Explains how to make data-driven design part of your product design workflow, in a book that explains the best practices for analyzing and applying user data when making design decisions, teaches different approaches to data-informed design, explores potential pitfalls when relying on data and much more. Original.
This book is for any manager or team leader that has the green light to implement a data governance program. The problem of managing data continues to grow with issues surrounding cost of storage, exponential growth, as well as administrative, management and security concerns - the solution to being able to scale all of these issues up is data governance which provides better services to users and saves money. What you will find in this book is an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. With the provided framework and case studies you will be enabled and educated in launching your very own successful and money saving data governance program. Provides a complete overview of the data governance lifecycle, that can help you discern technology and staff needs Specifically aimed at managers who need to implement a data governance program at their company Includes case studies to detail ´do´s´ and ´don´ts´ in real-world situations
Never has it been more essential to work in the world of data. Scholars and students need to be able to analyze, design and curate information into useful tools of communication, insight and understanding. This book is the starting point in learning the process and skills of data visualization, teaching the concepts and skills of how to present data and inspiring effective visual design. Benefits of this book: * A flexible step-by-step journey that equips you to achieve great data visualization * A curated collection of classic and contemporary examples, giving illustrations of good and bad practice * Examples on every page to give creative inspiration * Illustrations of good and bad practice show you how to critically evaluate and improve your own work * Advice and experience from the best designers in the field * Loads of online practical help, checklists, case studies and exercises make this the most comprehensive text available
From an award-winning project comes an inspiring, collaborative book that makes data artistic, personal - and open to all Each week for a year, Giorgia and Stefanie sent each other a postcard describing what had happened to them during that week around a particular theme. But they didn´t write it, they drew it: a week of smiling, a week of apologies, a week of desires. Presenting their fifty-two cards, along with thoughts and ideas about the data-drawing process, Dear Data hopes to inspire you to draw, slow down and make connections with other people, to see the world through a new lens, where everything and anything can be a creative starting point for play and expression.
- übersichtliche und anwendungsbezogene Einführung in Data Science - zahlreiche Anwendungsfälle und Praxisbeispiele aus unterschiedlichen Branchen verdeutlichen Inhalte und Umsetzung, dabei werden Potenziale, aber auch mögliche Fallstricke aufgezeigt Bedingt durch technologische Entwicklungen wie das Internet of Things (IoT), mobile Anwendungen oder die Digitalisierung von Produktionsprozessen werden immer mehr Daten generiert. Klassische Methoden des Data Warehouse und der Business Intelligence stoßen hier oft an ihre Grenzen. Data Science mit seinen neuen Technologien und Methoden zur Speicherung großer Datenmengen in Kombination mit Konzepten etwa aus dem Bereich Machine Learning ermöglicht eine umfassende Analyse der Daten, um Muster zu identifizieren und Vorhersagemodelle zu entwickeln. Dieses Buch bietet eine umfassende Einführung in Data Science und dessen praktische Relevanz für Unternehmen. Dabei werden sowohl Aufgabenfelder und Methoden sowie Rollen- und Organisationsmodelle vorgestellt sowie Konzepte und Architekturen für Data Science erläutert. Zahlreiche Anwendungsfälle und Praxisbeispiele geben Einblicke in die reale Welt von Data Science und erlauben dem Leser einen direkten Transfer zu seiner täglichen Arbeit.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today´s techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book
This book constitutes the refereed proceedings of the 19th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2017, held in Moscow, Russia, in October 2017. The 16 revised full papers presented together with three invited papers were carefully reviewed and selected from 75 submissions. The papers are organized in the following topical sections: data analytics; next generation genomic sequencing: challenges and solutions; novel approaches to analyzing and classifying of various astronomical entities and events; ontology population in data intensive domains; heterogeneous data integration issues; data curation and data provenance support; and temporal summaries generation.
A guide for business enterprises on how to manage and govern big data, covering such topics as categories of data governance tools, data modeling, analytics and reporting, data security, and evaluation criteria for data governance platforms.