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Advanced analytics with Spark : patterns for learning from data at scale

Contributor(s): Ryza, Sandy | Laserson, Uri | Owen, Sean | Wills,JoshMaterial type: Text

TextPublication details: Mumbai : Shroff Publishers, 2017Edition: Second editionDescription: xii, 264 pages : illustrationsISBN: 9781491972922; 9789352135714Subject(s): Spark | Big data. | Data miningDDC classification: 006.3

Contents:

Analyzing big data -- Introduction to data analysis with Scala and Spark -- 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 trip data -- Estimating financial risk through Monte Carlo simulation -- Analyzing genomics data and the BDG project -- Analyzing neuroimaging data with PySpark and Thunder.

Summary: The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by presenting examples and a set of self-contained patterns for performing large-scale data analysis with Spark. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-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'll find these patterns useful for working on your own data applications.

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Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Lending Books	Main Library Stacks	REF	006.3 ADV (Browse shelf(Opens below))	Available		015581

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Previous	005.72 MEY Transforms in CSS : revamp the way you design	005.74 ULL A first course in database systems /	005.82 STA Cryptography and network security : principles and practice	006.3 ADV Advanced analytics with Spark : patterns for learning from data at scale	006.3 ZUR Introduction to artificial neural systems	006.31 BUD Fundamentals of deep learning :	006.31 GOL Genetic algorithms in search, optimization, and machine learning	Next

The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by presenting examples and a set of self-contained patterns for performing large-scale data analysis with Spark. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-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'll find these patterns useful for working on your own data applications.

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