Book Image

Java: Data Science Made Easy

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

Java: Data Science Made Easy

By: Richard M. Reese, Jennifer L. Reese, Alexey Grigorev

Overview of this book

Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings. By the end of this course, you will be up and running with various facets of data science using Java, in no time at all. This course contains premium content from two of our recently published popular titles: - Java for Data Science - Mastering Java for Data Science
Table of Contents (29 chapters)
Title Page
Credits
Preface
Free Chapter
1
Module 1
15
Module 2
26
Bibliography

Exploratory data analysis in Java


Exploratory Data Analysis is about taking a dataset and extracting the most important information from it, in such a way that it is possible to get an idea of what the data looks like. This includes two main parts:

The summarization step is very helpful for understanding data. For numerical variables, in this step we calculate the most important sample statistics: 

  • The extremes (the minimal and the maximal values)
  • The mean value, or the sample average
  • The standard deviation, which describes the spread of the data

Often we consider other statistics, such as the median and the quartiles (25% and 75%).

As we have already seen in the previous chapter, Java offers a great set of tools for data preparation. The same set of tools can be used for EDA, and especially for creating summaries.

Search engine datasets

In this chapter, we will use our running example--building a search engine. In Chapter 2, Data Processing Toolbox, we extracted some data from HTML pages...