Franklin

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

Author/Creator:
Manivannan, Arun, author.
Publication:
Birmingham : Packt Publishing, 2015.
Format/Description:
Book
1 online resource (254 p.)
Edition:
1st edition
Series:
Quick answers to common problems.
Quick answers to common problems
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Subjects:
Database searching.
Statistics -- Data processing.
Form/Genre:
Electronic books.
System Details:
text file
Summary:
Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes About This Book Implement Scala in your data analysis using features from Spark, Breeze, and Zeppelin Scale up your data anlytics infrastructure with practical recipes for Scala machine learning Recipes for every stage of the data analysis process, from reading and collecting data to distributed analytics Who This Book Is For This book shows data scientists and analysts how to leverage their existing knowledge of Scala for quality and scalable data analysis. What You Will Learn Familiarize and set up the Breeze and Spark libraries and use data structures Import data from a host of possible sources and create dataframes from CSV Clean, validate and transform data using Scala to pre-process numerical and string data Integrate quintessential machine learning algorithms using Scala stack Bundle and scale up Spark jobs by deploying them into a variety of cluster managers Run streaming and graph analytics in Spark to visualize data, enabling exploratory analysis In Detail This book will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightfulvisualizations, and machine learning toolkits. Starting with introductory recipes on utilizing the Breeze and Spark libraries, get to grips withhow to import data from a host of possible sources and how to pre-process numerical, string, and date data. Next, you'll get an understanding of concepts that will help you visualize data using the Apache Zeppelin and Bokeh bindings in Scala, enabling exploratory data analysis. iscover how to program quintessential machine learning algorithms using Spark ML library. Work through steps to scale your machine learning models and deploy them into a standalone cluster, EC2, YARN, and Mesos. Finally dip into the powerful options presented by Spark Streaming, and machine learning for streaming data, as well as utilizing Spark GraphX. Style and approach This book contains a rich set of recipes that covers the full spectrum of interesting data analysis tasks and will help you revolutionize your data analysis skills using Scala and Spark.
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
Using GraphX to analyze Twitter dataIndex
Notes:
Includes index.
Description based on online resource; title from PDF title page (ebrary, viewed January 4, 2016).
ISBN:
1-78439-499-8