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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

Frequency distributions


A frequency distribution is a function that gives the number of occurrences of each item of a dataset. It is like a histogram (for example, Figure 3-8). It amounts to counting the number or percentage of occurrences of each possible value.

Figure 3-20 shows the relative frequency of each of the 26 letters in English language text. For example, the letter e is the most frequent, at 13%. This information would be useful if you were producing a word game like Scrabble or if you were attempting to decipher an encoded English message.

Figure 3-20. Frequency distribution of letters in English

Some frequency distributions occur naturally. One of the most common is the bell-shaped distribution that results when compiling many measurements of a single quality. It can be seen in Figure 3-21, which shows the distribution of the heights of American males, aged 25-34:

Figure 3-21. Heights of American males

In statistics, a dataset of measurements {x11, x2,..., xn} is called a sample...

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