Java Data Analysis by Hubbard, John R. -- Read -- Imperial Library of Trantor

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Java Data Analysis

Table of Contents Java Data Analysis Credits About the Author About the Reviewers www.PacktPub.com

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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? Other NoSQL database systems Summary

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 Summary

A. 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

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