Book Image

Java for Data Science

By : Richard M. Reese, Jennifer L. Reese
Book Image

Java for Data Science

By: Richard M. Reese, Jennifer L. Reese

Overview of this book

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions
Table of Contents (19 chapters)
Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Summary


In this chapter, we provided a brief introduction to the basic statistical analysis techniques you may encounter in data science applications. We started with simple techniques to calculate the mean, median, and mode of a set of numerical data. Both standard Java and third-party Java APIs were used to show how these attributes are calculated. While these techniques are relatively simple, there are some issues that need to be considered when calculating them.

Next, we examined linear regression. This technique is more predictive in nature and attempts to calculate other values in the future or past based on a sample dataset. We examine both simple linear regression and multiple regression and used Apache Commons classes to perform the regression and JavaFX to draw graphs.

Simple linear regression uses a single independent variable to predict a dependent variable. Multiple regression uses more than one independent variable. Both of these techniques have statistical attributes used to...