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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

Conditional probability


A conditional probability is one that is computed under the assumption that some related event is known to have occurred. For example, in the marble experiment, if we know that the first marble drawn was red, then the probability that the second marble is green is 4/5 = 80%. This is a conditional probability, the condition being that the first marble was red. It is written as:

(The vertical bar symbol | is read as "given").

On the other hand, the (unconditional) probability that the second marble is green is:

It should seem sensible that the probability that the second marble is green is greater (80%) after having removed a red marble. It should also seem sensible that the unconditional probability of green on the second (67%) is the same as the probability that the first is green (4/6).

The general formula for the conditional probability of an event F, given an event E, is

The symbolism E ∩ F means "E and F".

To apply this formula to our marbles example, let E be the...

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Java Data Analysis
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