Java Data Analysis.

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.