Book description
Data collection, processing, analysis, and more
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 easytofollow examples
 A highly practical course covering a broad set of topics  from the basics of Machine Learning to Deep Learning and Big Data frameworks.
Who This Book Is For
This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you!
What You Will Learn
 Understand the key concepts of data science
 Explore the data science ecosystem available in Java
 Work with the Java APIs and techniques used to perform efficient data analysis
 Find out how to approach different machine learning problems with Java
 Process unstructured information such as natural language text or images, and create your own search
 Learn how to build deep neural networks with DeepLearning4j
 Build data science applications that scale and process large amounts of data
 Deploy data science models to production and evaluate their performance
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 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, Javabased 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
Style and approach
This course follows a tutorial approach, providing examples of each of the concepts covered. With a stepbystep instructional style, this book covers various facets of data science and will get you up and running quickly.
Publisher resources
Table of contents
 Preface
 Module 1

Getting Started with Data Science
 Problems solved using data science
 Understanding the data science problem  solving approach
 Acquiring data for an application
 The importance and process of cleaning data
 Visualizing data to enhance understanding
 The use of statistical methods in data science
 Machine learning applied to data science
 Using neural networks in data science
 Deep learning approaches
 Performing text analysis
 Visual and audio analysis
 Improving application performance using parallel techniques
 Assembling the pieces
 Summary
 Data Acquisition
 Data Cleaning
 Data Visualization
 Statistical Data Analysis Techniques
 Machine Learning
 Neural Networks
 Deep Learning
 Text Analysis
 Visual and Audio Analysis
 Mathematical and Parallel Techniques for Data Analysis
 Bringing It All Together
 Module 2
 Data Science Using Java
 Data Processing Toolbox
 Exploratory Data Analysis
 Supervised Learning  Classification and Regression
 Unsupervised Learning  Clustering and Dimensionality Reduction
 Working with Text  Natural Language Processing and Information Retrieval
 Extreme Gradient Boosting
 Deep Learning with DeepLearning4J
 Scaling Data Science
 Deploying Data Science Models
 Bibliography
Product information
 Title: Java: Data Science Made Easy
 Author(s):
 Release date: July 2017
 Publisher(s): Packt Publishing
 ISBN: 9781788475655
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