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Books > Computing & IT > General theory of computing
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Java for Data Science (Paperback)
Loot Price: R1,426
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Java for Data Science (Paperback)
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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.
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