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

Random variables


To understand statistical data analysis, we must first understand the concept of a random variable.

A random variable is a function that assigns a number to each element of a sample space. By converting symbolic outcomes such as HTTH, they allow for simpler mathematical analysis of the data.

The coin example illustrates this. The sample space S1 has 16 outcomes. For each outcome x, let X(x) be the number of heads in that outcome. For example, X(HHTH) = 3, and X(TTTT) = 0. This is the same as the transformation of S1 into S2 in the previous discussion. Here, we are using the random variable X for the same purpose. Now we can translate statements about the probability functions p and P into statements about purely numerical functions.