Java data analysis : data mining, big data analysis, NoSQL, and data visualization / John R. Hubbard.

Format:

Book

Author/Creator:

Hubbard, John R., author.

Language:

English

Subjects (All):

Java (Computer program language).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

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

System Details:

text file

Summary:

Get the most out of the popular Java libraries and tools to perform efficient data analysis About This Book Get your basics right for data analysis with Java and make sense of your data through effective visualizations. Use various Java APIs and tools such as Rapidminer and WEKA for effective data analysis and machine learning. This is your companion to understanding and implementing a solid data analysis solution using Java Who This Book Is For If you are a student or Java developer or a budding data scientist who wishes to learn the fundamentals of data analysis and learn to perform data analysis with Java, this book is for you. Some familiarity with elementary statistics and relational databases will be helpful but is not mandatory, to get the most out of this book. A firm understanding of Java is required. What You Will Learn Develop Java programs that analyze data sets of nearly any size, including text Implement important machine learning algorithms such as regression, classification, and clustering Interface with and apply standard open source Java libraries and APIs to analyze and visualize data Process data from both relational and non-relational databases and from time-series data Employ Java tools to visualize data in various forms Understand multimedia data analysis algorithms and implement them in Java. In Detail Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you'll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you'll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of...

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Introduction to Data Analysis

Origins of data analysis

The scientific method

Actuarial science

Calculated by steam

A spectacular example

Herman Hollerith

ENIAC

VisiCalc

Data, information, and knowledge

Why Java?

Java Integrated Development Environments

Summary

Chapter 2: Data Preprocessing

Data types

Variables

Data points and datasets

Null values

Relational database tables

Key fields

Key-value pairs

Hash tables

File formats

Microsoft Excel data

XML and JSON data

Generating test datasets

Metadata

Data cleaning

Data scaling

Data filtering

Sorting

Merging

Hashing

Chapter 3: Data Visualization

Tables and graphs

Scatter plots

Line graphs

Bar charts

Histograms

Time series

Java implementation

Moving average

Data ranking

Frequency distributions

The normal distribution

A thought experiment

The exponential distribution

Java example

Chapter 4: Statistics

Descriptive statistics

Random sampling

Random variables

Probability distributions

Cumulative distributions

The binomial distribution

Multivariate distributions

Conditional probability

The independence of probabilistic events

Contingency tables

Bayes' theorem

Covariance and correlation

The standard normal distribution

The central limit theorem

Confidence intervals

Hypothesis testing

Chapter 5: Relational Databases

The relation data model

Relational databases

Foreign keys

Relational database design

Creating a database

SQL commands

Inserting data into the database

Database queries

SQL data types

JDBC.

Using a JDBC PreparedStatement

Batch processing

Database views

Subqueries

Table indexes

Chapter 6: Regression Analysis

Linear regression

Linear regression in Excel

Computing the regression coefficients

Variation statistics

Java implementation of linear regression

Anscombe's quartet

Polynomial regression

Multiple linear regression

The Apache Commons implementation

Curve fitting

Chapter 7: Classification Analysis

Decision trees

What does entropy have to do with it?

The ID3 algorithm

Java Implementation of the ID3 algorithm

The Weka platform

The ARFF filetype for data

Java implementation with Weka

Bayesian classifiers

Support vector machine algorithms

Logistic regression

K-Nearest Neighbors

Fuzzy classification algorithms

Chapter 8: Cluster Analysis

Measuring distances

The curse of dimensionality

Hierarchical clustering

Weka implementation

K-means clustering

K-medoids clustering

Affinity propagation clustering

Chapter 9: Recommender Systems

Utility matrices

Similarity measures

Cosine similarity

A simple recommender system

Amazon's item-to-item collaborative filtering recommender

Implementing user ratings

Large sparse matrices

Using random access files

The Netflix prize

Chapter 10: NoSQL Databases

The Map data structure

SQL versus NoSQL

The Mongo database system

The Library database

Java development with MongoDB

The MongoDB extension for geospatial databases

Indexing in MongoDB

Why NoSQL and why MongoDB?

Other NoSQL database systems

Chapter 11: Big Data Analysis with Java

Scaling, data striping, and sharding

Google's PageRank algorithm

Google's MapReduce framework.

Some examples of MapReduce applications

The WordCount example

Scalability

Matrix multiplication with MapReduce

MapReduce in MongoDB

Apache Hadoop

Hadoop MapReduce

Appendix: Java Tools

The command line

Java

NetBeans

MySQL

MySQL Workbench

Accessing the MySQL database from NetBeans

The Apache Commons Math Library

The javax JSON Library

The Weka libraries

MongoDB

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed October 18, 2017).

OCLC:

1008968666