Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

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.

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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Data Science Using Java
Data Science Using Java
Data science
Data science process models
Data science in Java
Summary
2
Data Processing Toolbox
Data Processing Toolbox
Standard Java library
Extensions to the standard library
Accessing data
Search engine - preparing data
Summary
3
Exploratory Data Analysis
Exploratory Data Analysis
Exploratory data analysis in Java
Interactive Exploratory Data Analysis in Java
Summary
4
Supervised Learning - Classification and Regression
Supervised Learning - Classification and Regression
Classification
Case study - page prediction
Regression
Case study - hardware performance
Summary
5
Unsupervised Learning - Clustering and Dimensionality Reduction
Unsupervised Learning - Clustering and Dimensionality Reduction
Dimensionality reduction
Cluster analysis
Summary
6
Working with Text - Natural Language Processing and Information Retrieval
Working with Text - Natural Language Processing and Information Retrieval
Natural Language Processing and information retrieval
Machine learning for texts
Summary
7
Extreme Gradient Boosting
Extreme Gradient Boosting
Gradient Boosting Machines and XGBoost
XGBoost in practice
Summary
8
Deep Learning with DeepLearning4J
Deep Learning with DeepLearning4J
Neural Networks and DeepLearning4J
Deep learning for cats versus dogs
Summary
9
Scaling Data Science
Scaling Data Science
Apache Hadoop
Apache Spark
Link prediction
Summary
10
Deploying Data Science Models
Deploying Data Science Models
Microservices
Online evaluation
Summary

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 cumbersome. Luckily, there are nice libraries for that purpose, and one of them is Apache Commons CSV:

<dependency> 
  <groupId>org.apache.commons</groupId> 
  <artifactId>commons-csv</artifactId> 
  ...
Previous Section
End of Section 4
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