Book Image

Java: Data Science Made Easy

By : Richard M. Reese, Jennifer L. Reese, Alexey Grigorev
Book Image

Java: Data Science Made Easy

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

Overview of this book

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

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Java: Data Science Made Easy
Richard M. Reese | Jennifer L. Reese | Alexey Grigorev
Jul, 2017 | 12 hours 14 minutes
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Table of Contents (29 chapters)
Title Page
Title Page
Credits
Credits
Preface
Preface
Free Chapter
1
Module 1
Module 1
2
Getting Started with Data Science
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
3
Data Acquisition
Data Acquisition
Understanding the data formats used in data science applications
Data acquisition techniques
Summary
4
Data Cleaning
Data Cleaning
Handling data formats
The nitty gritty of cleaning text
Cleaning images
Summary
5
Data Visualization
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
6
Statistical Data Analysis Techniques
Statistical Data Analysis Techniques
Working with mean, mode, and median
Standard deviation
Sample size determination
Hypothesis testing
Regression analysis
Summary
7
Machine Learning
Machine Learning
Supervised learning techniques
Unsupervised machine learning
Reinforcement learning
Summary
8
Neural Networks
Neural Networks
Training a neural network
Understanding static neural networks
Understanding dynamic neural networks
Additional network architectures and algorithms
Summary
9
Deep Learning
Deep Learning
Deeplearning4j architecture
Deep learning and regression analysis
Restricted Boltzmann Machines
Deep autoencoders
Convolutional networks
Recurrent Neural Networks
Summary
10
Text Analysis
Text Analysis
Implementing named entity recognition
Classifying text
Understanding tagging and POS
Extracting relationships from sentences
Sentiment analysis
Summary
11
Visual and Audio Analysis
Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
12
Visual and Audio Analysis
Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
13
Mathematical and Parallel Techniques for Data Analysis
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
14
Bringing It All Together
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
15
Module 2
Module 2
16
Data Science Using Java
Data Science Using Java
Data science
Data science process models
Data science in Java
Summary
17
Data Processing Toolbox
Data Processing Toolbox
Standard Java library
Extensions to the standard library
Accessing data
Search engine - preparing data
Summary
18
Exploratory Data Analysis
Exploratory Data Analysis
Exploratory data analysis in Java
Interactive Exploratory Data Analysis in Java
Summary
19
Supervised Learning - Classification and Regression
Supervised Learning - Classification and Regression
Classification
Case study - page prediction
Regression
Case study - hardware performance
Summary
20
Unsupervised Learning - Clustering and Dimensionality Reduction
Unsupervised Learning - Clustering and Dimensionality Reduction
Dimensionality reduction
Cluster analysis
Summary
21
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
22
Extreme Gradient Boosting
Extreme Gradient Boosting
Gradient Boosting Machines and XGBoost
XGBoost in practice
Summary
23
Deep Learning with DeepLearning4J
Deep Learning with DeepLearning4J
Neural Networks and DeepLearning4J
Deep learning for cats versus dogs
Summary
24
Scaling Data Science
Scaling Data Science
Apache Hadoop
Apache Spark
Link prediction
Summary
25
Deploying Data Science Models
Deploying Data Science Models
Microservices
Online evaluation
Summary
26
Bibliography
Bibliography

Data science process models

Applying data science is much more than just selecting a suitable machine learning algorithm and using it on the data. It is always good to keep in mind that machine learning is only a small part of the project; there are other parts such as understanding the problem, collecting the data, testing the solution and deploying to the production.

When working on any project, not just data science ones, it is beneficial to break it down into smaller manageable pieces and complete them one-by-one. For data science, there are best practices that describe how to do it the best way, and they are called process models. There are multiple models, including CRISP-DM and OSEMN.

In this chapter, CRISP-DM is explained as Obtain, Scrub, Explore, Model, and iNterpret (OSEMN), which is more suitable for data analysis tasks and addresses many important steps to a lesser extent.

CRISP-DM

Cross Industry Standard Process for Data Mining (CRISP-DM) is a process methodology for developing...

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