Big data analytics with Java : big data analytics - massive, predictive, social and self-driving / Rajat Mehta.

Format:

Book

Author/Creator:

Mehta, Rajat, author.

Language:

English

Subjects (All):

Java (Computer program language).

Data mining.

Big data.

Physical Description:

1 online resource (419 pages) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, India : Packt Publishing, 2017.

System Details:

text file

Biography/History:

MEHTA RAJAT: The author is a VP (Technical Architect) in technology in JP Morgan Chase in New York. The author is a sun certified java developer and has worked on java related technologies for more than 16 years. Current role for the past few years heavily involves the usage of bid data stack and running analytics on it. Author is also a contributor in various open source projects that are available on his GitHub repository and is also a frequent write on dev magazines.

Summary:

Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book 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. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. 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 In Detail 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. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications a...

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Big Data Analytics with Java

Why data analytics on big data?

Big data for analytics

Big data - a bigger pay package for Java developers

Basics of Hadoop - a Java sub-project

Distributed computing on Hadoop

HDFS concepts

Design and architecture of HDFS

Main components of HDFS

HDFS simple commands

Apache Spark

Concepts

Transformations

Actions

Spark Java API

Spark samples using Java 8

Loading data

Data operations - cleansing and munging

Analyzing data - count, projection, grouping, aggregation, and max/min

Actions on RDDs

Paired RDDs

Saving data

Collecting and printing results

Executing Spark programs on Hadoop

Apache Spark sub-projects

Spark machine learning modules

Mahout - a popular Java ML library

Deeplearning4j - a deep learning library

Summary

Chapter 2: First Steps in Data Analysis

Datasets

Data cleaning and munging

Basic analysis of data with Spark SQL

Building SparkConf and context

Dataframe and datasets

Load and parse data

Analyzing data - the Spark-SQL way

Spark SQL for data exploration and analytics

Market basket analysis - Apriori algorithm

Implementation of the Apriori algorithm in Apache Spark

Efficient market basket analysis using FP-Growth algorithm

Running FP-Growth on Apache Spark

Chapter 3: Data Visualization

Data visualization with Java JFreeChart

Using charts in big data analytics

Time series chart

All India seasonal and annual average temperature series dataset

Simple single Time Series chart

Multiple Time Series on a single chart window

Bar charts

Histograms

When would you use a histogram?.

How to make histograms using JFreeChart?

Line charts

Scatter plots

Box plots

Advanced visualization technique

Prefuse

IVTK Graph toolkit

Other libraries

Chapter 4: Basics of Machine Learning

What is machine learning?

Real-life examples of machine learning

Type of machine learning

A small sample case study of supervised and unsupervised learning

Steps for machine learning problems

Choosing the machine learning model

What are the feature types that can be extracted from the datasets?

How do you select the best features to train your models?

How do you run machine learning analytics on big data?

Getting and preparing data in Hadoop

Training and storing models on big data

Apache Spark machine learning API

Chapter 5: Regression on Big Data

Linear regression

What is simple linear regression?

Where is linear regression used?

Logistic regression

Which mathematical functions does logistic regression use?

Where is logistic regression used?

Predicting heart disease using logistic regression

Chapter 6: Naive Bayes and Sentiment Analysis

Conditional probability

Bayes theorem

Naïve bayes algorithm

Advantages of naïve bayes

Disadvantages of naïve bayes

Sentimental analysis

Concepts for sentimental analysis

Tokenization

Stop words removal

Stemming

N-grams

Term presence and Term Frequency

TF-IDF

Bag of words

Dataset

Data exploration of text data

Sentimental analysis on this dataset

SVM or Support Vector Machine

Chapter 7: Decision Trees

What is a decision tree?

Building a decision tree

Choosing the best features for splitting the datasets

Data exploration

Cleaning and munging the data

Training and testing the model

Summary.

Chapter 8: Ensembling on Big Data

Ensembling

Types of ensembling

Bagging

Boosting

Advantages and disadvantages of ensembling

Random forests

Gradient boosted trees (GBTs)

Classification problem and dataset used

Training and testing our random forest model

Training and testing our gradient boosted tree model

Chapter 9: Recommendation Systems

Recommendation systems and their types

Content-based recommendation systems

Content-based recommender on MovieLens dataset

Collaborative recommendation systems

Advantages

Disadvantages

Alternating least square - collaborative filtering

Chapter 10: Clustering and Customer Segmentation on Big Data

Clustering

Types of clustering

Hierarchical clustering

K-means clustering

Bisecting k-means clustering

Customer segmentation

Clustering for customer segmentation

Changing the clustering algorithm

Chapter 11: Massive Graphs on Big Data

Refresher on graphs

Representing graphs

Common terminology on graphs

Common algorithms on graphs

Plotting graphs

Massive graphs on big data

Graph analytics

GraphFrames

Building a graph using GraphFrames

Graph analytics on airports and their flights

Graph analytics on flights data

Chapter 12: Real-Time Analytics on Big Data

Real-time analytics

Big data stack for real-time analytics

Real-time SQL queries on big data

Real-time data ingestion and storage

Real-time data processing

Real-time SQL queries using Impala

Flight delay analysis using Impala

Apache Kafka

Spark Streaming

Trending videos

Chapter 13: Deep Learning Using Big Data

Introduction to neural networks

Perceptron

Problems with perceptrons.

Sigmoid neuron

Multi-layer perceptrons

Accuracy of multi-layer perceptrons

Deep learning

Advantages and use cases of deep learning

Flower species classification using multi-Layer perceptrons

Deeplearning4j

Hand written digit recognizition using CNN

Diving into the code:

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed August 25, 2017).

OCLC:

1001347189