The first of its kind - a data visualisation book combining beautiful presentation with student-specific guidance and learning features. This resource empowers and inspires readers to be able to present data effectively.
Dieses Lehrbuch behandelt die wichtigsten Methoden zur Erkennung und Extraktion von ´´Wissen´´ aus numerischen und nicht-numerischen Datenbanken in Technik und Wirtschaft. Der Autor vermittelt einen kompakten und zugleich fundierten Überblick über die verschiedenen Methoden sowie deren Zielsetzungen und Eigenschaften. Dadurch werden Leser befähigt, Data Mining eigenständig anzuwenden.
If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You?ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Learn the algorithms and tools you need to build MapReduce applications with Hadoop and Spark for processing gigabyte, terabyte, or petabyte-sized datasets on clusters of commodity hardware. With this practical book, author Mahmoud Parsian, head of the big data team at Illumina, takes you step-by-stepthrough the design of machine-learning algorithms, such as Naive Bayes and Markov Chain, and shows you how apply them to clinical and biological datasets, using MapReduce design patterns. Apply MapReduce algorithms to clinical and biological data, such as DNA-Seq and RNA-Seq Use the most relevant regression/analytical algorithms used for different biological data types Apply t-test, joins, top-10, and correlation algorithms using MapReduce/Hadoop and Spark
Big data has been described as the ´´new oil.´´ Data Science and Big Data Analytics is all about harnessing the power of data for new insights. EMC, the world class information management company, has developed this book with you in mind--every concept and task can be completed using free or open-source software. This is also an approved study guide for the EMC Data Science Associate (EMCDSA) certification. You´ll learn everything you need to participate in big data projects, including how to: * Become an immediate contributor on a data science team * Reframe a business challenge as an analytics challenge * Deploy a structured lifecycle approach to data analytics problems * Apply appropriate analytic techniques and tools to analyze big data * Learn how to tell a compelling story with data to drive business action * Use open source tools such as R, Hadoop, and PostgreSQL * Prepare for EMC Proven Professional Data Scientist certification Today´s IT professionals, business analysts, and database administrators are expected to work with enormous datasets. After reading Data Science and Big Data Analytics, you´ll be on the cutting edge of this exciting paradigm shift. Data 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. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: * Become a contributor on a data science team * Deploy a structured lifecycle approach to data analytics problems * Apply appropriate analytic techniques and tools to analyzing big data * Learn how to tell a compelling story with data to drive business action * Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available at www.wiley.com/go/9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
Suitable for any manager or team leader that has the green light to implement a data governance program, this book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. It also includes case studies to detail ´do´s´ and ´don´ts´ in real-world situations.
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.
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
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: Data In 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).
Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here´s what to expect: Provides a background in big data and data engineering before moving on to data science and how it´s applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It´s a big, big data world out there--let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.