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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

Random sampling

A random sample of a dataset is a subset whose elements are randomly selected. The given dataset is called the population and is usually very large; for example, all male Americans aged 25-35 years.

In a simulation, selecting a random sample is straightforward, using a random number generator. But in a real world context, random sampling is nontrivial.

Random sampling is an important part of quality control in nearly all types of manufacturing, and it is an essential aspect of polling in the social sciences. In industrial sectors such as pharmaceuticals, random sampling is critical to both production and testing.

To understand the principles of random sampling, we must take a brief detour into the field of mathematical probability theory. This requires a few technical definitions.

A random experiment is a process, real or imagined, that has a specified set of possible outcomes, any one of which could result from the experiments. The set S of all possible outcomes is called...

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