دانلود کتاب Java Data Analysis.

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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 dataXML 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 distributionMultivariate 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. SubqueriesTable 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 WekaSupport 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-medoids 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.