Data Science with Java.pdf

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Preface

Who Should Read This Book

Why I Wrote This Book

A Word on Data Science Today

Navigating This Book

Conventions Used in This Book

Using Code Examples

O’Reilly Safari

How to Contact Us

Acknowledgments

1. Data I/O

What Is Data, Anyway?

Data Models

Univariate Arrays

Multivariate Arrays

Data Objects

Matrices and Vectors

JSON

Dealing with Real Data

Nulls

Blank Spaces

Parse Errors

Outliers

Managing Data Files

Understanding File Contents First

Reading from a Text File

Parsing big strings

Parsing delimited strings

Parsing JSON strings

Reading from a JSON File

Reading from an Image File

Writing to a Text File

Mastering Database Operations

Command-Line Clients

Structured Query Language

Create

Select

Insert

Update

Delete

Drop

Java Database Connectivity

Connections

Statements

Prepared statements

Result sets

Visualizing Data with Plots

Creating Simple Plots

Scatter plots

Bar charts

Plotting multiple series

Basic formatting

Plotting Mixed Chart Types

Saving a Plot to a File

2. Linear Algebra

Building Vectors and Matrices

Array Storage

Block Storage

Map Storage

Accessing Elements

Working with Submatrices

Randomization

Operating on Vectors and Matrices

Scaling

Transposing

Addition and Subtraction

Length

Distances

Multiplication

Inner Product

Outer Product

Entrywise Product

Compound Operations

Affine Transformation

Mapping a Function

Decomposing Matrices

Cholesky Decomposition

LU Decomposition

QR Decomposition

Singular Value Decomposition

Eigen Decomposition

Determinant

Inverse

Solving Linear Systems

3. Statistics

The Probabilistic Origins of Data

Probability Density

Cumulative Probability

Statistical Moments

Entropy

Continuous Distributions

Uniform

Normal

Multivariate normal

Log normal

Empirical

Discrete Distributions

Bernoulli

Binomial

Poisson

Characterizing Datasets

Calculating Moments

Sample moments

Updating moments

Descriptive Statistics

Count

Sum

Min

Max

Mean

Median

Mode

Variance

Standard deviation

Error on the mean

Skewness

Kurtosis

Multivariate Statistics

Covariance and Correlation

Covariance

Pearson’s correlation

Regression

Simple regression

Multiple regression

Working with Large Datasets

Accumulating Statistics

Merging Statistics

Regression

Using Built-in Database Functions

4. Data Operations

Transforming Text Data

Extracting Tokens from a Document

Utilizing Dictionaries

Vectorizing a Document

Scaling and Regularizing Numeric Data

Scaling Columns

Min-max scaling

Centering the data

Unit normal scaling

Scaling Rows

L1 regularization

L2 regularization

Matrix Scaling Operator

Reducing Data to Principal Components

Covariance Method

SVD Method

Creating Training, Validation, and Test Sets

Index-Based Resampling

List-Based Resampling

Mini-Batches

Encoding Labels

A Generic Encoder

One-Hot Encoding

5. Learning and Prediction

Learning Algorithms

Iterative Learning Procedure

Gradient Descent Optimizer

Evaluating Learning Processes

Minimizing a Loss Function

Linear loss

Quadratic loss

Cross-entropy loss

Bernoulli

Multinomial

Two-Point

Minimizing the Sum of Variances

Silhouette Coefficient

Log-Likelihood

Classifier Accuracy

Unsupervised Learning

k-Means Clustering

DBSCAN

Dealing with outliers

Optimizing radius of capture and minPoints

Inference from DBSCAN

Gaussian Mixtures

Gaussian mixture model

Fitting with the EM algorithm

Optimizing the number of clusters

Supervised Learning

Naive Bayes

Gaussian

Multinomial

Bernoulli

Iris example

Linear Models

Linear

Logistic

Softmax

Tanh

Linear model estimator

Iris example

Deep Networks

A network layer

Feed forward

Back propagation

Deep network estimator

MNIST example

6. Hadoop MapReduce

Hadoop Distributed File System

MapReduce Architecture

Writing MapReduce Applications

Anatomy of a MapReduce Job

Hadoop Data Types

Writable and WritableComparable types

Custom Writable and WritableComparable types

Writable

WritableComparable

Mappers

Generic mappers

Customizing a mapper

Reducers

Generic reducers

Customizing a reducer

The Simplicity of a JSON String as Text

Deployment Wizardry

Running a standalone program

Deploying a JAR application

Including dependencies

Simplifying with a BASH script

MapReduce Examples

Word Count

Custom Word Count

Sparse Linear Algebra

A. Datasets

Anscombe’s Quartet

Sentiment

Gaussian Mixtures

Iris

MNIST

Index

Download from finelybook www.finelybook.com Data Science with Java Michael R. Brzustowicz, PhD 2

Download from finelybook www.finelybook.com Data Science with Java by Michael R. Brzustowicz, PhD Copyright © 2017 Michael Brzustowicz. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Editors: Nan Barber and Brian Foster Production Editor: Kristen Brown Copyeditor: Sharon Wilkey Proofreader: Jasmine Kwityn Indexer: Lucie Haskins Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Rebecca Demarest June 2017: First Edition 3

Download from finelybook www.finelybook.com Revision History for the First Edition 2017-05-30: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Data Science with Java, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-491-93411-1 [LSI] This book was downloaded from AvaxHome! Visit my blog for more new books: https://avxhm.se/blogs/AlenMiler 4

Download from finelybook www.finelybook.com Dedication This book is for my cofounder and our two startups. 5

Download from finelybook www.finelybook.com Preface Data science is a diverse and growing field encompassing many subfields of both mathematics and computer science. Statistics, linear algebra, databases, machine intelligence, and data visualization are just a few of the topics that merge together in the realm of a data scientist. Technology abounds and the tools to practice data science are evolving rapidly. This book focuses on core, fundamental principles backed by clear, object-oriented code in Java. And while this book will inspire you to get busy right away practicing the craft of data science, it is my hope that you will take the lead in building the next generation of data science technology. 6

Download from finelybook www.finelybook.com Who Should Read This Book This book is for scientists and engineers already familiar with the concepts of application development who want to jump headfirst into data science. The topics covered here will walk you through the data science pipeline, explaining mathematical theory and giving code examples along the way. This book is the perfect jumping-off point into much deeper waters. 7

Download from finelybook www.finelybook.com Why I Wrote This Book I wrote this book to start a movement. As data science skyrockets to stardom, fueled by R and Python, very few practitioners venture into the world of Java. Clearly, the tools for data exploration lend themselves to the interpretive languages. But there is another realm of the engineering–science hybrid where scale, robustness, and convenience must merge. Java is perhaps the one language that can do it all. If this book inspires you, I hope that you will contribute code to one of the many open source Java projects that support data science. 8

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