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Too big to ignore : the business case for big data /
How businesses of all shapes and sizes can harness the power of Big Data If you haven't heard of Big Data, you're increasingly in the minority. People produce a mind-boggling amount of data every day-so much that making sense of it all is simply beyond the current capabilities of most orga...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2013]
|
| Colección: | Wiley and SAS business series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Simon, Phil. | |
| 245 | 1 | 0 | |a Too big to ignore : |b the business case for big data / |c Phil Simon. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c [2013] | |
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| 490 | 1 | |a Wiley & SAS business series | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher; resource not viewed. | |
| 505 | 0 | |a Too Big to Ignore; Contents; List of Tables and Figures; Preface; Acknowledgments; Introduction: This Ain't Your Father's Data; Better Car Insurance through Data; Potholes and General Road Hazards; Recruiting and Retention; How Big Is Big? The Size of Big Data; Why Now? Explaining the Big Data Revolution; The Always-On Consumer; The Plummeting of Technology Costs; The Rise of Data Science; Google and Infonomics; The Platform Economy; The 11/12 Watershed: Sandy and Politics; Social Media and Other Factors; Central Thesis of Book; Plan of Attack; Who Should Read This Book?; Summary; Notes. | |
| 505 | 8 | |a Chapter 1 Data 101 and the Data DelugeThe Beginnings: Structured Data; Structure This! Web 2.0 and the Arrival of Big Data; Unstructured Data; Semi-Structured Data; Metadata; The Composition of Data: Then and Now; The Current State of the Data Union; The Enterprise and the Brave New Big Data World; The Data Disconnect; Big Tools and Big Opportunities; Summary; Notes; Chapter 2 Demystifying Big Data; Characteristics of Big Data; Big Data Is Already Here; Big Data Is Extremely Fragmented; Big Data Is Not an Elixir; Small Data Extends Big Data; Big Data Is a Complement, Not a Substitute. | |
| 505 | 8 | |a Big Data Can Yield Better PredictionsBig Data Giveth-and Big Data Taketh Away; Big Data Is Neither Omniscient Nor Precise; Big Data Is Generally Wide, Not Long; Big Data Is Dynamic and Largely Unpredictable; Big Data Is Largely Consumer Driven; Big Data Is External and "Unmanageable" in the Traditional Sense; Big Data Is Inherently Incomplete; Big Overlap: Big Data, Business Intelligence, and Data Mining; Big Data Is Democratic; The Anti-Definition: What Big Data Is Not; Summary; Notes; Chapter 3 The Elements of Persuasion: Big Data Techniques; The Big Overview. | |
| 505 | 8 | |a Statistical Techniques and MethodsRegression; A/B Testing; Data Visualization; Heat Maps; Time Series Analysis; Automation; Machine Learning and Intelligence; Sensors and Nanotechnology; RFID and NFC; Semantics; Natural Language Processing; Text Analytics; Sentiment Analysis; Big Data and the Gang of Four; Predictive Analytics; Two Key Laws of Big Data; Collaborative Filtering; Limitations of Big Data; Summary; Notes; Chapter 4 Big Data Solutions; Projects, Applications, and Platforms; Hadoop; Other Data Storage Solutions; NoSQL Databases; NewSQL; Columnar Databases. | |
| 505 | 8 | |a Google: Following the Amazon Model?Websites, Start-Ups, and Web Services; Kaggle; Other Start-Ups; Hardware Considerations; The Art and Science of Predictive Analytics; Summary; Notes; Chapter 5 Case Studies: The Big Rewards of Big Data; Quantcast: A Small Big Data Company; Steps: A Big Evolution; Buy Your Audience; Results; Lessons; Explorys: The Human Case for Big Data; Better Healthcare through Hadoop; Steps; Results; Lessons; NASA: How Contests, Gamification, and OpenInnovation Enable Big Data; Background; Examples; A Sample Challenge; Lessons; Summary; Notes. | |
| 520 | |a How businesses of all shapes and sizes can harness the power of Big Data If you haven't heard of Big Data, you're increasingly in the minority. People produce a mind-boggling amount of data every day-so much that making sense of it all is simply beyond the current capabilities of most organizations. Traditional tools and systems just can't handle Big Data. How does a marketer identify an emerging trend when she can't read every tweet, blog post, and customer review? How do we separate meaningful information from the noise of the 2.5 quintillion bytes of data we create every day? Simpl. | ||
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| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Business |x Data processing. | |
| 650 | 0 | |a Big data. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Bases de données |x Gestion. | |
| 650 | 6 | |a Gestion |x Informatique. | |
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| 830 | 0 | |a Wiley and SAS business series. | |
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