Java: Data Science Made Easy
eBook - ePub

Java: Data Science Made Easy

Richard M. Reese, Jennifer L. Reese, Alexey Grigorev

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  1. 715 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Java: Data Science Made Easy

Richard M. Reese, Jennifer L. Reese, Alexey Grigorev

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About This Book

Data collection, processing, analysis, and moreAbout 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 ForThis 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 performanceIn 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 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 ScienceStyle and approachThis 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.

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Information

Publisher
Packt Publishing
Year
2017
ISBN
9781788479189
Edition
1
Topic
Computer Science
Subtopic
Data Processing
Index
Computer Science

Module 1

Java for Data Science
Examine the techniques and Java tools supporting the growing field of data science

Getting Started with Data Science

Data science is not a single science as much as it is a collection of various scientific disciplines integrated for the purpose of analyzing data. These disciplines include various statistical and mathematical techniques, including:
  • Computer science
  • Data engineering
  • Visualization
  • Domain-specific knowledge and approaches
With the advent of cheaper storage technology, more and more data has been collected and stored permitting previously unfeasible processing and analysis of data. With this analysis came the need for various techniques to make sense of the data. These large sets of data, when used to analyze data and identify trends and patterns, become known as big data.
This in turn gave rise to cloud computing and concurrent techniques such as map-reduce, which distributed the analysis process across a large number of processors, taking advantage of the power of parallel processing.
The process of analyzing big data is not simple and evolves to the specialization of developers who were known as data scientists. Drawing upon a myriad of technologies and expertise, they are able to analyze data to solve problems that previously were either not envisioned or were too difficult to solve.
Early big data applications were typified by the emergence of search engines capable of more powerful and accurate searches than their predecessors. For example, AltaVista was an early popular search engine that was eventually superseded by Google. While big data applications were not limited to these search engine functionalities, these applications laid the groundwork for future work in big data.
The term, data science, has been used since 1974 and evolved over time to include statistical analysis of data. The concepts of data mining and data analytics have been associated with data science. Around 2008, the term data scientist appeared and was used to describe a person who performs data analysis. A more in-depth discussion of the history of data science can be found at http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#3d9ea08369fd.
This book aims to take a broad look at data science using Java and will briefly touch on many topics. It is likely that the reader may find topics of interest and pursue these at greater depth independently. The purpose of this book, however, is simply to introduce the reader to the significant data science topics and to illustrate how they can be addressed using Java.
There are many algorithms used in data science. In this book, we do not attempt to explain how they work except at an introductory level. Rather, we are more interested in explaining how they can be used to solve problems. Specifically, we are interested in knowing how they can be used with Java.

Problems solved using data science

The various data science techniques that we will illustrate have been used to solve a variety of problems. Many of these techniques are motivated to achieve some economic gain, but they have also been used to solve many pressing social and environmental problems. Problem domains where these techniques have been used include finance, optimizing business processes, understanding customer needs, performing DNA analysis, foiling terrorist plots, and finding relationships between transactions to detect fraud, among many other data-intensive problems.
Data mining is a popular application area for data science. In this activity, large quantities of data are processed and analyzed to glean information about the dataset, to provide meaningful insights, and to develop meaningful conclusions and predictions. It has been used to analyze customer behavior, detecting relationships between what may appear to be unrelated events, and to make predictions about future behavior.
Machine learning is an important aspect of data science. This technique allows the computer to solve various problems without needing to be explicitly programmed. It has been used in self-driving cars, speech recognition, and in web searches. In data mining, the data is extracted and processed. With machine learning, computers use the data to take some sort of action.

Understanding the data science problem - solving approach

Data science is concerned with the processing and analysis of large quantities of data to create models that can be used to make predictions or otherwise support a specific goal. This process often involves the building and training of models. The specific approach to solve a problem is dependent on the nature of the problem. However, in general, the following are the high-level tasks that are used in the analysis process:
  • Acquiring the data: Before we can process the data, it must be acquired. The data is frequently stored in a variety of formats and will come from a wide range of data sources.
  • Cleaning the data: Once the data has been acquired, it often needs to be converted to a different format before it can be used. In addition, the data needs to be processed, or cleaned, so as to remove errors, resolve inconsistencies, and otherwise put it in a form ready for analysis.
  • Analyzing the data: This can be performed using a number of techniques including:
    • Statistical analysis: This uses a multitude of statistical approaches to provide insight into data. It includes simple techniques and more advanced techniques such as regression analysis.
    • AI analysis: These can be grouped as machine learning, neural networks, and deep learning techniques:
      • Machine learning approaches are characterized by programs that can learn without being specifically programmed to complete a specific task
      • Neural networks are built around models patterned after the neural connection of the brain
      • Deep learning attempts to identify higher levels of abstraction within a set of data
    • Text analysis: This is a common form of analysis, which works with natural languages to identify features such as the names of people and places, the relationship between parts of text, and the implied meaning of text.
    • Data visualization: This is an important analysis tool. By displaying the data in a visual form, a hard-to-understand set of numbers can be more readily understood.
    • Video, image, and audio processing and analysis: This is a more specialized form of analysis, which is becoming more commo...

Table of contents