book

Data Science with Java

by Michael R. Brzustowicz

June 2017

Beginner to intermediate

233 pages

5h 57m

English

O'Reilly Media, Inc.

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Who Should Read This BookWhy I Wrote This BookA Word on Data Science TodayNavigating This BookConventions Used in This BookUsing Code ExamplesO’Reilly SafariHow to Contact UsAcknowledgments
What Is Data, Anyway?Data ModelsUnivariate ArraysMultivariate ArraysData ObjectsMatrices and VectorsJSONDealing with Real DataNullsBlank SpacesParse ErrorsOutliersManaging Data FilesUnderstanding File Contents FirstReading from a Text FileReading from a JSON FileReading from an Image FileWriting to a Text FileMastering Database OperationsCommand-Line ClientsStructured Query LanguageJava Database ConnectivityVisualizing Data with PlotsCreating Simple PlotsPlotting Mixed Chart TypesSaving a Plot to a File
Building Vectors and MatricesArray StorageBlock StorageMap StorageAccessing ElementsWorking with SubmatricesRandomizationOperating on Vectors and MatricesScalingTransposingAddition and SubtractionLengthDistancesMultiplicationInner ProductOuter ProductEntrywise ProductCompound OperationsAffine TransformationMapping a FunctionDecomposing MatricesCholesky DecompositionLU DecompositionQR DecompositionSingular Value DecompositionEigen DecompositionDeterminantInverseSolving Linear Systems
The Probabilistic Origins of DataProbability DensityCumulative ProbabilityStatistical MomentsEntropyContinuous DistributionsDiscrete DistributionsCharacterizing DatasetsCalculating MomentsDescriptive StatisticsMultivariate StatisticsCovariance and CorrelationRegressionWorking with Large DatasetsAccumulating StatisticsMerging StatisticsRegressionUsing Built-in Database Functions
Transforming Text DataExtracting Tokens from a DocumentUtilizing DictionariesVectorizing a DocumentScaling and Regularizing Numeric DataScaling ColumnsScaling RowsMatrix Scaling OperatorReducing Data to Principal ComponentsCovariance MethodSVD MethodCreating Training, Validation, and Test SetsIndex-Based ResamplingList-Based ResamplingMini-BatchesEncoding LabelsA Generic EncoderOne-Hot Encoding
Learning AlgorithmsIterative Learning ProcedureGradient Descent OptimizerEvaluating Learning ProcessesMinimizing a Loss FunctionMinimizing the Sum of VariancesSilhouette CoefficientLog-LikelihoodClassifier AccuracyUnsupervised Learningk-Means ClusteringDBSCANGaussian MixturesSupervised LearningNaive BayesLinear ModelsDeep Networks
Hadoop Distributed File SystemMapReduce ArchitectureWriting MapReduce ApplicationsAnatomy of a MapReduce JobHadoop Data TypesMappersReducersThe Simplicity of a JSON String as TextDeployment WizardryMapReduce ExamplesWord CountCustom Word CountSparse Linear Algebra
Anscombe’s QuartetSentimentGaussian MixturesIrisMNIST

Content preview from Data Science with Java

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.

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.

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 ...