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Java: Data Science Made Easy

Java: Data Science Made Easy

Author: Richard M. Reese and Jennifer L. Reese

Publisher: Packt Publishing


Publish Date: July 7, 2017

ISBN-10: 1788475658

Pages: 734

File Type: PDF

Language: English

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Book Preface

Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyze patterns or predict future behavior. 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.

What this learning path covers

Module 1, Java for data science, this module takes an expansive yet cursory approach to various aspects of data science. A brief introduction to the field is presented in the first chapter. Subsequent chapters cover significant aspects of data science, such as data cleaning and the application of neural networks. The last chapter combines topics discussed throughout the book to create a comprehensive data science application.
Module 2, Mastering Java for data science, in this module we will see how we can utilize Java’s toolbox for processing small and large datasets, then look into doing initial exploration data analysis.

Next, we will review the Java libraries that implement common Machine Learning models for classification, regression, clustering, and dimensionality reduction problems. Then we will get into more advanced techniques and discuss Information Retrieval and Natural Language Processing, XGBoost, deep learning, and large scale tools for processing big datasets such as Apache Hadoop and Apache Spark. Finally, we will also have a look at how to evaluate and deploy the produced models such that the other services can use them.

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