Java: Data Science Made Easy

Data collection, processing, analysis, and more
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Java: Data Science Made Easy

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

Data collection, processing, analysis, and more
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Book Details

ISBN 139781788475655
Paperback734 pages

Book Description

Data 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 Science

Table of Contents

Chapter 1: Module 1
Chapter 2: 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
Chapter 3: Data Acquisition
Understanding the data formats used in data science applications
Data acquisition techniques
Summary
Chapter 4: Data Cleaning
Handling data formats
The nitty gritty of cleaning text
Cleaning images
Summary
Chapter 5: 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
Chapter 6: Statistical Data Analysis Techniques
Working with mean, mode, and median
Standard deviation
Sample size determination
Hypothesis testing
Regression analysis
Summary
Chapter 7: Machine Learning
Supervised learning techniques
Unsupervised machine learning
Reinforcement learning
Summary
Chapter 8: Neural Networks
Training a neural network
Understanding static neural networks
Understanding dynamic neural networks
Additional network architectures and algorithms
Summary
Chapter 9: Deep Learning
Deeplearning4j architecture
Deep learning and regression analysis
Restricted Boltzmann Machines
Deep autoencoders
Convolutional networks
Recurrent Neural Networks
Summary
Chapter 10: Text Analysis
Implementing named entity recognition
Classifying text
Understanding tagging and POS
Extracting relationships from sentences
Sentiment analysis
Summary
Chapter 11: Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
Chapter 12: Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
Chapter 13: 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
Chapter 14: 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
Chapter 15: Module 2
Chapter 16: Data Science Using Java
Data science
Data science process models
Data science in Java
Summary
Chapter 17: Data Processing Toolbox
Standard Java library
Extensions to the standard library
Accessing data
Search engine - preparing data
Summary
Chapter 18: Exploratory Data Analysis
Exploratory data analysis in Java
Interactive Exploratory Data Analysis in Java
Summary
Chapter 19: Supervised Learning - Classification and Regression
Classification
Case study - page prediction
Regression
Case study - hardware performance
Summary
Chapter 20: Unsupervised Learning - Clustering and Dimensionality Reduction
Dimensionality reduction
Cluster analysis
Summary
Chapter 21: Working with Text - Natural Language Processing and Information Retrieval
Natural Language Processing and information retrieval
Machine learning for texts
Summary
Chapter 22: Extreme Gradient Boosting
Gradient Boosting Machines and XGBoost
XGBoost in practice
Summary
Chapter 23: Deep Learning with DeepLearning4J
Neural Networks and DeepLearning4J
Deep learning for cats versus dogs
Summary
Chapter 24: Scaling Data Science
Apache Hadoop
Apache Spark
Link prediction
Summary
Chapter 25: Deploying Data Science Models
Microservices
Online evaluation
Summary
Chapter 26: Bibliography

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 searc
  • 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 performance

Authors

Table of Contents

Chapter 1: Module 1
Chapter 2: 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
Chapter 3: Data Acquisition
Understanding the data formats used in data science applications
Data acquisition techniques
Summary
Chapter 4: Data Cleaning
Handling data formats
The nitty gritty of cleaning text
Cleaning images
Summary
Chapter 5: 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
Chapter 6: Statistical Data Analysis Techniques
Working with mean, mode, and median
Standard deviation
Sample size determination
Hypothesis testing
Regression analysis
Summary
Chapter 7: Machine Learning
Supervised learning techniques
Unsupervised machine learning
Reinforcement learning
Summary
Chapter 8: Neural Networks
Training a neural network
Understanding static neural networks
Understanding dynamic neural networks
Additional network architectures and algorithms
Summary
Chapter 9: Deep Learning
Deeplearning4j architecture
Deep learning and regression analysis
Restricted Boltzmann Machines
Deep autoencoders
Convolutional networks
Recurrent Neural Networks
Summary
Chapter 10: Text Analysis
Implementing named entity recognition
Classifying text
Understanding tagging and POS
Extracting relationships from sentences
Sentiment analysis
Summary
Chapter 11: Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
Chapter 12: Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
Chapter 13: 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
Chapter 14: 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
Chapter 15: Module 2
Chapter 16: Data Science Using Java
Data science
Data science process models
Data science in Java
Summary
Chapter 17: Data Processing Toolbox
Standard Java library
Extensions to the standard library
Accessing data
Search engine - preparing data
Summary
Chapter 18: Exploratory Data Analysis
Exploratory data analysis in Java
Interactive Exploratory Data Analysis in Java
Summary
Chapter 19: Supervised Learning - Classification and Regression
Classification
Case study - page prediction
Regression
Case study - hardware performance
Summary
Chapter 20: Unsupervised Learning - Clustering and Dimensionality Reduction
Dimensionality reduction
Cluster analysis
Summary
Chapter 21: Working with Text - Natural Language Processing and Information Retrieval
Natural Language Processing and information retrieval
Machine learning for texts
Summary
Chapter 22: Extreme Gradient Boosting
Gradient Boosting Machines and XGBoost
XGBoost in practice
Summary
Chapter 23: Deep Learning with DeepLearning4J
Neural Networks and DeepLearning4J
Deep learning for cats versus dogs
Summary
Chapter 24: Scaling Data Science
Apache Hadoop
Apache Spark
Link prediction
Summary
Chapter 25: Deploying Data Science Models
Microservices
Online evaluation
Summary
Chapter 26: Bibliography

Book Details

ISBN 139781788475655
Paperback734 pages
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