What to do 10: Postscript

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Book SynopsisGain practical insights by exploiting data in your business to build advanced predictive modeling applications Key Features A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Master open source Python tools to build sophisticated predictive models Book Description Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: 1. Learning Predictive Analytics with Python 2. Mastering Predictive Analytics with Python What you will learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis Who this book is for This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you.Table of ContentsTable of Contents Module 1 Module 2

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Book SynopsisStreamline your workflow, transform raw data into actionable insights, and use Alteryx Designer to shape, design, and visualize data Key Features Acquire the skills necessary to perform analytics operations like an expert Discover hidden trends and insights in your data from various sources to make accurate predictions Reduce the time and effort required to derive insights from your data Purchase of the print or Kindle book includes a free eBook in the PDF format Book DescriptionAlteryx allows you to create data manipulation and analytic workflows with a simple, easy-to-use, code-free UI, and perform fast-executing workflows, offering multiple ways to achieve the same results. The Alteryx Designer Cookbook is a comprehensive guide to maximizing your Alteryx skills and determining the best ways to perform data operations. This book's recipes will guide you through an analyst's complete journey, covering all aspects of the data life cycle. The first set of chapters will teach you how to read data from various sources to obtain reports and pass it through the required adjustment operations for analysis. After an explanation of the Alteryx platform components with a particular focus on Alteryx Designer, you’ll be taken on a tour of what and how you can accomplish by using this tool. Along the way, you’ll learn best practices and design patterns. The book also covers real-world examples to help you apply your understanding of the features in Alteryx to practical scenarios. By the end of this book, you’ll have enhanced your proficiency with Alteryx Designer and an improved ability to execute tasks within the tool efficiently.What you will learn Speed up the cleansing, data preparing, and shaping process Perform operations and transformations on the data to suit your needs Blend different types of data sources for analysis Pivot and un-pivot the data for easy manipulation Perform aggregations and calculations on the data Encapsulate reusable logic into macros Develop high-quality, data-driven reports to improve consistency Who this book is forThis book is for data analysts, data professionals, and business intelligence professionals seeking to harness the full potential of the tool. A basic understanding of Alteryx Designer and Alteryx terminology, including macros, apps, and workflows, is all you need to get started with this book.Table of ContentsTable of Contents Input data from files Working with databases Data Preparation Data Transformations Data Parsing Grouping Data Blending and Merging data Aggregations Dynamic Operations/ Tools Macros and Apps Downloads, APIs & Web Services Developer options Reporting with Alteryx Outputting Data

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Book SynopsisA comprehensive yet quick guide to the best approaches to designing data visualizations, with real examples and illustrative diagrams. Whatever the desired outcome ensure success by following this expert design process. This book is for anyone who has responsibility for, or is interested in trying to find innovative and effective ways to visually analyze and communicate data. There is no skill, no knowledge and no role-based pre-requisites or expectations of anyone reading this book.

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Book SynopsisA pragmatic approach to Big Data by taking the reader on a journey between Big Data (what it is) and the Smart Data (what it is for). Today’s decision making can be reached via information (related to the data), knowledge (related to people and processes), and timing (the capacity to decide, act and react at the right time). The huge increase in volume of data traffic, and its format (unstructured data such as blogs, logs, and video) generated by the “digitalization” of our world modifies radically our relationship to the space (in motion) and time, dimension and by capillarity, the enterprise vision of performance monitoring and optimization.Table of ContentsPREFACE ix LIST OF FIGURES AND TABLES xiii INTRODUCTION xv CHAPTER 1. WHAT IS BIG DATA? 1 1.1. The four “V”s characterizing Big Data 3 1.1.1. V for “Volume” 3 1.1.2. V for “Variety” 4 1.1.3. V for “Velocity” 8 1.1.4. V for “Value”, associated with Smart Data 9 1.2. The technology that supports Big Data 10 CHAPTER 2. WHAT IS SMART DATA? 13 2.1. How can we define it? 13 2.1.1. More formal integration into business processes 13 2.1.2. A stronger relationship with transaction solutions 14 2.1.3. The mobility and the temporality of information 15 2.2. The structural dimension 17 2.2.1. The objectives of a BICC 17 2.3. The closed loop between Big Data and Smart Data 18 CHAPTER 3. ZERO LATENCY ORGANIZATION 21 3.1. From Big Data to Smart Data for a zero latency organization 21 3.2. Three types of latency 21 3.2.1. Latency linked to data 21 3.2.2. Latency linked to analytical processes 22 3.2.3. Latency linked to decisionmaking processes 23 3.2.4. Action latency 23 CHAPTER 4. SUMMARY BY EXAMPLE 25 4.1. Example 1: date/product/price recommendation 26 4.1.1. Steps “1” and “2” 28 4.1.2. Steps “3” and “4”: enter the world of “Smart Data” 29 4.1.3. Step “5”: the presentation phase 29 4.1.4. Step “6”: the “Holy Grail” (the purchase) 30 4.1.5. Step “7”: Smart Data 30 4.2. Example 2: yield/revenue management (rate controls) 31 4.2.1. How it works: an explanation based on the Tetris principle (see Figure 4.4) 35 4.3. Example 3: optimization of operational performance 38 4.3.1. General department (top management) 42 4.3.2. Operations departments (middle management) 42 4.3.3. Operations management (and operational players) 43 CONCLUSION 47 BIBLIOGRAPHY 51 GLOSSARY 53 INDEX 57

