Book Image

Big Data Analytics with Java

Book Image

Big Data Analytics with Java


Overview of this book

This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.
Table of Contents (21 chapters)
Big Data Analytics with Java
About the Author
About the Reviewers
Customer Feedback
Free Chapter
Big Data Analytics with Java
Ensembling on Big Data
Real-Time Analytics on Big Data

Data cleaning and munging

The major amount of time spent by a developer while performing a data analysis task is spent in data cleaning or producing data in a particular format. Most of the time, while performing analysis of some log file data or getting files from some other system, there will definitely be some data cleaning involved. Data cleaning can be in many forms whether it involves discarding a certain kind of data or converting some bad data into a different format. Also note that most of the machine learning algorithms involve running algorithms on a mathematical dataset, but most of the practical datasets won't always have mathematical data. Converting text data to mathematical form is another important task that many developers need to do themselves before they can apply the data analysis tasks on the data.

If there are problems in the data that we need to resolve before we use it, then this approach of fixing the data is called as data munging. One of the common data munging...