Java Data Analysis

John R. Hubbard

• Publisher: Packt Publishing
• Year: 2017
• eBook ISBN: 9781787286405
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Modelling & Design
• Index: Computer Science

Get the most out of the popular Java libraries and tools to perform efficient data analysisAbout 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 JavaWho This Book Is ForIf 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 DetailData 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 the various data analysis techniques, and how to implement them using Java.Style and approachThe book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy-to-follow examples, this book will turn you into an ace data analyst in no time.

Java Data Analysis

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Table of Contents

Java Data Analysis

Credits

About the Author

About the Reviewers

www.PacktPub.com

eBooks, discount offers, and more

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

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

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Java implementation with Weka

Support vector machine algorithms

Logistic regression

K-Nearest Neighbors

Fuzzy classification algorithms

Summary

8. Cluster Analysis

Measuring distances

The curse of dimensionality

Hierarchical clustering

Weka implementation

K-means clustering

K-medoids clustering

Affinity propagation clustering

Summary

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

Summary

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...

Table of contents

• Java Data Analysis