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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

Chapter 1. Introduction to Data Analysis

Data analysis is the process of organizing, cleaning, transforming, and modeling data to obtain useful information and ultimately, new knowledge. The terms data analytics, business analytics, data mining, artificial intelligence, machine learning, knowledge discovery, and big data are also used to describe similar processes. The distinctions of these fields probably lie more in their areas of application than in their fundamental nature. Some argue that these are all part of the new discipline of data science.

The central process of gaining useful information from organized data is managed by the application of computer science algorithms. Consequently, these will be a central focus of this book.

Data analysis is both an old field and a new one. Its origins lie among the mathematical fields of numerical methods and statistical analysis, which reach back into the eighteenth century. But many of the methods that we shall study gained prominence much more recently, with the ubiquitous force of the internet and the consequent availability of massive datasets.

In this first chapter, we look at a few famous historical examples of data analysis. These can help us appreciate the importance of the science and its promise for the future.

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