Result: Java for Data Science

Title:
Java for Data Science
Publisher Information:
Packt Publishing 2017
Document Type:
Electronic Resource Electronic Resource
Index Terms:
Availability:
Open access content. Open access content
copyrighted
Note:
English
Contributing Source:
CYBERLIBRIS
From OAIsterĀ®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1268801281
Database:
OAIster

Further Information

Examine the techniques and Java tools supporting the growing field of data scienceAbout This BookYour entry ticket to the world of data science with the stability and power of JavaExplore, analyse, and visualize your data effectively using easy-to-follow examplesMake your Java applications more capable using machine learningWho This Book Is ForThis 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 LearnUnderstand the nature and key concepts used in the field of data scienceGrasp how data is collected, cleaned, and processedBecome comfortable with key data analysis techniquesSee specialized analysis techniques centered on machine learningMaster the effective visualization of your dataWork with the Java APIs and techniques used to perform data analysisIn DetailData 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.