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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

Some examples of MapReduce applications


Here are a few examples of big data problems that can be solved with the MapReduce framework:

  1. Given a repository of text files, find the frequency of each word. This is called the WordCount problem.

  2. Given a repository of text files, find the number of words of each word length.

  3. Given two matrices in sparse matrix format, compute their product.

  4. Factor a matrix given in sparse matrix format.

  5. Given a symmetric graph whose nodes represent people and edges represent friendship, compile a list of common friends.

  6. Given a symmetric graph whose nodes represent people and edges represent friendship, compute the average number of friends by age.

  7. Given a repository of weather records, find the annual global minima and maxima by year.

  8. Sort a large list. Note that in most implementations of the MapReduce framework, this problem is trivial, because the framework automatically sorts the output from the map() function.

  9. Reverse a graph.

  10. Find a minimal spanning tree (MST) of a...

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