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  • Book Overview & Buying Big Data Analytics with Java
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Big Data Analytics with Java

Big Data Analytics with Java

By : RAJAT MEHTA
4.3 (3)
Buy this Book
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Big Data Analytics with Java

Big Data Analytics with Java

4.3 (3)
By: RAJAT MEHTA
Buy this Book

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

Preface

Even as you read this content, there is a revolution happening behind the scenes in the field of big data. From every coffee that you pick up from a coffee store to everything you click or purchase online, almost every transaction, click, or choice of yours is getting analyzed. From this analysis, a lot of deductions are now being made to offer you new stuff and better choices according to your likes. These techniques and associated technologies are picking up so fast that as developers we all should be a part of this new wave in the field of software. This would allow us better prospects in our careers, as well as enhance our skill set to directly impact the business we work for.

Earlier technologies such as machine learning and artificial intelligence used to sit in the labs of many PhD students. But with the rise of big data, these technologies have gone mainstream now. So, using these technologies, you can now predict which advertisement the user is going to click on next, or which product they would like to buy, or it can also show whether the image of a tumor is cancerous or not. The opportunities here are vast. Big data in itself consists of a whole lot of technologies whether cluster computing frameworks such as Apache Spark or Tez or distributed filesystems such as HDFS and Amazon S3 or real-time SQL on underlying data using Impala or Spark SQL.

This book provides a lot of information on big data technologies, including machine learning, graph analytics, real-time analytics and an introductory chapter on deep learning as well. I have tried to cover both technical and conceptual aspects of these technologies. In doing so, I have used many real-world case studies to depict how these technologies can be used in real life. So this book will teach you how to run a fast algorithm on the transactional data available on an e-commerce site to figure out which items sell together, or how to run a page rank algorithm on a flight dataset to figure out the most important airports in a country based on air traffic. There are many content gems like these in the book for readers.

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Tech Concepts
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Programming languages
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