What this book covers

Chapter 1, Introduction to Data Analysis, introduces the subject, citing its historical development and its importance in solving critical problems of the society.

Chapter 2, Data Preprocessing, describes the various formats for data storage, the management of datasets, and basic preprocessing techniques such as sorting, merging, and hashing.

Chapter 3, Data Visualization, covers graphs, charts, time series, moving averages, normal and exponential distributions, and applications in Java.

Chapter 4, Statistics, reviews fundamental probability and statistical principles, including randomness, multivariate distributions, binomial distribution, conditional probability, independence, contingency tables, Bayes' theorem, covariance and correlation, central limit theorem, confidence intervals, and hypothesis testing.

Chapter 5, Relational Databases, covers the development and access of relational databases, including foreign keys, SQL, queries, JDBC, batch processing, database views, subqueries, and indexing. You will learn how to use Java and JDBC to analyze data stored in relational databases.

Chapter 6, Regression Analysis, demonstrates an important part of predictive analysis, including linear, polynomial, and multiple linear regression. You will learn how to implement these techniques in Java using the Apache Commons Math library.

Chapter 7, Classification Analysis, covers decision trees, entropy, the ID3 algorithm and its Java implementation, ARFF files, Bayesian classifiers and their Java implementation, support vector machine (SVM) algorithms, logistic regression, K-nearest neighbors, and fuzzy classification algorithms. You will learn how to implement these algorithms in Java with the Weka library.

Chapter 8, Cluster Analysis, includes hierarchical clustering, K-means clustering, K-medoids clustering, and affinity propagation clustering. You will learn how to implement these algorithms in Java with the Weka library.

Chapter 9, Recommender Systems, covers utility matrices, similarity measures, cosine similarity, Amazon's item-to-item recommender system, large sparse matrices, and the historic Netflix Prize competition.

Chapter 10, NoSQL Databases, centers on the MongoDB database system. It also includes geospatial databases and Java development with MongoDB.

Chapter 11, Big Data Analysis, covers Google's PageRank algorithm and its MapReduce framework. Particular attention is given to the complete Java implementations of two characteristic examples of MapReduce: WordCount and matrix multiplication.

Appendix, Java Tools, walks you through the installation of all of the software used in the book: NetBeans, MySQL, Apache Commons Math Library, javax.json, Weka, and MongoDB.