Big Data Analytics with Java

Description:

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Key Features

Acquire real-world set of tools for building enterprise level data science applications
Surpasses the barrier of other languages in data science and learn create useful object-oriented codes
Extensive use of Java compliant big data tools like apache spark, Hadoop, etc.

Book Description

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 Naive 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.

What you will learn

Start from simple analytic tasks on big data
Get into more complex tasks with predictive analytics on big data using machine learning
Learn real time analytic tasks
Understand the concepts with examples and case studies
Prepare and refine data for analysis
Create charts in order to understand the data
See various real-world datasets

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eBook Details:
Category: Java
Author: Rajat Mehta
Language: English
ISBN10: 1787288986
ISBN13: 9781787288980
Pages: 418
PubDate: 2017-07-31 00:00:00
UploadDate: 9/25/2017 5:49:21 pm

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