Java for Data Science

Examine the techniques and Java tools supporting the growing field of data science

About This Book

• Your entry ticket to the world of data science with the stability and power of Java
• Explore, analyse, and visualize your data effectively using easy-to-follow examples
• Make your Java applications more capable using machine learning

Who This Book Is For

This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful.

What You Will Learn

• Understand the nature and key concepts used in the field of data science
• Grasp how data is collected, cleaned, and processed
• Become comfortable with key data analysis techniques
• See specialized analysis techniques centered on machine learning
• Master the effective visualization of your data
• Work with the Java APIs and techniques used to perform data analysis

In Detail

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 book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application.

The book 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. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation.

The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book.

Style and approach

This book follows a tutorial approach, providing examples of each of the major concepts covered.

With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

本書介紹了支援日益增長的數據科學領域的技術和Java工具。

關於本書：
- 以Java的穩定性和強大功能，進入數據科學的世界。
- 通過易於理解的示例，探索、分析和可視化數據。
- 使用機器學習使您的Java應用程序更具能力。

本書適合對Java開發應用程序感到舒適的開發人員。那些想進入數據科學領域或希望構建智能應用程序的人會發現本書非常理想。有志於成為數據科學家的人也會發現本書非常有幫助。

您將學到什麼：
- 理解數據科學領域的性質和關鍵概念。
- 掌握數據的收集、清理和處理。
- 熟悉關鍵數據分析技術。
- 掌握以機器學習為中心的專業分析技術。
- 掌握有效的數據可視化。
- 使用Java API和技術進行數據分析。

詳細內容：
數據科學涉及從各種數據源中提取知識和洞察力，以分析模式或預測未來行為。它涵蓋了統計學、計算機科學、數學、機器學習和數據挖掘等多個學科。本書介紹了重要的數據科學概念以及它們如何在Java中得到支持，並探討了常常具有統計挑戰性的技術，以便讓您理解它們的目的和應用。

本書首先介紹了數據科學，然後介紹了數據收集、數據清理、數據分析和數據可視化等基本數據科學任務。接下來討論了統計技術和更高級的主題，包括機器學習、神經網絡和深度學習。下一部分探討了文本、視覺和音頻數據等主要數據分析類別，然後討論了支持並行實現的資源。

最後一章演示了一個深入的數據科學問題，並提供了一個全面的基於Java的解決方案。由於主題的性質，本書早期提供了技術的簡單示例，然後在書中後期進行了更詳細的介紹。這樣可以更自然地介紹本書中提出的技術和概念。

風格和方法：
本書採用教程方式，提供了每個主要概念的示例。

通過逐步指導的風格，本書涵蓋了數據科學的各個方面，並能夠快速上手。