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Advanced analytics with Spark /
In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You{u2019}ll...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
2015.
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Ryza, Sandy, |e author. | |
| 245 | 1 | 0 | |a Advanced analytics with Spark / |c Sandy Ryza, Uri Laserson, Sean Owen and Josh Wills. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, |c 2015. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
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| 588 | |a Description based on print version record. | ||
| 500 | |a Includes index. | ||
| 520 | |a In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You{u2019}ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques{u2014}classification, collaborative filtering, and anomaly detection among others{u2014}to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you{u2019}ll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 630 | 0 | 0 | |a Spark (Electronic resource : Apache Software Foundation) |
| 630 | 0 | 7 | |a Spark (Electronic resource : Apache Software Foundation) |2 fast |0 (OCoLC)fst01938143 |
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| 650 | 6 | |a Exploration de données (Informatique) |x Logiciels. | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 700 | 1 | |a Laserson, Uri, |e author. | |
| 700 | 1 | |a Owen, Sean, |e author. | |
| 700 | 1 | |a Wills, Josh, |e author. | |
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