Java: Data Science Made Easy

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 easy-to-follow 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 searc
• 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, 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.

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

Table of Contents

Module 1
Getting Started with Data Science
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