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Java Data Analysis
Java Data Analysis

Java Data Analysis: Data mining, big data analysis, NoSQL, and data visualization

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Profile Icon John R. Hubbard
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Paperback Sep 2017 412 pages 1st Edition
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Java Data Analysis

Chapter 2. Data Preprocessing

Before data can be analyzed, it is usually processed into some standardized form. This chapter describes those processes.

Data types

Data is categorized into types. A data type identifies not only the form of the data but also what kind of operations can be performed upon it. For example, arithmetic operations can be performed on numerical data, but not on text data.

A data type can also determine how much computer storage space an item requires. For example, a decimal value like 3.14 would normally be stored in a 32-bit (four bytes) slot, while a web address such as https://google.com might occupy 160 bits.

Here is a categorization of the main data types that we will be working with in this book. The corresponding Java types are shown in parentheses:

  • Numeric types
    • Integer (int)
    • Decimal (double)
  • Text type
    • String (String)
  • Object types
    • Date (java.util.Date)
    • File (java.io.File)
    • General object (Object)

Variables

In computer science, we think of a variable as a storage location that holds a data value. In Java, a variable is introduced by declaring it to have a specific type. For example, consider the following statement:

String lastName;

It declares the variable lastName to have type String.

We can also initialize a variable with an explicit value when it is declared, like this:

double temperature = 98.6;

Here, we would think of a storage location named temperature that contains the value 98.6 and has type double.

Structured variables can also be declared and initialized in the same statement:

int[] a = {88, 11, 44, 77, 22};

This declares the variable a to have type int[] (array of ints) and contain the five elements specified.

Data points and datasets

In data analysis, it is convenient to think of the data as points of information. For example, in a collection of biographical data, each data point would contain information about one person. Consider the following data point:

("Adams", "John", "M", 26, 704601929)

It could represent a 26-year-old male named John Adams with ID number 704601929.

We call the individual data values in a data point fields (or attributes). Each of these values has its own type. The preceding example has five fields: three text and two numeric.

The sequence of data types for the fields of a data point is called its type signature. The type signature for the preceding example is (text, text, text, numeric, numeric). In Java, that type signature would be (String, String, String, int, int).

A dataset is a set of data points, all of which have the same type signature. For example, we could have a dataset that represents a group of people, each point representing a...

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...

Hash tables

A dataset of key-value pairs is usually implemented as a hash table. It is a data structure in which the key acts like an index into the set, much like page numbers in a book or line numbers in a table. This direct access is much faster than sequential access, which is like searching through a book page-by-page for a certain word or phrase.

In Java, we usually use the java.util.HashMap<Key,Value> class to implement a key-value pair dataset. The type parameters Key and Value are specified classes. (There is also an older HashTable class, but it is considered obsolete.)

Here is a data file of seven South American countries:

Hash tables

Figure 2-1 Countries data file

Here is a Java program that loads this data into a HashMap object:

Hash tables

Listing 2-1 HashMap example for Countries data

The Countries.dat file is in the data folder. Line 15 instantiates a java.io.File object named dataFile to represent the file. Line 16 instantiates a java.util.HashMap object named dataset. It is structured to have...

Data types


Data is categorized into types. A data type identifies not only the form of the data but also what kind of operations can be performed upon it. For example, arithmetic operations can be performed on numerical data, but not on text data.

A data type can also determine how much computer storage space an item requires. For example, a decimal value like 3.14 would normally be stored in a 32-bit (four bytes) slot, while a web address such as https://google.com might occupy 160 bits.

Here is a categorization of the main data types that we will be working with in this book. The corresponding Java types are shown in parentheses:

  • Numeric types

    • Integer (int)

    • Decimal (double)

  • Text type

    • String (String)

  • Object types

    • Date (java.util.Date)

    • File (java.io.File)

    • General object (Object)

Variables


In computer science, we think of a variable as a storage location that holds a data value. In Java, a variable is introduced by declaring it to have a specific type. For example, consider the following statement:

String lastName;

It declares the variable lastName to have type String.

We can also initialize a variable with an explicit value when it is declared, like this:

double temperature = 98.6;

Here, we would think of a storage location named temperature that contains the value 98.6 and has type double.

Structured variables can also be declared and initialized in the same statement:

int[] a = {88, 11, 44, 77, 22};

This declares the variable a to have type int[] (array of ints) and contain the five elements specified.

Data points and datasets


In data analysis, it is convenient to think of the data as points of information. For example, in a collection of biographical data, each data point would contain information about one person. Consider the following data point:

("Adams", "John", "M", 26, 704601929)

It could represent a 26-year-old male named John Adams with ID number 704601929.

We call the individual data values in a data point fields (or attributes). Each of these values has its own type. The preceding example has five fields: three text and two numeric.

The sequence of data types for the fields of a data point is called its type signature. The type signature for the preceding example is (text, text, text, numeric, numeric). In Java, that type signature would be (String, String, String, int, int).

A dataset is a set of data points, all of which have the same type signature. For example, we could have a dataset that represents a group of people, each point representing a unique member of the group. Since...

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Key benefits

  • Get your basics right for data analysis with Java and make sense of your data through effective visualizations.
  • Use various Java APIs and tools such as Rapidminer and WEKA for effective data analysis and machine learning.
  • This is your companion to understanding and implementing a solid data analysis solution using Java

Description

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.

Who is this book for?

If you are a student or Java developer or a budding data scientist who wishes to learn the fundamentals of data analysis and learn to perform data analysis with Java, this book is for you. Some familiarity with elementary statistics and relational databases will be helpful but is not mandatory, to get the most out of this book. A firm understanding of Java is required.

What you will learn

  • Develop Java programs that analyze data sets of nearly any size, including text
  • Implement important machine learning algorithms such as regression, classification, and clustering
  • Interface with and apply standard open source Java libraries and APIs to analyze and visualize data
  • Process data from both relational and non-relational databases and from time-series data
  • Employ Java tools to visualize data in various forms
  • Understand multimedia data analysis algorithms and implement them in Java.
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Table of Contents

13 Chapters
1. Introduction to Data Analysis Chevron down icon Chevron up icon
2. Data Preprocessing Chevron down icon Chevron up icon
3. Data Visualization Chevron down icon Chevron up icon
4. Statistics Chevron down icon Chevron up icon
5. Relational Databases Chevron down icon Chevron up icon
6. Regression Analysis Chevron down icon Chevron up icon
7. Classification Analysis Chevron down icon Chevron up icon
8. Cluster Analysis Chevron down icon Chevron up icon
9. Recommender Systems Chevron down icon Chevron up icon
10. NoSQL Databases Chevron down icon Chevron up icon
11. Big Data Analysis with Java Chevron down icon Chevron up icon
A. Java Tools Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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