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

Chapter 3. Data Acquisition

It is never much fun to work with code that is not formatted properly or uses variable names that do not convey their intended purpose. The same can be said of data, except that bad data can result in inaccurate results. Thus, data acquisition is an important step in the analysis of data. Data is available from a number of sources but must be retrieved and ultimately processed before it can be useful. It is available from a variety of sources. We can find it in numerous public data sources as simple files, or it may be found in more complex forms across the Internet. In this chapter, we will demonstrate how to acquire data from several of these, including various Internet sites and several social media sites.

We can access data from the Internet by downloading specific files or through a process known as web scraping, which involves extracting the contents of a web page. We also explore a related topic known as web crawling, which involves applications that examine...