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Book SynopsisDeveloping a data curious organization will take advantage of the burgeoning data resources available as a result of increasing digitalization. With this book, author Carl Allchin shows today's business professionals how to become data empowered.

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Book SynopsisThis 2010 comprehensive overview of relational mathematics provides an easy introduction to the topic, but is nevertheless theoretically sound and up-to-date. Assuming a minimum of prerequisites, it is suitable for applied scientists, explaining all of the necessary mathematics from scratch.Trade Review'The author illustrates the mathematical theory and demonstrates practical tasks in operations research, social sciences and the humanities with graspable results at the research level. The multitude of examples is visualized using matrices and graphs. The book is suitable for applied scientists and the broad community of researchers in various fields who use relations in their scientific work.' Zentralblatt MATHTable of ContentsPreface; 1. Introduction; Part I. Representations of Relations: 2. Sets, subsets and elements; 3. Relations; Part II. Operations and Constructions: 4. Algebraic operations on relations; 5. Order and function: the standard view; 6. Relations and vectors; 7. Domain construction; Part III. Algebra: 8. Relation algebra; 9. Orders and lattices; 10. Rectangles, fringes, inverses; 11. Concept analysis; Part IV. Applications: 12. Orderings: an advanced view; 13. Preference and indifference; 14. Aggregating preferences; 15. Relational graph theory; 16. Standard Galois mechanisms; Part V. Advanced Topics: 17. Mathematical applications; 18. Implication structures; 19. Power operations; Appendix A. Notations; Appendix B. Postponed proofs of Part II; Appendix C. Algebraic visualization; Appendix D. Historical annotations; Table of symbols; References; Index.

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Book SynopsisIn the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.Table of Contents1. Introduction; 2. Setting the stage for clustering; 3. Agglomerative hierarchical clustering; 4. k-means clustering; 5. Gaussian mixture models; 6. Advanced methods; 7. Conclusion.

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Book SynopsisYou want the rigour of good data architecture at the speed of agile? Then this is the missing link - your step-by-step guide to Data Vault success. Success with a Data Vault starts with the business and ends with the business. Sure, theres some technical stuff in the middle, and it is absolutely essential -- but its not sufficient on its own. This book will help you shape the business perspective, and weave it into the more technical aspects of Data Vault modeling. You can read the foundational books and go on courses, but one massive risk still remains. Dan Linstedt, the founder of the Data Vault, very clearly directs those building a Data Vault to base its design on an enterprise ontology. And Hans Hultgren similarly stresses the importance of the business concepts model. So its important. We get that. But: What on earth is an enterprise ontology/business concept model, cause I wont know if Ive got one if I dont know what Im looking for? If I cant find one, how do I get my hands on such a thing? Even if I have one of these wonderful things, how do I apply it to get the sort of Data Vault thats recommended? Its actually not as hard as some would fear to answer all of these questions, and its certainly worth the effort. This book just might save you a world of pain. Its a supplement to other material on Data Vault modeling, but its the vital missing link to finding simplicity for Data Vault success.

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Book SynopsisBuild a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning Key Features Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset Acquire implementable knowledge on digital transformation through 8 practical laws Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly Book DescriptionIn today’s digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization’s data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise. The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company’s operations through AI and machine learning. By the end of the book, you will have the tools and techniques to drive your organization’s digital transformation. Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: "Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon."What you will learn Train your organization to transition from being data-driven to being value-driven Navigate and master the big data business model maturity index Learn a methodology for determining the economic value of your data and analytics Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used Become aware of digital transformation misconceptions and pitfalls Create empowered and dynamic teams that fuel your organization’s digital transformation Who this book is forThis book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.Table of ContentsTable of Contents The CEO Mandate: Become Value-driven, Not Data-driven Value Engineering: The Secret Sauce for Data Science Success A Review of Basic Economic Concepts University of San Francisco Economic Value of Data Research Paper The Economic Value of Data Theorems The Economics of Artificial Intelligence The Schmarzo Economic Digital Asset Valuation Theorem The 8 Laws of Digital Transformation Creating a Culture of Innovation Through Empowerment Appendix A: My Most Popular Economics of Data, Analytics, and Digital Transformation Infographics Appendix B: The Economics of Data, Analytics, and Digital Transformation Cheat Sheet

