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Java Data Analysis

Java Data Analysis

By : John R. Hubbard
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Java Data Analysis

Java Data Analysis

By: John R. Hubbard

Overview of this book

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you’ll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you’ll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.
Table of Contents (14 chapters)
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13
Index

Relational database tables

In a relational database, we think of each dataset as a table, with each data point being a row in the table. The dataset's signature defines the columns of the table.

Here is an example of a relational database table. It has four rows and five columns, representing a dataset of four data points with five fields:

Last name

First name

Sex

Age

ID

Adams

John

M

26

704601929

White

null

F

39

440163867

Jones

Paul

M

49

602588410

Adams

null

F

30

120096334

Note

There are two null fields in this table.

Because a database table is really a set of rows, the order of the rows is irrelevant, just as the order of the data points in any dataset is irrelevant. For the same reason, a database table may not contain duplicate rows and a dataset may not contain duplicate data points.

Key fields

A dataset may specify that all values of a designated field be unique. Such a field is called a key field for the dataset. In the preceding example, the ID number field...

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