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Advanced Analytics with Spark: Patterns for Learning from Data at Scale

Sandy Ryza
4.9/5 (18857 ratings)
Read Now Download Now ⏭ listen AudioBook
Description:In the second edition of 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. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques--including classification, clustering, collaborative filtering, and anomaly detection--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 the book's patterns useful for working on your own data applications.With this book, you will:Familiarize yourself with the Spark programming modelBecome comfortable within the Spark ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public data setsDiscover which machine learning tools make sense for particular problemsAcquire code that can be adapted to many usesWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Advanced Analytics with Spark: Patterns for Learning from Data at Scale. To get started finding Advanced Analytics with Spark: Patterns for Learning from Data at Scale, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.
Pages
280
Format
PDF, EPUB & Kindle Edition
Publisher
O'Reilly Media
Release
2017
ISBN
1491972904

Advanced Analytics with Spark: Patterns for Learning from Data at Scale

Sandy Ryza
4.4/5 (1290744 ratings)
Read Now Download now ⏭ listen AudioBook
Description: In the second edition of 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. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques--including classification, clustering, collaborative filtering, and anomaly detection--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 the book's patterns useful for working on your own data applications.With this book, you will:Familiarize yourself with the Spark programming modelBecome comfortable within the Spark ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public data setsDiscover which machine learning tools make sense for particular problemsAcquire code that can be adapted to many usesWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Advanced Analytics with Spark: Patterns for Learning from Data at Scale. To get started finding Advanced Analytics with Spark: Patterns for Learning from Data at Scale, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.
Pages
280
Format
PDF, EPUB & Kindle Edition
Publisher
O'Reilly Media
Release
2017
ISBN
1491972904

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