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Data science and big data analytics : discovering, analyzing, visualizing and presenting data /
This is a book about harnessing the power of data for new insights and 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 learn...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Indianpolis, IN :
John Wiley & Sons,
©2015.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 245 | 0 | 0 | |a Data science and big data analytics : |b discovering, analyzing, visualizing and presenting data / |c edited by EMC Education Services ; [key contributors David Dietrich, Barry Heller, Beibei Yang]. |
| 264 | 1 | |a Indianpolis, IN : |b John Wiley & Sons, |c ©2015. | |
| 300 | |a 1 online resource : |b color illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a CIP data; item not viewed. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 1. Introduction to big data analytics -- 2. Data analytics lifecycle -- 3. Review of basic data analytic methods using R -- 4. Advanced analytical theory and methods: clustering -- 5. Advanced analytical theory and methods: association rules -- 6. Advanced analytical theory and methods: regression -- 7. Advanced analytical theory and methods: classification -- 8. Advanced analytical theory and methods: time series analysis -- 9. Advanced analytical theory and methods: text analysis -- 10. Advanced analytics, technology and tools: MapReduce and Hadoop -- 11. Advanced analytics, technology and tools: in-database analytics -- 12. The endgame, or putting it all together. | |
| 520 | |a This is a book about harnessing the power of data for new insights and 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. It 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. -- |c Edited summary from book. | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Quantitative research. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Recherche quantitative. | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Database Management |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a Fouille de données. |2 eclas | |
| 650 | 7 | |a Traitement électronique des données. |2 eclas | |
| 650 | 7 | |a Programmation informatique. |2 eclas | |
| 650 | 7 | |a Quantitative research |2 fast | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Data Mining |2 gnd | |
| 650 | 7 | |a Big Data |2 gnd | |
| 650 | 7 | |a Datenanalyse |2 gnd | |
| 655 | 6 | |a Ressources Internet. | |
| 655 | 7 | |a e-books. |2 aat | |
| 655 | 7 | |a Livres numériques. |2 rvmgf | |
| 700 | 1 | |a Dietrich, David |c (Computer scientist), |e contributor. | |
| 700 | 1 | |a Heller, Robert, |d 1932-2012, |e contributor. | |
| 700 | 1 | |a Yang, Beibei, |e contributor. | |
| 710 | 2 | |a EMC Education Services. | |
| 758 | |i has work: |a Data science and big data analytics (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFBP3v4XdXkPCh6BXJ8cj3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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