Book Image

Apache Spark for Data Science Cookbook

By : Padma Priya Chitturi
Book Image

Apache Spark for Data Science Cookbook

By: Padma Priya Chitturi

Overview of this book

Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.
Table of Contents (17 chapters)
Apache Spark for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction


R is a flexible, open source, and powerful statistical programming language. It is preferred by many professional statisticians and researchers in a variety of fields. It has extensive statistical and graphical capabilities. R combines the aspects of functional and object-oriented programming. One of the key features of R is implicit looping, which yields compact, simple code and frequently leads to faster execution. It provides a command-line interpreted statistical computing environment with built-in scripting language.

R is an integrated suite of software facilities for data manipulation, calculation, and graphical display. Its key strengths are effective data handling and storage facility, and a collection of tools for data analysis. It provides a number of extensions that support data processing and machine learning tasks. However, interactive analysis in R is limited as the runtime is single-threaded and can only process datasets that fit in a single machine's memory.

The...