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Book SynopsisLeverage BigQuery to understand and prepare your data to ensure that it's accurate, reliable, and ready for analysis and modeling Key Features Use mock datasets to explore data with the BigQuery web UI, bq CLI, and BigQuery API in the Cloud console Master optimization techniques for storage and query performance in BigQuery Engage with case studies on data exploration and preparation for advertising, transportation, and customer support data Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges. The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem. The book doesn’t merely offer theoretical insights; it’s a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You’ll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems. By the end of this book, you’ll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.What you will learn Assess the quality of a dataset and learn best practices for data cleansing Prepare data for analysis, visualization, and machine learning Explore approaches to data visualization in BigQuery Apply acquired knowledge to real-life scenarios and design patterns Set up and organize BigQuery resources Use SQL and other tools to navigate datasets Implement best practices to query BigQuery datasets Gain proficiency in using data preparation tools, techniques, and strategies Who this book is forThis book is for data analysts seeking to enhance their data exploration and preparation skills using BigQuery. It guides anyone using BigQuery as a data warehouse to extract business insights from large datasets. A basic understanding of SQL, reporting, data modeling, and transformations will assist with understanding the topics covered in this book.Table of ContentsTable of Contents Introducing BigQuery and Its Components BigQuery Organization and Design Exploring Data in BigQuery Loading and Transforming Data Querying BigQuery Data Exploring Data with Notebooks Further Exploring and Visualizing Data An Overview of Data Preparation Tools Cleansing and Transforming Data Best Practices for Data Preparation, Optimization, and Cost Control Hands-On Exercise – Analyzing Advertising Data Hands-On Exercise Analyzing Transportation Data Hands-On Exercise – Analyzing Customer Support Data Summary and Future Directions

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Book SynopsisEverything you need to know to apply data contracts and build a truly data-driven organization that harnesses quality data to deliver tangible business value Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand data contracts and their power to resolving the problems in contemporary data platforms Learn how to design and implement a cutting-edge data platform powered by data contracts Access practical guidance from the pioneer of data contracts to get expert insights on effective utilization Book DescriptionDespite the passage of time and the evolution of technology and architecture, the challenges we face in building data platforms persist. Our data often remains unreliable, lacks trust, and fails to deliver the promised value. With Driving Data Quality with Data Contracts, you’ll discover the potential of data contracts to transform how you build your data platforms, finally overcoming these enduring problems. You’ll learn how establishing contracts as the interface allows you to explicitly assign responsibility and accountability of the data to those who know it best—the data generators—and give them the autonomy to generate and manage data as required. The book will show you how data contracts ensure that consumers get quality data with clearly defined expectations, enabling them to build on that data with confidence to deliver valuable analytics, performant ML models, and trusted data-driven products. By the end of this book, you’ll have gained a comprehensive understanding of how data contracts can revolutionize your organization’s data culture and provide a competitive advantage by unlocking the real value within your data.What you will learn Gain insights into the intricacies and shortcomings of today’s data architectures Understand exactly how data contracts can solve prevalent data challenges Drive a fundamental transformation of your data culture by implementing data contracts Discover what goes into a data contract and why it’s important Design a modern data architecture that leverages the power of data contracts Explore sample implementations to get practical knowledge of using data contracts Embrace best practices for the successful deployment of data contracts Who this book is forIf you’re a data engineer, data leader, architect, or practitioner thinking about your data architecture and looking to design one that enables your organization to get the most value from your data, this book is for you. Additionally, staff engineers, product managers, and software engineering leaders and executives will also find valuable insights.Table of ContentsTable of Contents A Brief History of Data Platforms Introducing Data Contracts How to Get Adoption in Your Organization Bringing Data Consumers and Generators Closer Together Embedding Data Governance What Makes Up a Data Contract A Contract-Driven Data Architecture A Sample Implementation Implementing Data Contracts In Your Organization Data Contracts in Practice

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Book SynopsisIn Silico Chemistry and Biology: Current and Future Prospects provides a compact overview on recent advances in this highly dynamic branch of chemistry. Various methods of protein modelling and computer-assisted drug design are presented, including fragment- and ligand-based approaches. Many successful practical applications of these techniques are demonstrated. The authors also look to the future and describe the main challenges of the field. Table of ContentsFrom the Content: - Different Methods for in silico Drug Discovery - Force Fields: Use and Applications - Protein Modelling - Molecular Docking and MD: Mimicking the Real Biological Process - Ligand-based Drug Design: Recent Advancements - Fragment-based Methods for Drug Discovery - Successful Applications of in silico Drug Design Processes

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