book

Java for Data Science

Name: Java for Data Science
ISBN: 9781785280115

by Richard M. Reese, Shilpi Saxena, Jennifer L. Reese

January 2017

Beginner to intermediate

386 pages

7h 57m

English

Packt Publishing

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Java for Data Science
Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
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Preface
What this book covers
What you need for this book
Who this book is for

Conventions
Reader feedback
Customer support
Downloading the example codeErrataPiracyQuestions
1. Getting Started with Data Science
Problems solved using data science
Understanding the data science problem - solving approach
Using Java to support data science
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
Understanding the data formats used in data science applicationsOverview of CSV dataOverview of spreadsheetsOverview of databasesOverview of PDF filesOverview of JSONOverview of XMLOverview of streaming dataOverview of audio/video/images in Java
Data acquisition techniques
Using the HttpUrlConnection classWeb crawlers in JavaCreating your own web crawlerUsing the crawler4j web crawlerWeb scraping in JavaUsing API calls to access common social media sitesUsing OAuth to authenticate usersHanding TwitterHandling WikipediaHandling FlickrHandling YouTubeSearching by keyword
Summary
3. Data Cleaning
Handling data formatsHandling CSV dataHandling spreadsheetsHandling Excel spreadsheetsHandling PDF filesHandling JSONUsing JSON streaming APIUsing the JSON tree API
The nitty gritty of cleaning text
Using Java tokenizers to extract wordsJava core tokenizersThird-party tokenizers and librariesTransforming data into a usable formSimple text cleaningRemoving stop wordsFinding words in textFinding and replacing textData imputationSubsetting dataSorting textData validationValidating data typesValidating datesValidating e-mail addressesValidating ZIP codesValidating names
Cleaning images
Changing the contrast of an imageSmoothing an imageBrightening an imageResizing an imageConverting images to different formats
Summary
4. Data Visualization
Understanding plots and graphsVisual analysis goals
Creating index charts
Creating bar charts
Using country as the categoryUsing decade as the category
Creating stacked graphs
Creating pie charts
Creating scatter charts
Creating histograms
Creating donut charts
Creating bubble charts
Summary
5. Statistical Data Analysis Techniques
Working with mean, mode, and medianCalculating the meanUsing simple Java techniques to find meanUsing Java 8 techniques to find meanUsing Google Guava to find meanUsing Apache Commons to find meanCalculating the medianUsing simple Java techniques to find medianUsing Apache Commons to find the medianCalculating the modeUsing ArrayLists to find multiple modesUsing a HashMap to find multiple modesUsing a Apache Commons to find multiple modes
Standard deviation
Sample size determination
Hypothesis testing
Regression analysis
Using simple linear regressionUsing multiple regression
Summary
6. Machine Learning
Supervised learning techniquesDecision treesDecision tree typesDecision tree librariesUsing a decision tree with a book datasetTesting the book decision treeSupport vector machinesUsing an SVM for camping dataTesting individual instancesBayesian networksUsing a Bayesian network
Unsupervised machine learning
Association rule learningUsing association rule learning to find buying relationships
Reinforcement learning
Summary
7. Neural Networks
Training a neural networkGetting started with neural network architectures
Understanding static neural networks
A basic Java example
Understanding dynamic neural networks
Multilayer perceptron networksBuilding the modelEvaluating the modelPredicting other valuesSaving and retrieving the modelLearning vector quantizationSelf-Organizing MapsUsing a SOMDisplaying the SOM results
Additional network architectures and algorithms
The k-Nearest Neighbors algorithmInstantaneously trained networksSpiking neural networksCascading neural networksHolographic associative memoryBackpropagation and neural networks
Summary
8. Deep Learning
Deeplearning4j architectureAcquiring and manipulating dataReading in a CSV fileConfiguring and building a modelUsing hyperparameters in ND4JInstantiating the network modelTraining a modelTesting a model
Deep learning and regression analysis
Preparing the dataSetting up the classReading and preparing the dataBuilding the modelEvaluating the model
Restricted Boltzmann Machines
Reconstruction in an RBMConfiguring an RBM
Deep autoencoders
Building an autoencoder in DL4JConfiguring the networkBuilding and training the networkSaving and retrieving a networkSpecialized autoencoders
Convolutional networks
Building the modelEvaluating the model
Recurrent Neural Networks
Summary
9. Text Analysis
Implementing named entity recognitionUsing OpenNLP to perform NERIdentifying location entities
Classifying text
Word2Vec and Doc2VecClassifying text by labelsClassifying text by similarity
Understanding tagging and POS
Using OpenNLP to identify POSUnderstanding POS tags
Extracting relationships from sentences
Using OpenNLP to extract relationships
Sentiment analysis
Downloading and extracting the Word2Vec modelBuilding our model and classifying text
Summary
10. Visual and Audio Analysis
Text-to-speechUsing FreeTTSGetting information about voicesGathering voice information
Understanding speech recognition
Using CMUPhinx to convert speech to textObtaining more detail about the words
Extracting text from an image
Using Tess4j to extract text
Identifying faces
Using OpenCV to detect faces
Classifying visual data
Creating a Neuroph Studio project for classifying visual imagesTraining the model
Summary
11. Mathematical and Parallel Techniques for Data Analysis
Implementing basic matrix operationsUsing GPUs with DeepLearning4j
Using map-reduce
Using Apache's Hadoop to perform map-reduceWriting the map methodWriting the reduce methodCreating and executing a new Hadoop job
Various mathematical libraries
Using the jblas APIUsing the Apache Commons math APIUsing the ND4J API
Using OpenCL
Using Aparapi
Creating an Aparapi applicationUsing Aparapi for matrix multiplication
Using Java 8 streams
Understanding Java 8 lambda expressions and streamsUsing Java 8 to perform matrix multiplicationUsing Java 8 to perform map-reduce
Summary
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
Extracting data for a sentiment analysis modelBuilding the sentiment modelProcessing the JSON inputCleaning data to improve our resultsRemoving stop wordsPerforming sentiment analysisAnalysing the results
Other optional enhancements
Summary

Overview

Java for Data Science is a comprehensive guide that combines the power and stability of Java with modern data science techniques. Throughout this book, you'll explore the Java tools and libraries needed to acquire, clean, and analyze data. By following its clear, step-by-step approach, you'll gain the skills to build your own intelligent applications.

What this Book will help me do

Understand the core principles of data science and how Java supports these techniques.
Gain expertise in data acquisition, cleaning, visualization, and analysis using Java libraries.
Apply machine learning and neural network concepts to tackle a variety of data science challenges.
Develop skills in performing advanced statistical analysis and data mining with Java tools.
Learn how to build and deploy Java-based solutions for big data along with practical examples.

Author(s)

Richard M. Reese is a seasoned software developer with years of experience in Java programming. Together with co-author None Reese, they provide pragmatic, step-by-step instructions designed to make complex topics approachable. Their focus is on equipping readers with practical tools and real-world solutions through thoughtful, clear, and readable examples.

Who is it for?

This book is ideal for experienced Java developers who are looking to expand their skills into the domain of data science. If you're a beginner in data science, the step-by-step instructional style will enable you to learn quickly while leveraging your Java knowledge. This book is also suitable for students learning Java programming who want to understand its applications in analytics and machine learning. If your goal is to create innovative applications with data-driven insights using Java, this book is for you.

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Publisher Resources

ISBN: 9781785280115

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