Hidden Bibliographic Details
| Other authors / contributors: | Laserson, Uri, author.
Owen, Sean, author.
Wills, Josh, author.
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| ISBN: | 9781491912768
1491912766
9781491912768
9781491912713
1491912715
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| Digital file characteristics: | text file
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| Notes: | Includes index.
Description based on print version record.
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| Summary: | 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.
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| Other form: | Print version: Ryza, Sandy, Advanced analytics with Spark. First edition 9781491912737
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