Book description
Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0
About This Book
 Perform data analysis and build predictive models on huge datasets that leverage Apache Spark
 Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges
 Work through practical examples on realworld problems with sample code snippets
Who This Book Is For
This book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you!
What You Will Learn
 Consolidate, clean, and transform your data acquired from various data sources
 Perform statistical analysis of data to find hidden insights
 Explore graphical techniques to see what your data looks like
 Use machine learning techniques to build predictive models
 Build scalable data products and solutions
 Start programming using the RDD, DataFrame and Dataset APIs
 Become an expert by improving your data analytical skills
In Detail
This is the era of Big Data. The words ?Big Data' implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages.
Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R.
With ample case studies and realworld examples, Spark for Data Science will help you ensure the successful execution of your data science projects.
Style and approach
This book takes a stepbystep approach to statistical analysis and machine learning, and is explained in a conversational and easytofollow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Realworld examples with sample code snippets are also included.
Publisher resources
Table of contents

Spark for Data Science
 Spark for Data Science
 Credits
 Foreword
 About the Authors
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Big Data and Data Science – An Introduction
 2. The Spark Programming Model
 3. Introduction to DataFrames
 4. Unified Data Access

5. Data Analysis on Spark
 Data analytics life cycle
 Data acquisition
 Data preparation
 Basics of statistics
 Descriptive statistics
 Inferential statistics
 Summary
 References
 6. Machine Learning
 7. Extending Spark with SparkR
 8. Analyzing Unstructured Data
 9. Visualizing Big Data
 10. Putting It All Together
 11. Building Data Science Applications
Product information
 Title: Spark for Data Science
 Author(s):
 Release date: September 2016
 Publisher(s): Packt Publishing
 ISBN: 9781785885655
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