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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

The scientific method

On November 11, 1572, a young Danish nobleman named Tycho Brahe observed the supernova of a star that we now call SN 1572. From that time until his death 30 years later, he devoted his wealth and energies to the accumulation of astronomical data. His young German assistant, Johannes Kepler, spent 18 years analyzing that data before he finally formulated his three laws of planetary motion in 1618.

The scientific method

Figure 1 Kepler

Historians of science usually attribute Kepler's achievement as the beginning of the Scientific Revolution. Here were the essential steps of the scientific method: observe nature, collect the data, analyze the data, formulate a theory, and then test that theory with more data. Note the central step here: data analysis.

Of course, Kepler did not have either of the modern tools that data analysts use today: algorithms and computers on which to implement them. He did, however, apply one technological breakthrough that surely facilitated his number crunching: logarithms. In 1620, he stated that Napier's invention of logarithms in 1614 had been essential to his discovery of the third law of planetary motion.

Kepler's achievements had a profound effect upon Galileo Galilei a generation later, and upon Isaac Newton a generation after him. Both men practiced the scientific method with spectacular success.

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