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Java Data Analysis

You're reading from   Java Data Analysis Data mining, big data analysis, NoSQL, and data visualization

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787285651
Length 412 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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John R. Hubbard John R. Hubbard
Author Profile Icon John R. Hubbard
John R. Hubbard
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Data Analysis 2. Data Preprocessing FREE CHAPTER 3. Data Visualization 4. Statistics 5. Relational Databases 6. Regression Analysis 7. Classification Analysis 8. Cluster Analysis 9. Recommender Systems 10. NoSQL Databases 11. Big Data Analysis with Java A. Java Tools Index

Tables and graphs

Most datasets are still maintained in tabular form, as in Figure 2-12, but tables with thousands of rows and many columns are far more common than that simple example. Even when many of the data fields are text or Boolean, a graphical summary can be much easier to comprehend.

There are several different ways to represent data graphically. In addition to more imaginative displays, such as Minard's map (Figure 3-1), we review the more standard methods here.

Scatter plots

A scatter plot, also called a scatter chart, is simply a plot of a dataset whose signature is two numeric values. If we label the two fields x and y, then the graph is simply a two-dimensional plot of those (x, y) points.

Scatter plots are easy to do in Excel. Just enter the numeric data in two columns and then select Insert | All Charts | X Y (Scatter). Here is a simple example:

Scatter plots

Figure 3-2. Excel data

The given data is shown in Figure 3-2 and its corresponding scatter plot is in Figure 3-3:

Scatter plots

Figure 3-3. Scatter...

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