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Java for Data Science

By : Richard M. Reese, Reese

Java for Data Science

By: Richard M. Reese, Reese

Overview of this book

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with Data Science

1. Getting Started with Data Science

Problems solved using data science

Understanding the data science problem - solving approach

Acquiring data for an application

The importance and process of cleaning data

Visualizing data to enhance understanding

The use of statistical methods in data science

Machine learning applied to data science

Using neural networks in data science

Deep learning approaches

Performing text analysis

Visual and audio analysis

Improving application performance using parallel techniques

Assembling the pieces

Summary

2. Data Acquisition

2. Data Acquisition

Understanding the data formats used in data science applications

Data acquisition techniques

Summary

3. Data Cleaning

3. Data Cleaning

Handling data formats

The nitty gritty of cleaning text

Cleaning images

Summary

4. Data Visualization

4. Data Visualization

Understanding plots and graphs

Creating index charts

Creating bar charts

Creating stacked graphs

Creating pie charts

Creating scatter charts

Creating histograms

Creating donut charts

Creating bubble charts

Summary

5. Statistical Data Analysis Techniques

5. Statistical Data Analysis Techniques

Working with mean, mode, and median

Standard deviation

Sample size determination

Hypothesis testing

Regression analysis

Summary

6. Machine Learning

6. Machine Learning

Supervised learning techniques

Unsupervised machine learning

Reinforcement learning

Summary

7. Neural Networks

7. Neural Networks

Training a neural network

Understanding static neural networks

Understanding dynamic neural networks

Additional network architectures and algorithms

Summary

8. Deep Learning

8. Deep Learning

Deeplearning4j architecture

Deep learning and regression analysis

Restricted Boltzmann Machines

Deep autoencoders

Convolutional networks

Recurrent Neural Networks

Summary

9. Text Analysis

9. Text Analysis

Implementing named entity recognition

Classifying text

Understanding tagging and POS

Extracting relationships from sentences

Sentiment analysis

Summary

10. Visual and Audio Analysis

10. Visual and Audio Analysis

Text-to-speech

Understanding speech recognition

Extracting text from an image

Identifying faces

Classifying visual data

Summary

11. Mathematical and Parallel Techniques for Data Analysis

11. Mathematical and Parallel Techniques for Data Analysis

Implementing basic matrix operations

Using map-reduce

Various mathematical libraries

Using OpenCL

Using Aparapi

Using Java 8 streams

Summary

12. Bringing It All Together

12. Bringing It All Together

Defining the purpose and scope of our application

Understanding the application's architecture

Data acquisition using Twitter

Understanding the TweetHandler class

Other optional enhancements

Summary

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Creating pie charts

The following pie chart example is based on the 2000 population of selected European countries as summarized here:

Country	Population	Percentage
Belgium	10,263,618	3
France	61,137,000	26
Germany	82,187,909	35
Netherlands	15,907,853	7
Sweden	8,872,000	4
United Kingdom	59,522,468	25

The JavaFX implementation uses the same Application base class and main method as used in the previous examples. We will not use a separate method for creating the GUI, but instead place this code in the start method, as shown here:

public class PieChartSample extends Application { 
 
    public void start(Stage stage) { 
        Scene scene = new Scene(new Group()); 
        stage.setTitle("Europian Country Population"); 
        stage.setWidth(500); 
        stage.setHeight(500); 
        ... 
    } 
 
    public static void main(String[] args) { 
        launch(args); 
    } 
     
}

A pie chart...

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