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Dedication
by Michael R. Brzustowicz
Data Science with Java
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
Reading from a JSON File
Reading from an Image File
Writing to a Text File
Mastering Database Operations
Command-Line Clients
Structured Query Language
Java Database Connectivity
Visualizing Data with Plots
Creating Simple Plots
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
Discrete Distributions
Characterizing Datasets
Calculating Moments
Descriptive Statistics
Multivariate Statistics
Covariance and Correlation
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
Scaling Rows
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
Minimizing the Sum of Variances
Silhouette Coefficient
Log-Likelihood
Classifier Accuracy
Unsupervised Learning
k-Means Clustering
DBSCAN
Gaussian Mixtures
Supervised Learning
Naive Bayes
Linear Models
Deep Networks
6. Hadoop MapReduce
Hadoop Distributed File System
MapReduce Architecture
Writing MapReduce Applications
Anatomy of a MapReduce Job
Hadoop Data Types
Mappers
Reducers
The Simplicity of a JSON String as Text
Deployment Wizardry
MapReduce Examples
Word Count
Custom Word Count
Sparse Linear Algebra
A. Datasets
Anscombe’s Quartet
Sentiment
Gaussian Mixtures
Iris
MNIST
Index
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Data Science with Java
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Preface
Dedication
This book is for my cofounder and our two startups.
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