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

Data science


Data science is the discipline of extracting actionable knowledge from data of various forms. The name data science emerged quite recently--it was invented by DJ Patil and Jeff Hammerbacher and popularized in the article Data Scientist: The Sexiest Job of the 21st Century in 2012. But the discipline itself had existed before for quite a while and previously was known by other names such as data mining or predictive analytics. Data science, like its predecessors, is built on statistics and machine learning algorithms for knowledge extraction and model building.

The science part of the term data science is no coincidence--if we look up science, its definition can be summarized to systematic organization of knowledge in terms testable explanations and predictions. This is exactly what data scientists do, by extracting patterns from available data, they can make predictions about future unseen data, and they make sure the predictions are validated beforehand. 

Nowadays, data science...