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Mastering Java for Data Science
Mastering Java for Data Science

Mastering Java for Data Science: Analytics and more for production-ready applications

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Profile Icon Alexey Grigorev
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Paperback Apr 2017 364 pages 1st Edition
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Arrow left icon
Profile Icon Alexey Grigorev
Arrow right icon
Mex$1128.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Apr 2017 364 pages 1st Edition
eBook
Mex$631.99 Mex$902.99
Paperback
Mex$1128.99
Subscription
Free Trial
eBook
Mex$631.99 Mex$902.99
Paperback
Mex$1128.99
Subscription
Free Trial

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Mastering Java for Data Science

Data Processing Toolbox

In the previous chapter, we discussed the best practices for approaching data science problems. We looked at CRISP-DM, which is the methodology for dealing with data mining projects, and one of the first steps there is data preprocessing. In this chapter, we will take a closer look at how to do this in Java.

Specifically, we will cover the following topics:

  • Standard Java library
  • Extensions to the standard library
  • Reading data from different sources such as text, HTML, JSON, and databases
  • DataFrames for manipulating tabular data

In the end, we will put everything together to prepare the data for the search engine.

By the end of this chapter, you will be able to process data such that it can be used for machine learning and further analysis.

Standard Java library

The standard Java library is very rich and offers a lot of tools for data manipulation, including:

  • Collections for organizing data in memory
  • I/O for reading and writing data
  • Streaming APIs for making data transformations easy

In this chapter, we will look at all these tools in detail. 

Collections

Data is the most important part of data science. When dealing with data, it needs to be efficiently stored and processed, and for this we use data structures. A data structure describes a way to store data efficiently to solve a specific problem, and the Java Collection API is the standard Java API for data structures. This API offers a wide variety of implementations that are useful in practical data science applications.

We will not describe...

Extensions to the standard library

The standard Java library is quite powerful, but some things take a long time to write using it or they are simply missing. There are a number of extensions to the standard library, and the most prominent libraries are Apache Commons (a collection of libraries) and Google Guava. They make it easier to use the standard API or extend it, for example, by adding new collections or implementations.

To begin with, we will briefly go over the most relevant parts of these libraries, and later on we will see how they are used in practice.

Apache Commons

Apache Commons is a collection of open source libraries for Java, with the goal of creating reusable Java components. There are quite a few of them, including Apache Commons Lang, Apache...

Accessing data

By now we already have spent a lot of time describing how to read and write data. But there is much more to that: data often comes in different formats such as CSV, HTML, or JSON or it can be stored in a database. Knowing how to access and process this data is important for Data Science and now we will describe in detail how to do it for the most common data formats and sources.

Text data and CSV

We already have spoken about reading text data in great detail, and it can be done, for example, using the Files helper class from the NIO API or IOUtils from Commons IO.

CSV (Comma Separated Values) is a common way to organize tabular data in plain text files. While it is possible to parse CSV files by hand, there are some corner cases, which make it a bit...

Search engine - preparing data

In the first chapter, we introduced the running example, building a search engine. A search engine is a program that, given a query from the user, returns results ordered by relevance with respect to the query. In this chapter, we will perform the first steps--obtaining and processing data.

Suppose we are working on a web portal where users generate a lot of content, but they have trouble finding what other people have created. To overcome this problem, we propose to build a search engine, and product management has identified the typical queries that the users will put in.

For example, "Chinese food", "homemade pizza", and "how to learn programming" are typical queries from this list.

Now we need to collect the data. Luckily for us, there are already search engines on the Internet that can take in a query and return a list of URLs they consider relevant...

Standard Java library


The standard Java library is very rich and offers a lot of tools for data manipulation, including:

  • Collections for organizing data in memory
  • I/O for reading and writing data
  • Streaming APIs for making data transformations easy

In this chapter, we will look at all these tools in detail. 

Collections

Data is the most important part of data science. When dealing with data, it needs to be efficiently stored and processed, and for this we use data structures. A data structure describes a way to store data efficiently to solve a specific problem, and the Java Collection API is the standard Java API for data structures. This API offers a wide variety of implementations that are useful in practical data science applications.

We will not describe the collection API in full detail, but concentrate on the most useful and important ones--list, set, and map interfaces.

Lists are collections where each element can be accessed by its index. The g0-to implementation of the List interface is...

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Key benefits

  • An overview of modern Data Science and Machine Learning libraries available in Java
  • Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks.
  • Easy-to-follow illustrations and the running example of building a search engine.

Description

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.

Who is this book for?

This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you!

What you will learn

  • Get a solid understanding of the data processing toolbox available in Java
  • Explore the data science ecosystem available in Java
  • Find out how to approach different machine learning problems with Java
  • Process unstructured information such as natural language text
  • or images
  • Create your own search engine
  • Get state-of-the-art performance with XGBoost
  • Learn how to build deep neural networks with DeepLearning4j
  • Build applications that scale and process large amounts of data
  • Deploy data science models to production and evaluate their
  • performance
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Length: 364 pages
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Length: 364 pages
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Table of Contents

10 Chapters
Data Science Using Java Chevron down icon Chevron up icon
Data Processing Toolbox Chevron down icon Chevron up icon
Exploratory Data Analysis Chevron down icon Chevron up icon
Supervised Learning - Classification and Regression Chevron down icon Chevron up icon
Unsupervised Learning - Clustering and Dimensionality Reduction Chevron down icon Chevron up icon
Working with Text - Natural Language Processing and Information Retrieval Chevron down icon Chevron up icon
Extreme Gradient Boosting Chevron down icon Chevron up icon
Deep Learning with DeepLearning4J Chevron down icon Chevron up icon
Scaling Data Science Chevron down icon Chevron up icon
Deploying Data Science Models Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Full star icon 5
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1 star 0%
Luca Massaron May 07, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Disclaimer: I am one of the technical reviewers of this book and an author of other data science and machine learning books.I strongly recommend reading this book in order to have a complete well-rounded overview of what it means doing data science in a company environment today. Commonly we think of data science as just the series of data preparation, experimentation and model development that can lead to a PoC, a Proof of Concept. Clearly, such can be easily done in Python or R in a successful way, but to discover soon after that the project has to be completely rewritten and rethought, if it has to go beyond the PoC status and become an integrated part of the software of company that sustains its competitive advantage in the market.Alexey clearly describes what Java can do for your data science projects, allowing you to go into production with your data science projects leveraging an existing Java ecosystem, which is common to so many small and large companies around the World. Most IT people consider Java as solid, mature, trusted, and reliable and it is (most likely) already been applied to existing ETL processing on the data you are using for your projects. Java can really be the key allowing your data science projects to go smoothly and successfully into production.Moreover, Alexey illustrates in the book many original interesting examples, uncommon to many other books around (no, really no Iris dataset here) and it presents so many fabulous tricks he clearly derived from his many years of practice in data science, that the book is a gem in itself. I found myself learning something new each chapter I read :-)
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