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For years, business users have leveraged spreadsheets for storing and communicating data. Although spreadsheets may be easy to create and update, making important corporate decisions based on spreadsheets is risky due to the lack of data credibility. Whether you are a manager, developer, end user, or student, this book will help you turn spreadsheet data into credible, useful, reliable data that can be trusted in order to make important decisions.A chapter is dedicated to each of the following topics: Brief history of spreadsheets Spreadsheet paradox Spreadsheet varieties The PDF spreadsheet Spreadsheet formatting Spreadsheet disambiguation The intermediate database The ssdef database The corporate database The metadata database (mnemonic database) Political considerations Data modeling and the spreadsheet Case study

After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function.Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia Options for Julia IDEs Programming structures and functions Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing Data visualization and some simple yet powerful statistics for data exploration purposes Dimensionality reduction and feature evaluation Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines) Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights.Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.

This book lays out the roles everyone, up and down the organization chart, can and must play to ensure that data is up to the demands of its use, in day-in, day-out work, decision-making, planning, and analytics. By now, everyone knows that bad data extorts an enormous toll, adding huge (though often hidden) costs, and making it more difficult to make good decisions and leverage advanced analyses. While the problems are pervasive and insidious, they are also solvable! As Tom Redman, "the Data Doc," explains in Getting in Front on Data, the secret lies in getting the right people in the right roles to "get in front" of the management and social issues that lead to bad data in the first place. Everyone should see himself or herself in this book. We are all both data customers and data creators-after all, we use data created by others and create data used by others. And all of us must step up to these roles. As data customers, we must clarify our most important needs and communicate them to data creators. As data creators, we must strive to meet those needs by finding and eliminating the root causes of error. Getting in Front on Data proposes new roles for data professionals as: embedded data managers, in helping data customers and creators complete their work, DQ team leads, in connecting customers and creators, pulling the entire program together, and training people on their new roles, data maestros, in providing deep expertise on the really tough problems, chief data architects, in establishing common data definitions, and technologists, in increasing scale and decreasing unit cost. Getting in Front on Data introduces a new role, the data provocateur, the motive force in attacking data quality properly! This book urges everyone to unleash their inner provocateur. Finally, it crystallizes what senior leaders must do if their entire organizations are to enjoy the benefits of high-quality data!

Master a graph data modeling technique superior to traditional data modeling for both relational and NoSQL databases (graph, document, key-value, and column), leveraging cognitive psychology to improve big data designs. From Karen Lopez's Foreword:In this book, Thomas Frisendal raises important questions about the continued usefulness of traditional data modeling notations and approaches: Are Entity Relationship Diagrams (ERDs) relevant to analytical data requirements? Are ERDs relevant in the new world of Big Data? Are ERDs still the best way to work with business users to understand their needs? Are Logical and Physical Data Models too closely coupled? Are we correct in using the same notations for communicating with business users and developers? Should we refine our existing notations and tools to meet these new needs, or should we start again from a blank page? What new notations and approaches will we need? How will we use those to build enterprise database systems?Frisendal takes us through the history of data modeling, enterprise data models and traditional modeling methods. He points out, quite contentiously, where he feels we have gone wrong and in a few places where we got it right. He then maps out the psychology of meaning and context, while identifying important issues about where data modeling may or may not fit in business modeling. The main subject of this work is a proposal for a new exploration-driven modeling approach and new modeling notations for business concept models, business solutions models, and physical data models with examples on how to leverage those for implementing into any target database or datastore. These new notations are based on a property graph approach to modeling data. From the author's introduction:This book proposes a new approach to data modeling-one that "turns the inside out". For well over thirty years, relational modeling and normalization was the name of the game. One can ask that if normalization was the answer, what was the problem? There is something upside-down in that approach, as we will see in this book.Data analysis (modeling) is much like exploration. Almost literally. The data modeler wanders around searching for structure and content. It requires perception and cognitive skills, supported by intuition (a psychological phenomenon), that together determine how well the landscape of business semantics is mapped.Mapping is what we do; we explore the unknowns, draw the maps and post the "Here be Dragons" warnings. Of course there are technical skills involved, and surprisingly, the most important ones come from psychology and visualization (again perception and cognition) rather than pure mathematical ability.Two compelling events make a paradigm shift in data modeling possible, and also necessary: The advances in applied cognitive psychology address the needs for proper contextual framework and for better communication, also in data modeling, and The rapid intake of non-relational technologies (Big Data and NoSQL).

Organizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps.Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess.Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.

How do we design for data when traditional design techniques cannot extend to new database technologies? In this era of big data and the Internet of Things, it is essential that we have the tools we need to understand the data coming to us faster than ever before, and to design databases and data processing systems that can adapt easily to ever-changing data schemas and ever-changing business requirements. There must be no intellectual disconnect between data and the software that manages it. It must be possible to extract meaning and knowledge from data to drive artificial intelligence applications. Novel NoSQL data organization techniques must be used side-by-side with traditional SQL databases. Are existing data modeling techniques ready for all of this?The Concept and Object Modeling Notation (COMN) is able to cover the full spectrum of analysis and design. A single COMN model can represent the objects and concepts in the problem space, logical data design, and concrete NoSQL and SQL document, key-value, columnar, and relational database implementations. COMN models enable an unprecedented level of traceability of requirements to implementation. COMN models can also represent the static structure of software and the predicates that represent the patterns of meaning in databases.This book will teach you:the simple and familiar graphical notation of COMN with its three basic shapes and four line styleshow to think about objects, concepts, types, and classes in the real world, using the ordinary meanings of English words that aren’t tangled with confused techno-speakhow to express logical data designs that are freer from implementation considerations than is possible in any other notationhow to understand key-value, document, columnar, and table-oriented database designs in logical and physical termshow to use COMN to specify physical database implementations in any NoSQL or SQL database with the precision necessary for model-driven developmentA quick reference guide to COMN is included in an appendix. The full notation reference is available at http://www.tewdur.com/.

Data models are the main medium used to communicate data requirements from business to IT, and within IT from analysts, modelers, and architects, to database designers and developers. Therefore it's essential to get the data model right. But how do you determine right? That's where the Data Model Scorecard® comes in.The Data Model Scorecard is a data model quality scoring tool containing ten categories aimed at improving the quality of your organization's data models. Many of my consulting assignments are dedicated to applying the Data Model Scorecard to my client's data models - I will show you how to apply the Scorecard in this book.This book, written for people who build, use, or review data models, contains the Data Model Scorecard template and an explanation along with many examples of each of the ten Scorecard categories. There are three sections:In Section I, Data Modeling and the Need for Validation, receive a short data modeling primer in Chapter 1, understand why it is important to get the data model right in Chapter 2, and learn about the Data Model Scorecard in Chapter 3.In Section II, Data Model Scorecard Categories, we will explain each of the ten categories of the Data Model Scorecard. There are ten chapters in this section, each chapter dedicated to a specific Scorecard category: Chapter 4: Correctness Chapter 5: Completeness Chapter 6: Scheme Chapter 7: Structure Chapter 8: Abstraction Chapter 9: Standards Chapter 10: Readability Chapter 11: Definitions Chapter 12: Consistency Chapter 13: DataIn Section III, Validating Data Models, we will prepare for the model review (Chapter 14), cover tips to help during the model review (Chapter 15), and then review a data model based upon an actual project (Chapter 16).

A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.

Escrito por más de 120 profesionistas en la gestión de datos, la guía DAMA de los fundamentos para la gestión de datos (DAMA-DMBOK) es la recopilación más impresionante jamás realizada de principios y mejores prácticas en la gestión de datos. Este libro proporciona a profesionales de IT, ejecutivos, trabajadores del conocimiento, educadores e investigadores de gestión de datos un método de manejo de datos para desarrollar su arquitectura de información. En comparación con los libros PMBOK o el BABOK, el libro DAMA-DMBOK proporciona información sobre: Gobierno de datos Gestión de Arquitectura de Datos Desarrollo de datos Gestión de Operaciones de base de datos Gestión de la seguridad de datos Gestión de datos maestros y de referencia Gestión de almacenamiento de datos e inteligencia de negocios Gestión de documentación y contenidos Gestión de metadatos Gestión de calidad de datos Desarrollo profesionalComo introducción oficial a la gestión de datos, los objetivos de la guía DAMA-DMBOK son: Construir consensos para una visión general aplicable a las funciones de gestión de datos. Proporcionar definiciones estandarizadas para funciones comúnmente utilizadas en la gestión de datos, resultados, roles y otras terminologías. Documentar principios guiados para la gestión de datos. Presentar una visión neutral de buenas prácticas comúnmente aceptadas, técnicas y métodos ampliamente adoptados, y alcances alternativos significantes. Clarificar los alcances y límites de la gestión de datos. Desempeñarse como guía de referencia para una mayor compresión para el lectorEditores: Mark Mosley, Editor de desarrollo, Michel Brackett, Editor de producción, Susan Early, Asistente de editor, y Deborah Henderson, Patrocinador del proyecto. Prologo por John Zachman, Prefacio por John Schley (presidente internacional de DAMA) y Deborah Henderson (Presidenta de fundación DAMA, Vicepresidenta internacional de educación e investigación DAMA) y Epilogo por Michel Brackett. (Galardonado al Premio a la Trayectoria de DAMA Internacional). El DMBOK fue traducido al español por: Derly Almanza, Cinthia Carolina Sanchez Osorio, Karen Dawson, Ramón Vasquez, Juan Azcurra, Juan Diego Lorenzo, Fernado Giliberto, Sergio Tornati y Pablo Cigliuti.

