数据分析是包含检查、清洗、转化和建模的整个过程，旨在发现有用的信息。Java是实现数据分析任务的最流行语言之一。
约翰·R.哈伯德著的这本《Java数据分析(影印版)(英文版)》将提供数据科学和相关流程步骤的快速概览。你将从中学到统计数据分析技巧，并通过流行的Java API和类库把它们实现。你还能在实际案例中学到诸如分类和回归之类的机器学习概念。
在这个过程中，你将熟悉RapidMinet和Weka等工具，了解这些Java工具如何更有效地用于分析。还会学到如何与关系型、NoSQL和时间序列数据打交道。本书也将介绍如何利用不同的Java类库创建富有洞见又容易理解的图表。
学完本书，你将对多种数据分析技巧和相应的Java实现拥有扎实的基础知识。

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 Pre_processing
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
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
Summary
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
Summary
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
Summary
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
Summary
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
Java implementation with Weka
Support vector machine algorithms
Logistic regression
K-Nearest Neighbors
Fuzzy classification algorithms
Summary
Chapter 8: Cluster Analysis
Measuring distances
The curse of dimensionality
Hierarchical clustering
Weka implementation
K-means clustering
K-mecloids clustering
Affinity propagation clustering
Summary
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
Summary
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
Summary
Chapter 11：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
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 librari