Book Image

Java Data Analysis

By : John R. Hubbard
Book Image

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 (20 chapters)
Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Data ranking


Another common way to present small datasets is to rank the data, assigning ordinal labels (first, second, third, and so on) to the data points. This can be done by sorting the data on the key field.

Figure 3-17 shows an Excel worksheet with data on students' grade-point averages (GPAs).

Figure 3-17. Excel data

To rank these, select Tools | Data Analysis | Rank and Percentile to bring up the Moving Average dialog box:

Figure 3-18. Ranking scores in Excel

Here, we have identified cells B1 to B17 as holding the data, with the first cell being a label. The output is to start in cell D1. The results are shown in the following screenshot:

Figure 3-19. Results from Excel ranking

Column D contains the (relative) index of the record being ranked. For example, cells D3-G3 show the record of the third student in the original list (in column A) with name Cohen: that student ranks second, at the 86th percentile.