Big Data Analytics with Java

Learn the basics of analytics on big data using Java, machine learning and other big data tools
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Big Data Analytics with Java

Rajat Mehta

Learn the basics of analytics on big data using Java, machine learning and other big data tools

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Book Details

ISBN 139781787288980
Paperback418 pages

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 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

Chapter 1: Big Data Analytics with Java
Why data analytics on big data?
Summary
Chapter 2: First Steps in Data Analysis
Datasets
Data cleaning and munging
Basic analysis of data with Spark SQL
Implementation of the Apriori algorithm in Apache Spark
Summary
Chapter 3: Data Visualization
Data visualization with Java JFreeChart
Time Series chart
Bar charts
Histograms
Line charts
Scatter plots
Box plots
Advanced visualization technique
Summary
Chapter 4: Basics of Machine Learning
What is machine learning?
Summary
Chapter 5: Regression on Big Data
Linear regression
Logistic regression
Summary
Chapter 6: Naive Bayes and Sentiment Analysis
Conditional probability
Bayes theorem
Naive Bayes algorithm
Sentimental analysis
SVM or Support Vector Machine
Summary
Chapter 7: Decision Trees
What is a decision tree?
Summary
Chapter 8: Ensembling on Big Data
Ensembling
Summary
Chapter 9: Recommendation Systems
Recommendation systems and their types
Content-based recommendation systems
Summary
Chapter 10: Clustering and Customer Segmentation on Big Data
Clustering
Customer segmentation
Dataset
Data exploration
Clustering for customer segmentation
Summary
Chapter 11: Massive Graphs on Big Data
Refresher on graphs
Massive graphs on big data
Summary
Chapter 12: Real-Time Analytics on Big Data
Real-time analytics
Summary
Chapter 13: Deep Learning Using Big Data
Introduction to neural networks
Perceptron
Deep learning
Flower species classification using multi-Layer perceptrons
Deeplearning4j
Hand written digit recognizition using CNN
Summary

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

Authors

Table of Contents

Chapter 1: Big Data Analytics with Java
Why data analytics on big data?
Summary
Chapter 2: First Steps in Data Analysis
Datasets
Data cleaning and munging
Basic analysis of data with Spark SQL
Implementation of the Apriori algorithm in Apache Spark
Summary
Chapter 3: Data Visualization
Data visualization with Java JFreeChart
Time Series chart
Bar charts
Histograms
Line charts
Scatter plots
Box plots
Advanced visualization technique
Summary
Chapter 4: Basics of Machine Learning
What is machine learning?
Summary
Chapter 5: Regression on Big Data
Linear regression
Logistic regression
Summary
Chapter 6: Naive Bayes and Sentiment Analysis
Conditional probability
Bayes theorem
Naive Bayes algorithm
Sentimental analysis
SVM or Support Vector Machine
Summary
Chapter 7: Decision Trees
What is a decision tree?
Summary
Chapter 8: Ensembling on Big Data
Ensembling
Summary
Chapter 9: Recommendation Systems
Recommendation systems and their types
Content-based recommendation systems
Summary
Chapter 10: Clustering and Customer Segmentation on Big Data
Clustering
Customer segmentation
Dataset
Data exploration
Clustering for customer segmentation
Summary
Chapter 11: Massive Graphs on Big Data
Refresher on graphs
Massive graphs on big data
Summary
Chapter 12: Real-Time Analytics on Big Data
Real-time analytics
Summary
Chapter 13: Deep Learning Using Big Data
Introduction to neural networks
Perceptron
Deep learning
Flower species classification using multi-Layer perceptrons
Deeplearning4j
Hand written digit recognizition using CNN
Summary

Book Details

ISBN 139781787288980
Paperback418 pages
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