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Data mining for dummies /
In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. It explains the details of the knowledge discovery process including: model creation, validity testing, and interpretation; effective...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
John Wiley & Sons,
2014.
|
| Colección: | --For dummies.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 520 | |a In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. It explains the details of the knowledge discovery process including: model creation, validity testing, and interpretation; effective communication of findings; available tools, both paid and open-source; and data selection, transformation, and evaluation. This guide takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. -- |c Edited summary from book. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Catching the data-mining train -- A day in your life as a data miner -- Teaming up to reach your goals -- Learning the laws of data mining -- Embracing the data-mining process -- Planning for data-mining success -- Gearing up with the right software -- Digging into your data -- Making new data -- Ferreting out public data sources -- Buying data -- Getting familiar with your data -- Dealing in graphic detail -- Showing your data who's boss -- Your exciting career in modeling -- Data mining using classic statistical methods -- Mining data for clues -- Expanding your horizons -- Ten great resources for data miners -- Ten useful kinds of analysis that complement data mining. | |
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