醉染图书Java数据分析()9787564177362

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 Develomn Evironments
Summary
Chapter 2：Data Pre_processing
Da yes
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
SL commands
Inserting data into the database
Database queries
SL da yes
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
Anscombes 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 3 algorithm
Java Implementation of the 3 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 Neiors
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
Amazons item-to-item collaborative filtering recommender
Implementing user ratings
Large sparse matrices
Using random access files
The Netflix prize
Summary
Chapter 10：NoSL Databases
The Map data structure
SL versus NoSL
The Mongo database system
The Library database
Java development with MongoDB
The MongoDB extension for geospatial databases
Indexing in MongoDB
Why NoSL and why MongoDB?
Other NoSL database systems
Summary
Chapter 11：Data Analysis with Java
Scaling， da sriing， and sharding
Googles PageRank algorithm
Googles 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
MySL
MySL Workbench
Accessing the MySL database from NetBeans
The Apache Commons Math Library
The javax JSON Library
The Weka libraries
MongoDB
Index