Java: Data Science Made Easy

Java: Data Science Made Easy

by Richard M. Reese and Jennifer L. Reese
English | 2017 | ISBN: 1788475658 | 715 Pages | True PDF | 9 MB

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

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 Scienc


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