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Scala data analysis cookbook : navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes / Arun Manivannan.

By: Material type: TextTextSeries: Quick answers to common problemsPublisher: Birmingham : Packt Publishing, 2015Description: 1 online resource : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781784394998
  • 1784394998
  • 1784396745
  • 9781784396749
Subject(s): Genre/Form: DDC classification:
  • 005.133 23
LOC classification:
  • QA76.9.D343
Online resources:
Contents:
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Breeze; Introduction; Getting Breeze -- the linear algebra library; Working with vectors; Working with matrices; Vectors and matrices with randomly distributed values; Reading and writing CSV files; Chapter 2: Getting Started with Apache Spark DataFrames; Introduction; Getting Apache Spark; Creating a DataFrame from CSV; Manipulating DataFrames; Creating a DataFrame from Scala case classes; Chapter 3: Loading and Preparing Data -- DataFrame
IntroductionLoading more than 22 features into classes; Loading JSON into DataFrames; Storing data as Parquet files; Using the Avro data model in Parquet; Loading from RDBMS; Preparing data in Dataframes; Chapter 4: Data Visualization; Introduction; Visualizing using Zeppelin; Creating scatter plots with Bokeh-Scala; Creating a time series MultiPlot with Bokeh-Scala; Chapter 5: Learning from Data; Introduction; Supervised and unsupervised learning; Gradient descent; Predicting continuous values using linear regression; Binary classification using LogisticRegression and SVM
Binary classification using LogisticRegression with Pipeline APIClustering using K-means; Feature reduction using principal component analysis; Chapter 6: Scaling Up; Introduction; Building the Uber JAR; Submitting jobs to the Spark cluster (local); Running the Spark Standalone cluster on EC2; Running the Spark Job on Mesos (local); Running the Spark Job on YARN (local); Chapter 7: Going Further; Introduction; Using Spark Streaming to subscribe to a Twitter stream; Using Spark as an ETL tool; Using StreamingLogisticRegression to classify a Twitter stream using Kafka as a training stream
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Online resource; title from PDF title page (EBSCO, viewed January 20, 2017)

Includes index.

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Breeze; Introduction; Getting Breeze -- the linear algebra library; Working with vectors; Working with matrices; Vectors and matrices with randomly distributed values; Reading and writing CSV files; Chapter 2: Getting Started with Apache Spark DataFrames; Introduction; Getting Apache Spark; Creating a DataFrame from CSV; Manipulating DataFrames; Creating a DataFrame from Scala case classes; Chapter 3: Loading and Preparing Data -- DataFrame

IntroductionLoading more than 22 features into classes; Loading JSON into DataFrames; Storing data as Parquet files; Using the Avro data model in Parquet; Loading from RDBMS; Preparing data in Dataframes; Chapter 4: Data Visualization; Introduction; Visualizing using Zeppelin; Creating scatter plots with Bokeh-Scala; Creating a time series MultiPlot with Bokeh-Scala; Chapter 5: Learning from Data; Introduction; Supervised and unsupervised learning; Gradient descent; Predicting continuous values using linear regression; Binary classification using LogisticRegression and SVM

Binary classification using LogisticRegression with Pipeline APIClustering using K-means; Feature reduction using principal component analysis; Chapter 6: Scaling Up; Introduction; Building the Uber JAR; Submitting jobs to the Spark cluster (local); Running the Spark Standalone cluster on EC2; Running the Spark Job on Mesos (local); Running the Spark Job on YARN (local); Chapter 7: Going Further; Introduction; Using Spark Streaming to subscribe to a Twitter stream; Using Spark as an ETL tool; Using StreamingLogisticRegression to classify a Twitter stream using Kafka as a training stream

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