Master how to data model MongoDB applications.Congratulations! You completed the MongoDB application within the given tight timeframe and there is a party to celebrate your application's release into production. Although people are congratulating you at the celebration, you are feeling some uneasiness inside. To complete the project on time required making a lot of assumptions about the data, such as what terms meant and how calculations are derived. In addition, the poor documentation about the application will be of limited use to the support team, and not investigating all of the inherent rules in the data may eventually lead to poorly-performing structures in the not-so-distant future.Now, what if you had a time machine and could go back and read this book. You would learn that even NoSQL databases like MongoDB require some level of data modeling. Data modeling is the process of learning about the data, and regardless of technology, this process must be performed for a successful application. You would learn the value of conceptual, logical, and physical data modeling and how each stage increases our knowledge of the data and reduces assumptions and poor design decisions.Read this book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives: Understand how data modeling contributes to the process of learning about the data, and is, therefore, a required technique, even when the resulting database is not relational. That is, NoSQL does not mean NoDataModeling! Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits. Explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts, and learn the basics of adding, querying, updating, and deleting data in MongoDB. Practice a streamlined, template-driven approach to performing conceptual, logical, and physical data modeling. Recognize that data modeling does not always have to lead to traditional data models! Distinguish top-down from bottom-up development approaches and complete a top-down case study which ties all of the modeling techniques together.

Master frameworks, techniques, and tools for conducting meetings, leading sessions and workshops, and transferring knowledge through education and training. In addition to focusing on proven methods, this book contains many new and innovative ideas developed through decades of the author's experience. There are 12 chapters: Chapter 1, Facilitation Framework, classifies all facilitation types into four generic categories: Strategies and Solutions, Programs and Processes, Learning and Development, and Cooperation and Collaboration. Chapter 2, Value Proposition, leverages the Career Steps Framework to prove the return on investment of facilitation skills and competency. Chapter 3, Facilitation Process, explains each phase of the facilitation process: Contract, Prepare, During Session, Conclude, and Evaluate. Chapter 4, Facilitation Leadership, explores Napoleon Hills' eleven factors of leadership, along with values, ethics, and competencies established by the International Association of Facilitators. Chapter 5, Engagers and Energizers, reveals the art and science of educating and transferring learning to adults and optimizing the engagement of session participants using Dr. Howard Gardner's Multiple Intelligences. Chapter 6, Tools, introduces the foundational technique of brainstorming and shows how to use 35 handy facilitation tools for a variety of situations including problem solving, group dynamics, and storytelling. Chapter 7, Workshop Environment, outlines facilitation-friendly principles followed by guidance on room set up, various seating patterns, equipment, food, and supplies. Chapter 8, Virtual Facilitation, provides suitable alternatives to face-to-face facilitation using practical techniques in four key areas: Engagement, Relationship, Communication, and Technology. Chapter 9, Cross-Cultural Facilitation, introduces proven techniques for how to facilitate learning transfer and effective collaboration across cultures through the application of Dr. Geert Hofsgtede's dimensions of cross-cultural communication. Chapter 10, Visual Facilitation, introduces the power of Visuals and Graphics Recording as a tool for effective collaboration and communication in organizational settings. Chapter 11, Self-Development, provides guidelines on how to develop your facilitation competency and track your progress. This chapter concludes with the author's own journey on becoming an accomplished facilitator. Chapter 12, Tools Library, outlines a step-by-step approach along with templates and examples where each of the 35 tools from Chapter 6 can be successfully leveraged.The book concludes with a section on facilitator and trainer resources.